* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/rBiasCorrection.Rcheck’ * using R Under development (unstable) (2026-10-30 r90795) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2) GNU Fortran (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2) * running under: Fedora Linux 44 (Server Edition) * using session charset: UTF-8 * current time: 2026-10-30 07:16:22 UTC * using option ‘--no-stop-on-test-error’ * checking for file ‘rBiasCorrection/DESCRIPTION’ ... OK * this is package ‘rBiasCorrection’ version ‘0.3.6’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘rBiasCorrection’ can be installed ... [5s/10s] OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking whether startup messages can be suppressed ... OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [9s/16s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... [11s/20s] OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘testthat.R’ [215s/304s] [215s/305s] ERROR Running the tests in ‘tests/testthat.R’ failed. Complete output: > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(rBiasCorrection) > > local_edition(3) > > test_check("rBiasCorrection") Attaching package: 'data.table' The following object is masked from 'package:base': %notin% [20261030_071808.]: Entered 'clean_dt'-Function [20261030_071809.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071809.]: got experimental data [20261030_071809.]: Entered 'clean_dt'-Function [20261030_071809.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071809.]: got calibration data [20261030_071809.]: ### Starting with regression calculations ### [20261030_071809.]: Entered 'regression_type1'-Function [20261030_071811.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071811.]: Logging df_agg: CpG#1 [20261030_071811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071811.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071811.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071811.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071811.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071811.]: Entered 'hyperbolic_regression'-Function [20261030_071811.]: 'hyperbolic_regression': minmax = FALSE [20261030_071812.]: Entered 'cubic_regression'-Function [20261030_071812.]: 'cubic_regression': minmax = FALSE [20261030_071812.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071812.]: Logging df_agg: CpG#2 [20261030_071812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071812.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071812.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071812.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071812.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071812.]: Entered 'hyperbolic_regression'-Function [20261030_071812.]: 'hyperbolic_regression': minmax = FALSE [20261030_071812.]: Entered 'cubic_regression'-Function [20261030_071812.]: 'cubic_regression': minmax = FALSE [20261030_071812.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071812.]: Logging df_agg: CpG#3 [20261030_071812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071812.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071812.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071812.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071812.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071812.]: Entered 'hyperbolic_regression'-Function [20261030_071812.]: 'hyperbolic_regression': minmax = FALSE [20261030_071813.]: Entered 'cubic_regression'-Function [20261030_071813.]: 'cubic_regression': minmax = FALSE [20261030_071813.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071813.]: Logging df_agg: CpG#4 [20261030_071813.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071813.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071813.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071813.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071813.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071813.]: Entered 'hyperbolic_regression'-Function [20261030_071813.]: 'hyperbolic_regression': minmax = FALSE [20261030_071813.]: Entered 'cubic_regression'-Function [20261030_071813.]: 'cubic_regression': minmax = FALSE [20261030_071813.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071813.]: Logging df_agg: CpG#5 [20261030_071813.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071813.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071813.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071813.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071813.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071813.]: Entered 'hyperbolic_regression'-Function [20261030_071813.]: 'hyperbolic_regression': minmax = FALSE [20261030_071814.]: Entered 'cubic_regression'-Function [20261030_071814.]: 'cubic_regression': minmax = FALSE [20261030_071811.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071811.]: Logging df_agg: CpG#6 [20261030_071811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071811.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071811.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071811.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071811.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071811.]: Entered 'hyperbolic_regression'-Function [20261030_071811.]: 'hyperbolic_regression': minmax = FALSE [20261030_071811.]: Entered 'cubic_regression'-Function [20261030_071811.]: 'cubic_regression': minmax = FALSE [20261030_071811.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071811.]: Logging df_agg: CpG#7 [20261030_071811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071811.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071811.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071811.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071811.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071811.]: Entered 'hyperbolic_regression'-Function [20261030_071811.]: 'hyperbolic_regression': minmax = FALSE [20261030_071812.]: Entered 'cubic_regression'-Function [20261030_071812.]: 'cubic_regression': minmax = FALSE [20261030_071812.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071812.]: Logging df_agg: CpG#8 [20261030_071812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071812.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071812.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071812.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071812.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071812.]: Entered 'hyperbolic_regression'-Function [20261030_071812.]: 'hyperbolic_regression': minmax = FALSE [20261030_071812.]: Entered 'cubic_regression'-Function [20261030_071812.]: 'cubic_regression': minmax = FALSE [20261030_071812.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071812.]: Logging df_agg: CpG#9 [20261030_071812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071812.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071812.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071812.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071812.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071812.]: Entered 'hyperbolic_regression'-Function [20261030_071812.]: 'hyperbolic_regression': minmax = FALSE [20261030_071813.]: Entered 'cubic_regression'-Function [20261030_071813.]: 'cubic_regression': minmax = FALSE [20261030_071813.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071813.]: Logging df_agg: row_means [20261030_071813.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071813.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071813.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071813.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071813.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071813.]: Entered 'hyperbolic_regression'-Function [20261030_071813.]: 'hyperbolic_regression': minmax = FALSE [20261030_071813.]: Entered 'cubic_regression'-Function [20261030_071813.]: 'cubic_regression': minmax = FALSE [20261030_071815.]: ### Starting with regression calculations ### [20261030_071815.]: Entered 'regression_type1'-Function [20261030_071816.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071816.]: Logging df_agg: CpG#1 [20261030_071816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071816.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071816.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071816.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071816.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071816.]: Entered 'hyperbolic_regression'-Function [20261030_071816.]: 'hyperbolic_regression': minmax = FALSE [20261030_071817.]: Entered 'cubic_regression'-Function [20261030_071817.]: 'cubic_regression': minmax = FALSE [20261030_071817.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071817.]: Logging df_agg: CpG#2 [20261030_071817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071817.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071817.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071817.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071817.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071817.]: Entered 'hyperbolic_regression'-Function [20261030_071817.]: 'hyperbolic_regression': minmax = FALSE [20261030_071817.]: Entered 'cubic_regression'-Function [20261030_071817.]: 'cubic_regression': minmax = FALSE [20261030_071817.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071818.]: Logging df_agg: CpG#3 [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071818.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071818.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071818.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071818.]: Entered 'cubic_regression'-Function [20261030_071818.]: 'cubic_regression': minmax = FALSE [20261030_071818.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071818.]: Logging df_agg: CpG#4 [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071818.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071818.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071818.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071818.]: Entered 'cubic_regression'-Function [20261030_071818.]: 'cubic_regression': minmax = FALSE [20261030_071818.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071818.]: Logging df_agg: CpG#5 [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071818.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071818.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071818.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071819.]: Entered 'cubic_regression'-Function [20261030_071819.]: 'cubic_regression': minmax = FALSE [20261030_071817.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071817.]: Logging df_agg: CpG#6 [20261030_071817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071817.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071817.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071817.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071817.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071817.]: Entered 'hyperbolic_regression'-Function [20261030_071817.]: 'hyperbolic_regression': minmax = FALSE [20261030_071817.]: Entered 'cubic_regression'-Function [20261030_071817.]: 'cubic_regression': minmax = FALSE [20261030_071817.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071817.]: Logging df_agg: CpG#7 [20261030_071817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071817.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071817.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071817.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071817.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071817.]: Entered 'hyperbolic_regression'-Function [20261030_071817.]: 'hyperbolic_regression': minmax = FALSE [20261030_071818.]: Entered 'cubic_regression'-Function [20261030_071818.]: 'cubic_regression': minmax = FALSE [20261030_071818.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071818.]: Logging df_agg: CpG#8 [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071818.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071818.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071818.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071818.]: Entered 'cubic_regression'-Function [20261030_071818.]: 'cubic_regression': minmax = FALSE [20261030_071818.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071818.]: Logging df_agg: CpG#9 [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071818.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071818.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071818.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071818.]: Entered 'cubic_regression'-Function [20261030_071818.]: 'cubic_regression': minmax = FALSE [20261030_071818.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071818.]: Logging df_agg: row_means [20261030_071818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071818.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071818.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071818.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071818.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071818.]: Entered 'hyperbolic_regression'-Function [20261030_071818.]: 'hyperbolic_regression': minmax = FALSE [20261030_071819.]: Entered 'cubic_regression'-Function [20261030_071819.]: 'cubic_regression': minmax = FALSE Saving _problems/test-algorithm_minmax_FALSE-74.R [20261030_071821.]: ### Starting with regression calculations ### [20261030_071821.]: Entered 'regression_type1'-Function [20261030_071822.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071822.]: Logging df_agg: CpG#1 [20261030_071822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071822.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071822.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071822.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071822.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071822.]: Entered 'hyperbolic_regression'-Function [20261030_071822.]: 'hyperbolic_regression': minmax = FALSE [20261030_071823.]: Entered 'cubic_regression'-Function [20261030_071823.]: 'cubic_regression': minmax = FALSE [20261030_071823.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071823.]: Logging df_agg: CpG#2 [20261030_071823.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071823.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071823.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071823.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071823.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071823.]: Entered 'hyperbolic_regression'-Function [20261030_071823.]: 'hyperbolic_regression': minmax = FALSE [20261030_071823.]: Entered 'cubic_regression'-Function [20261030_071823.]: 'cubic_regression': minmax = FALSE [20261030_071823.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071823.]: Logging df_agg: CpG#3 [20261030_071823.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071823.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071823.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071823.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071823.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071823.]: Entered 'hyperbolic_regression'-Function [20261030_071823.]: 'hyperbolic_regression': minmax = FALSE [20261030_071824.]: Entered 'cubic_regression'-Function [20261030_071824.]: 'cubic_regression': minmax = FALSE [20261030_071824.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071824.]: Logging df_agg: CpG#4 [20261030_071824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071824.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071824.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071824.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071824.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071824.]: Entered 'hyperbolic_regression'-Function [20261030_071824.]: 'hyperbolic_regression': minmax = FALSE [20261030_071824.]: Entered 'cubic_regression'-Function [20261030_071824.]: 'cubic_regression': minmax = FALSE [20261030_071824.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071824.]: Logging df_agg: CpG#5 [20261030_071824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071824.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071824.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071824.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071824.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071824.]: Entered 'hyperbolic_regression'-Function [20261030_071824.]: 'hyperbolic_regression': minmax = FALSE [20261030_071825.]: Entered 'cubic_regression'-Function [20261030_071825.]: 'cubic_regression': minmax = FALSE [20261030_071823.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071823.]: Logging df_agg: CpG#6 [20261030_071823.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071823.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071823.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071823.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071823.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071823.]: Entered 'hyperbolic_regression'-Function [20261030_071823.]: 'hyperbolic_regression': minmax = FALSE [20261030_071824.]: Entered 'cubic_regression'-Function [20261030_071824.]: 'cubic_regression': minmax = FALSE [20261030_071824.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071824.]: Logging df_agg: CpG#7 [20261030_071824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071824.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071824.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071824.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071824.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071824.]: Entered 'hyperbolic_regression'-Function [20261030_071824.]: 'hyperbolic_regression': minmax = FALSE [20261030_071824.]: Entered 'cubic_regression'-Function [20261030_071824.]: 'cubic_regression': minmax = FALSE [20261030_071824.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071824.]: Logging df_agg: CpG#8 [20261030_071824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071824.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071824.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071824.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071824.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071824.]: Entered 'hyperbolic_regression'-Function [20261030_071824.]: 'hyperbolic_regression': minmax = FALSE [20261030_071825.]: Entered 'cubic_regression'-Function [20261030_071825.]: 'cubic_regression': minmax = FALSE [20261030_071825.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071825.]: Logging df_agg: CpG#9 [20261030_071825.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071825.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071825.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071825.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071825.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071825.]: Entered 'hyperbolic_regression'-Function [20261030_071825.]: 'hyperbolic_regression': minmax = FALSE [20261030_071825.]: Entered 'cubic_regression'-Function [20261030_071825.]: 'cubic_regression': minmax = FALSE [20261030_071825.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071825.]: Logging df_agg: row_means [20261030_071825.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071825.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071825.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071825.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071825.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071825.]: Entered 'hyperbolic_regression'-Function [20261030_071825.]: 'hyperbolic_regression': minmax = FALSE [20261030_071825.]: Entered 'cubic_regression'-Function [20261030_071825.]: 'cubic_regression': minmax = FALSE Saving _problems/test-algorithm_minmax_FALSE-91.R [20261030_071827.]: Entered 'regression_type1'-Function [20261030_071829.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071830.]: Logging df_agg: CpG#1 [20261030_071830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071830.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071830.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071830.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071830.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071830.]: Entered 'hyperbolic_regression'-Function [20261030_071830.]: 'hyperbolic_regression': minmax = FALSE [20261030_071830.]: Entered 'cubic_regression'-Function [20261030_071830.]: 'cubic_regression': minmax = FALSE [20261030_071830.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071830.]: Logging df_agg: CpG#2 [20261030_071830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071830.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071830.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071830.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071830.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071830.]: Entered 'hyperbolic_regression'-Function [20261030_071830.]: 'hyperbolic_regression': minmax = FALSE [20261030_071831.]: Entered 'cubic_regression'-Function [20261030_071831.]: 'cubic_regression': minmax = FALSE [20261030_071831.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071831.]: Logging df_agg: CpG#3 [20261030_071831.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071831.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071831.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071831.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071831.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071831.]: Entered 'hyperbolic_regression'-Function [20261030_071831.]: 'hyperbolic_regression': minmax = FALSE [20261030_071832.]: Entered 'cubic_regression'-Function [20261030_071832.]: 'cubic_regression': minmax = FALSE [20261030_071832.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071832.]: Logging df_agg: CpG#4 [20261030_071832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071832.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071832.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071832.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071832.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071832.]: Entered 'hyperbolic_regression'-Function [20261030_071832.]: 'hyperbolic_regression': minmax = FALSE [20261030_071832.]: Entered 'cubic_regression'-Function [20261030_071832.]: 'cubic_regression': minmax = FALSE [20261030_071832.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071832.]: Logging df_agg: CpG#5 [20261030_071832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071832.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071832.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071832.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071832.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071832.]: Entered 'hyperbolic_regression'-Function [20261030_071832.]: 'hyperbolic_regression': minmax = FALSE [20261030_071832.]: Entered 'cubic_regression'-Function [20261030_071832.]: 'cubic_regression': minmax = FALSE [20261030_071831.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071831.]: Logging df_agg: CpG#6 [20261030_071831.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071831.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071831.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071831.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071831.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071831.]: Entered 'hyperbolic_regression'-Function [20261030_071831.]: 'hyperbolic_regression': minmax = FALSE [20261030_071831.]: Entered 'cubic_regression'-Function [20261030_071832.]: 'cubic_regression': minmax = FALSE [20261030_071832.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071832.]: Logging df_agg: CpG#7 [20261030_071832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071832.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071832.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071832.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071832.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071832.]: Entered 'hyperbolic_regression'-Function [20261030_071832.]: 'hyperbolic_regression': minmax = FALSE [20261030_071832.]: Entered 'cubic_regression'-Function [20261030_071832.]: 'cubic_regression': minmax = FALSE [20261030_071832.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071832.]: Logging df_agg: CpG#8 [20261030_071832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071832.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071832.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071832.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071832.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071832.]: Entered 'hyperbolic_regression'-Function [20261030_071832.]: 'hyperbolic_regression': minmax = FALSE [20261030_071833.]: Entered 'cubic_regression'-Function [20261030_071833.]: 'cubic_regression': minmax = FALSE [20261030_071833.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071833.]: Logging df_agg: CpG#9 [20261030_071833.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071833.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071833.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071833.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071833.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071833.]: Entered 'hyperbolic_regression'-Function [20261030_071833.]: 'hyperbolic_regression': minmax = FALSE [20261030_071833.]: Entered 'cubic_regression'-Function [20261030_071833.]: 'cubic_regression': minmax = FALSE [20261030_071833.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071833.]: Logging df_agg: row_means [20261030_071833.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071833.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071833.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071833.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071833.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071833.]: Entered 'hyperbolic_regression'-Function [20261030_071833.]: 'hyperbolic_regression': minmax = FALSE [20261030_071833.]: Entered 'cubic_regression'-Function [20261030_071833.]: 'cubic_regression': minmax = FALSE [20261030_071834.]: Entered 'solving_equations'-Function [20261030_071834.]: Solving cubic regression for CpG#1 Coefficients: -61.2046666666667Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -18.694Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 31.381 --> Root in between the borders! Added to results. Coefficients: -26.743Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -39.173Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -6.758Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 9.279 --> Root in between the borders! Added to results. Coefficients: -13.788Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 20.944 --> Root in between the borders! Added to results. Coefficients: -14.548Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 22.394 --> Root in between the borders! Added to results. Coefficients: -29.576Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -68.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -0.358Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 Root: 0.458 --> Root in between the borders! Added to results. [20261030_071834.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20261030_071834.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20261030_071834.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20261030_071834.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20261030_071834.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20261030_071834.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20261030_071834.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20261030_071834.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20261030_071834.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20261030_071834.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20261030_071834.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20261030_071834.]: Solving cubic regression for CpG#3 Coefficients: -52.5963333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 Root: 99.525 --> Root in between the borders! Added to results. Coefficients: -16.799Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 27.998 --> Root in between the borders! Added to results. Coefficients: -26.288Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 Root: 45.099 --> Root in between the borders! Added to results. Coefficients: -36.623Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 Root: 65.056 --> Root in between the borders! Added to results. Coefficients: -5.033Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 8.119 --> Root in between the borders! Added to results. Coefficients: -14.543Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 24.081 --> Root in between the borders! Added to results. Coefficients: -15.883Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 26.401 --> Root in between the borders! Added to results. Coefficients: -27.877Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -62.837Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -0.273Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 Root: 0.435 --> Root in between the borders! Added to results. [20261030_071834.]: Solving cubic regression for CpG#4 Coefficients: -58.749Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -18.509Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 28.962 --> Root in between the borders! Added to results. Coefficients: -29.5573333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 Root: 49.413 --> Root in between the borders! Added to results. Coefficients: -41.979Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 Root: 77.433 --> Root in between the borders! Added to results. Coefficients: -6.919Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 10.227 --> Root in between the borders! Added to results. Coefficients: -14.419Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 22.087 --> Root in between the borders! Added to results. Coefficients: -17.739Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 27.643 --> Root in between the borders! Added to results. Coefficients: -28.347Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 Root: 47.012 --> Root in between the borders! Added to results. Coefficients: -68.661Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -2.269Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 Root: 3.286 --> Root in between the borders! Added to results. [20261030_071834.]: Solving cubic regression for CpG#5 Coefficients: -48.4613333333333Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 Root: 69.307 --> Root in between the borders! Added to results. Coefficients: -14.212Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 26.846 --> Root in between the borders! Added to results. Coefficients: -25.9451428571429Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 Root: 43.525 --> Root in between the borders! Added to results. Coefficients: -32.688Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 Root: 51.914 --> Root in between the borders! Added to results. Coefficients: -4.698Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 10.155 --> Root in between the borders! Added to results. Coefficients: -12.058Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 23.388 --> Root in between the borders! Added to results. Coefficients: -13.918Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 26.383 --> Root in between the borders! Added to results. Coefficients: -24.912Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 Root: 42.174 --> Root in between the borders! Added to results. Coefficients: -63.758Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 Root: 84.079 --> Root in between the borders! Added to results. Coefficients: -0.588Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 Root: 1.375 --> Root in between the borders! Added to results. [20261030_071834.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20261030_071834.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20261030_071834.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20261030_071834.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20261030_071834.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20261030_071834.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20261030_071834.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20261030_071834.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20261030_071834.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20261030_071834.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20261030_071834.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20261030_071834.]: Solving cubic regression for CpG#7 Coefficients: -43.1756666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -11.657Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 24.106 --> Root in between the borders! Added to results. Coefficients: -22.4761428571429Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -28.9415Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -5.979Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 11.169 --> Root in between the borders! Added to results. Coefficients: -11.609Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 23.982 --> Root in between the borders! Added to results. Coefficients: -13.419Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 28.894 --> Root in between the borders! Added to results. Coefficients: -20.949Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 Root: 68.141 --> Root in between the borders! Added to results. Coefficients: -49.757Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -0.269Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 Root: 0.465 --> Root in between the borders! Added to results. [20261030_071834.]: Solving cubic regression for CpG#8 Coefficients: -56.4533333333333Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#1 Root: 69.454 --> Root in between the borders! Added to results. Coefficients: -18.67Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#10 Root: 28.727 --> Root in between the borders! Added to results. Coefficients: -24.0385714285714Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#2 Root: 35.506 --> Root in between the borders! Added to results. Coefficients: -43.945Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#3 Root: 57.457 --> Root in between the borders! Added to results. Coefficients: -5.7Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#4 Root: 9.919 --> Root in between the borders! Added to results. Coefficients: -12.485Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#5 Root: 20.266 --> Root in between the borders! Added to results. Coefficients: -26.88Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#6 Root: 38.919 --> Root in between the borders! Added to results. Coefficients: -31.842Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#7 Root: 44.627 --> Root in between the borders! Added to results. Coefficients: -68.008Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#8 Root: 79.666 --> Root in between the borders! Added to results. Coefficients: 2.08Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071834.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.008 --> '-10 < root < 0' --> substitute 0 [20261030_071834.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20261030_071834.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20261030_071834.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20261030_071834.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20261030_071834.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20261030_071834.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20261030_071834.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20261030_071834.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20261030_071834.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20261030_071834.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20261030_071834.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20261030_071834.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20261030_071834.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20261030_071834.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20261030_071834.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20261030_071834.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20261030_071834.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20261030_071834.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20261030_071834.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20261030_071834.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20261030_071834.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20261030_071834.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20261030_071834.]: Entered 'solving_equations'-Function [20261030_071834.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071834.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071835.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071835.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071835.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071835.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20261030_071835.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20261030_071835.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20261030_071835.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20261030_071835.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20261030_071835.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20261030_071835.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20261030_071835.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20261030_071835.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20261030_071835.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071835.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071835.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20261030_071835.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20261030_071835.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20261030_071835.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20261030_071835.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20261030_071835.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20261030_071835.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20261030_071835.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20261030_071835.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20261030_071835.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_071835.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071835.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071835.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20261030_071835.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20261030_071835.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20261030_071835.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20261030_071835.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20261030_071835.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20261030_071835.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20261030_071835.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20261030_071835.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20261030_071835.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_071835.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20261030_071835.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20261030_071835.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20261030_071835.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20261030_071835.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20261030_071835.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20261030_071835.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20261030_071835.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20261030_071835.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_071835.]: Entered 'solving_equations'-Function [20261030_071835.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20261030_071835.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20261030_071835.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20261030_071835.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20261030_071835.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20261030_071835.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20261030_071835.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20261030_071835.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20261030_071835.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20261030_071835.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20261030_071835.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20261030_071835.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20261030_071835.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20261030_071835.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20261030_071835.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20261030_071835.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20261030_071835.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20261030_071836.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20261030_071836.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512351181983175 [20261030_071836.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7524070675129 [20261030_071836.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5219407610414 [20261030_071836.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5271170603888 [20261030_071836.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7910190910562 [20261030_071836.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8687462878094 [20261030_071836.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.552548340177 [20261030_071836.]: Samplename: 75 Root: 77.553 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4375942302221 [20261030_071836.]: Samplename: 87.5 Root: 80.438 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704175608685 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20261030_071836.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20261030_071836.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20261030_071836.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20261030_071836.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20261030_071836.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20261030_071836.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20261030_071836.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20261030_071836.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20261030_071836.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20261030_071836.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20261030_071836.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20261030_071836.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20261030_071836.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20261030_071836.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20261030_071836.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20261030_071836.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20261030_071836.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20261030_071836.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20261030_071836.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20261030_071836.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20261030_071836.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20261030_071836.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20261030_071836.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20261030_071836.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20261030_071836.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20261030_071836.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20261030_071836.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20261030_071836.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20261030_071836.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20261030_071836.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20261030_071836.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20261030_071836.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20261030_071836.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20261030_071836.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20261030_071836.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20261030_071836.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20261030_071836.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20261030_071836.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20261030_071836.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20261030_071836.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20261030_071836.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20261030_071836.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20261030_071836.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20261030_071836.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20261030_071836.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20261030_071836.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20261030_071836.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20261030_071836.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20261030_071836.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20261030_071836.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20261030_071836.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20261030_071836.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20261030_071836.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20261030_071836.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20261030_071836.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20261030_071836.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20261030_071836.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_071836.]: ### Starting with regression calculations ### [20261030_071836.]: Entered 'regression_type1'-Function [20261030_071838.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20261030_071838.]: Logging df_agg: CpG#1 [20261030_071838.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071838.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20261030_071838.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20261030_071838.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20261030_071838.]: Entered 'hyperbolic_regression'-Function [20261030_071838.]: 'hyperbolic_regression': minmax = FALSE [20261030_071838.]: Entered 'cubic_regression'-Function [20261030_071838.]: 'cubic_regression': minmax = FALSE [20261030_071838.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20261030_071838.]: Logging df_agg: CpG#2 [20261030_071838.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071838.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20261030_071838.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20261030_071838.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20261030_071838.]: Entered 'hyperbolic_regression'-Function [20261030_071838.]: 'hyperbolic_regression': minmax = FALSE [20261030_071839.]: Entered 'cubic_regression'-Function [20261030_071839.]: 'cubic_regression': minmax = FALSE [20261030_071839.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512351181983175, 10.7524070675129, 25.5219407610414, 36.5271170603888, 50.7910190910562, 64.8687462878094, 77.552548340177, 80.4375942302221, 100)c(0.512351181983175, 1.7475929324871, 0.5219407610414, 0.972882939611203, 0.791019091056199, 2.3687462878094, 2.552548340177, 7.0624057697779, 0)c(NA, 13.9807434598968, 2.0877630441656, 2.59435450562988, 1.5820381821124, 3.78999406049504, 3.40339778690266, 8.07132087974617, 0) [20261030_071839.]: Logging df_agg: CpG#3 [20261030_071839.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071839.]: c(0.512351181983175, 10.7524070675129, 25.5219407610414, 36.5271170603888, 50.7910190910562, 64.8687462878094, 77.552548340177, 80.4375942302221, 100)[20261030_071839.]: c(0.512351181983175, 1.7475929324871, 0.5219407610414, 0.972882939611203, 0.791019091056199, 2.3687462878094, 2.552548340177, 7.0624057697779, 0)[20261030_071839.]: c(NA, 13.9807434598968, 2.0877630441656, 2.59435450562988, 1.5820381821124, 3.78999406049504, 3.40339778690266, 8.07132087974617, 0) [20261030_071839.]: Entered 'hyperbolic_regression'-Function [20261030_071839.]: 'hyperbolic_regression': minmax = FALSE [20261030_071839.]: Entered 'cubic_regression'-Function [20261030_071839.]: 'cubic_regression': minmax = FALSE [20261030_071839.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20261030_071839.]: Logging df_agg: CpG#4 [20261030_071839.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071839.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20261030_071839.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20261030_071839.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20261030_071839.]: Entered 'hyperbolic_regression'-Function [20261030_071839.]: 'hyperbolic_regression': minmax = FALSE [20261030_071840.]: Entered 'cubic_regression'-Function [20261030_071840.]: 'cubic_regression': minmax = FALSE [20261030_071840.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20261030_071840.]: Logging df_agg: CpG#5 [20261030_071840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071840.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20261030_071840.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20261030_071840.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20261030_071840.]: Entered 'hyperbolic_regression'-Function [20261030_071840.]: 'hyperbolic_regression': minmax = FALSE [20261030_071840.]: Entered 'cubic_regression'-Function [20261030_071840.]: 'cubic_regression': minmax = FALSE [20261030_071839.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20261030_071839.]: Logging df_agg: CpG#6 [20261030_071839.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071839.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20261030_071839.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20261030_071839.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20261030_071839.]: Entered 'hyperbolic_regression'-Function [20261030_071839.]: 'hyperbolic_regression': minmax = FALSE [20261030_071840.]: Entered 'cubic_regression'-Function [20261030_071840.]: 'cubic_regression': minmax = FALSE [20261030_071840.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20261030_071840.]: Logging df_agg: CpG#7 [20261030_071840.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071840.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20261030_071840.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20261030_071840.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20261030_071840.]: Entered 'hyperbolic_regression'-Function [20261030_071840.]: 'hyperbolic_regression': minmax = FALSE [20261030_071841.]: Entered 'cubic_regression'-Function [20261030_071841.]: 'cubic_regression': minmax = FALSE [20261030_071841.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20261030_071841.]: Logging df_agg: CpG#8 [20261030_071841.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071841.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20261030_071841.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20261030_071841.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20261030_071841.]: Entered 'hyperbolic_regression'-Function [20261030_071841.]: 'hyperbolic_regression': minmax = FALSE [20261030_071841.]: Entered 'cubic_regression'-Function [20261030_071841.]: 'cubic_regression': minmax = FALSE [20261030_071841.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20261030_071841.]: Logging df_agg: CpG#9 [20261030_071841.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071841.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20261030_071841.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20261030_071841.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20261030_071841.]: Entered 'hyperbolic_regression'-Function [20261030_071841.]: 'hyperbolic_regression': minmax = FALSE [20261030_071841.]: Entered 'cubic_regression'-Function [20261030_071842.]: 'cubic_regression': minmax = FALSE [20261030_071842.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20261030_071842.]: Logging df_agg: row_means [20261030_071842.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071842.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20261030_071842.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20261030_071842.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20261030_071842.]: Entered 'hyperbolic_regression'-Function [20261030_071842.]: 'hyperbolic_regression': minmax = FALSE [20261030_071842.]: Entered 'cubic_regression'-Function [20261030_071842.]: 'cubic_regression': minmax = FALSE [20261030_071843.]: Entered 'solving_equations'-Function [20261030_071843.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071843.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20261030_071843.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071843.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071843.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20261030_071843.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071843.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071843.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071843.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071843.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071844.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071844.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071844.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071844.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071844.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20261030_071844.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071844.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071844.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071844.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071844.]: ### Starting with regression calculations ### [20261030_071844.]: Entered 'regression_type1'-Function [20261030_071847.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20261030_071847.]: Logging df_agg: CpG#1 [20261030_071847.]: c(0, 12.5, 25, 37.5)[20261030_071847.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20261030_071847.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20261030_071847.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20261030_071847.]: Entered 'hyperbolic_regression'-Function [20261030_071847.]: 'hyperbolic_regression': minmax = FALSE [20261030_071847.]: Entered 'cubic_regression'-Function [20261030_071847.]: 'cubic_regression': minmax = FALSE [20261030_071847.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20261030_071847.]: Logging df_agg: CpG#2 [20261030_071847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071847.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20261030_071847.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20261030_071847.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20261030_071847.]: Entered 'hyperbolic_regression'-Function [20261030_071847.]: 'hyperbolic_regression': minmax = FALSE [20261030_071847.]: Entered 'cubic_regression'-Function [20261030_071847.]: 'cubic_regression': minmax = FALSE [20261030_071847.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20261030_071847.]: Logging df_agg: CpG#3 [20261030_071847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20261030_071847.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20261030_071847.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20261030_071847.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20261030_071847.]: Entered 'hyperbolic_regression'-Function [20261030_071847.]: 'hyperbolic_regression': minmax = FALSE [20261030_071847.]: Entered 'cubic_regression'-Function [20261030_071847.]: 'cubic_regression': minmax = FALSE [20261030_071847.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20261030_071847.]: Logging df_agg: CpG#4 [20261030_071847.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20261030_071847.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20261030_071847.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20261030_071847.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20261030_071847.]: Entered 'hyperbolic_regression'-Function [20261030_071847.]: 'hyperbolic_regression': minmax = FALSE [20261030_071847.]: Entered 'cubic_regression'-Function [20261030_071847.]: 'cubic_regression': minmax = FALSE [20261030_071847.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782903, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.43714152170971, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894627, 6.80110981502537, 5.101557746111) [20261030_071847.]: Logging df_agg: CpG#5 [20261030_071847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071847.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782903, 81.5490289118528, 94.898442253889)[20261030_071847.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.43714152170971, 5.9509710881472, 5.101557746111)[20261030_071847.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894627, 6.80110981502537, 5.101557746111) [20261030_071847.]: Entered 'hyperbolic_regression'-Function [20261030_071847.]: 'hyperbolic_regression': minmax = FALSE [20261030_071848.]: Entered 'cubic_regression'-Function [20261030_071848.]: 'cubic_regression': minmax = FALSE [20261030_071848.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20261030_071848.]: Logging df_agg: CpG#6 [20261030_071848.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_071848.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20261030_071848.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20261030_071848.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20261030_071848.]: Entered 'hyperbolic_regression'-Function [20261030_071848.]: 'hyperbolic_regression': minmax = FALSE [20261030_071848.]: Entered 'cubic_regression'-Function [20261030_071848.]: 'cubic_regression': minmax = FALSE [20261030_071848.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20261030_071848.]: Logging df_agg: CpG#7 [20261030_071848.]: c(0, 12.5, 25, 37.5)[20261030_071848.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20261030_071848.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20261030_071848.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20261030_071848.]: Entered 'hyperbolic_regression'-Function [20261030_071848.]: 'hyperbolic_regression': minmax = FALSE [20261030_071848.]: Entered 'cubic_regression'-Function [20261030_071848.]: 'cubic_regression': minmax = FALSE [20261030_071848.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20261030_071848.]: Logging df_agg: CpG#8 [20261030_071848.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071848.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20261030_071848.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20261030_071848.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20261030_071848.]: Entered 'hyperbolic_regression'-Function [20261030_071848.]: 'hyperbolic_regression': minmax = FALSE [20261030_071849.]: Entered 'cubic_regression'-Function [20261030_071849.]: 'cubic_regression': minmax = FALSE [20261030_071849.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20261030_071849.]: Logging df_agg: CpG#9 [20261030_071849.]: c(0, 12.5, 25, 37.5)[20261030_071849.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20261030_071849.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20261030_071849.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20261030_071849.]: Entered 'hyperbolic_regression'-Function [20261030_071849.]: 'hyperbolic_regression': minmax = FALSE [20261030_071849.]: Entered 'cubic_regression'-Function [20261030_071849.]: 'cubic_regression': minmax = FALSE [20261030_071849.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20261030_071849.]: Logging df_agg: row_means [20261030_071849.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20261030_071849.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20261030_071849.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20261030_071849.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20261030_071849.]: Entered 'hyperbolic_regression'-Function [20261030_071849.]: 'hyperbolic_regression': minmax = FALSE [20261030_071849.]: Entered 'cubic_regression'-Function [20261030_071849.]: 'cubic_regression': minmax = FALSE [20261030_071850.]: Entered 'clean_dt'-Function [20261030_071850.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071850.]: got experimental data [20261030_071850.]: Entered 'clean_dt'-Function [20261030_071850.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071850.]: got calibration data [20261030_071850.]: ### Starting with regression calculations ### [20261030_071850.]: Entered 'regression_type1'-Function [20261030_071850.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071850.]: Logging df_agg: CpG#1 [20261030_071850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071850.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071850.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071850.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071850.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071850.]: Entered 'hyperbolic_regression'-Function [20261030_071850.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071851.]: Logging df_agg: CpG#2 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071851.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071851.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071851.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071851.]: Logging df_agg: CpG#3 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071851.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071851.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071851.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071851.]: Logging df_agg: CpG#4 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071851.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071851.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071851.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071852.]: Entered 'cubic_regression'-Function [20261030_071852.]: 'cubic_regression': minmax = FALSE [20261030_071852.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071852.]: Logging df_agg: CpG#5 [20261030_071852.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071852.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071852.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071852.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071852.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071852.]: Entered 'hyperbolic_regression'-Function [20261030_071852.]: 'hyperbolic_regression': minmax = FALSE [20261030_071852.]: Entered 'cubic_regression'-Function [20261030_071852.]: 'cubic_regression': minmax = FALSE [20261030_071850.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071850.]: Logging df_agg: CpG#6 [20261030_071850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071850.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071850.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071850.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071850.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071850.]: Entered 'hyperbolic_regression'-Function [20261030_071850.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071851.]: Logging df_agg: CpG#7 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071851.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071851.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071851.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071851.]: Logging df_agg: CpG#8 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071851.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071851.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071851.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071851.]: Entered 'cubic_regression'-Function [20261030_071851.]: 'cubic_regression': minmax = FALSE [20261030_071851.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071851.]: Logging df_agg: CpG#9 [20261030_071851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071851.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071851.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071851.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071851.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071851.]: Entered 'hyperbolic_regression'-Function [20261030_071851.]: 'hyperbolic_regression': minmax = FALSE [20261030_071852.]: Entered 'cubic_regression'-Function [20261030_071852.]: 'cubic_regression': minmax = FALSE [20261030_071852.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071852.]: Logging df_agg: row_means [20261030_071852.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071852.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071852.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071852.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071852.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071852.]: Entered 'hyperbolic_regression'-Function [20261030_071852.]: 'hyperbolic_regression': minmax = FALSE [20261030_071852.]: Entered 'cubic_regression'-Function [20261030_071852.]: 'cubic_regression': minmax = FALSE [20261030_071853.]: Entered 'regression_type1'-Function [20261030_071854.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071854.]: Logging df_agg: CpG#1 [20261030_071854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071854.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071854.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071854.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071854.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071854.]: Entered 'hyperbolic_regression'-Function [20261030_071854.]: 'hyperbolic_regression': minmax = FALSE [20261030_071854.]: Entered 'cubic_regression'-Function [20261030_071854.]: 'cubic_regression': minmax = FALSE [20261030_071854.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071854.]: Logging df_agg: CpG#2 [20261030_071854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071854.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071854.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071854.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071854.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071854.]: Entered 'hyperbolic_regression'-Function [20261030_071854.]: 'hyperbolic_regression': minmax = FALSE [20261030_071854.]: Entered 'cubic_regression'-Function [20261030_071854.]: 'cubic_regression': minmax = FALSE [20261030_071854.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071854.]: Logging df_agg: CpG#3 [20261030_071854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071854.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071854.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071854.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071854.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071854.]: Entered 'hyperbolic_regression'-Function [20261030_071854.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071855.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071855.]: Logging df_agg: CpG#4 [20261030_071855.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071855.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071855.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071855.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071855.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071855.]: Entered 'hyperbolic_regression'-Function [20261030_071855.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071855.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071855.]: Logging df_agg: CpG#5 [20261030_071855.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071855.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071855.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071855.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071855.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071855.]: Entered 'hyperbolic_regression'-Function [20261030_071855.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071854.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071854.]: Logging df_agg: CpG#6 [20261030_071854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071854.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071854.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071854.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071854.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071854.]: Entered 'hyperbolic_regression'-Function [20261030_071854.]: 'hyperbolic_regression': minmax = FALSE [20261030_071854.]: Entered 'cubic_regression'-Function [20261030_071854.]: 'cubic_regression': minmax = FALSE [20261030_071854.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071854.]: Logging df_agg: CpG#7 [20261030_071854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071854.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071854.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071854.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071854.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071854.]: Entered 'hyperbolic_regression'-Function [20261030_071854.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071855.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071855.]: Logging df_agg: CpG#8 [20261030_071855.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071855.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071855.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071855.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071855.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071855.]: Entered 'hyperbolic_regression'-Function [20261030_071855.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071855.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071855.]: Logging df_agg: CpG#9 [20261030_071855.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071855.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071855.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071855.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071855.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071855.]: Entered 'hyperbolic_regression'-Function [20261030_071855.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071855.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071855.]: Logging df_agg: row_means [20261030_071855.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071855.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071855.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071855.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071855.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071855.]: Entered 'hyperbolic_regression'-Function [20261030_071855.]: 'hyperbolic_regression': minmax = FALSE [20261030_071855.]: Entered 'cubic_regression'-Function [20261030_071855.]: 'cubic_regression': minmax = FALSE [20261030_071856.]: Entered 'solving_equations'-Function [20261030_071856.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20261030_071856.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20261030_071856.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20261030_071856.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20261030_071856.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20261030_071856.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20261030_071856.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20261030_071856.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20261030_071856.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20261030_071856.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20261030_071856.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20261030_071856.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20261030_071856.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20261030_071856.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20261030_071856.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20261030_071856.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20261030_071856.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512351181983175 [20261030_071856.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7524070675129 [20261030_071856.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5219407610414 [20261030_071856.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5271170603888 [20261030_071856.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7910190910562 [20261030_071856.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8687462878094 [20261030_071856.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.552548340177 [20261030_071856.]: Samplename: 75 Root: 77.553 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4375942302221 [20261030_071856.]: Samplename: 87.5 Root: 80.438 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704175608685 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20261030_071856.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20261030_071856.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20261030_071856.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20261030_071856.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20261030_071856.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20261030_071856.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20261030_071856.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20261030_071856.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20261030_071856.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20261030_071856.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20261030_071856.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20261030_071856.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20261030_071856.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20261030_071856.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20261030_071856.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20261030_071856.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20261030_071856.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20261030_071856.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20261030_071856.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20261030_071856.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20261030_071856.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20261030_071856.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20261030_071856.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20261030_071856.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20261030_071856.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20261030_071856.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20261030_071856.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20261030_071856.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20261030_071856.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20261030_071856.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20261030_071856.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20261030_071856.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20261030_071856.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20261030_071856.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20261030_071856.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20261030_071856.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20261030_071856.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20261030_071856.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20261030_071856.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20261030_071856.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20261030_071856.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20261030_071856.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20261030_071856.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20261030_071856.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20261030_071856.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20261030_071856.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20261030_071856.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20261030_071856.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20261030_071856.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20261030_071856.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_071857.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20261030_071857.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20261030_071857.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20261030_071857.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20261030_071857.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20261030_071857.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20261030_071857.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20261030_071857.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20261030_071857.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20261030_071857.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_071857.]: ### Starting with regression calculations ### [20261030_071857.]: Entered 'regression_type1'-Function [20261030_071857.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20261030_071857.]: Logging df_agg: CpG#1 [20261030_071857.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071857.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20261030_071857.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20261030_071857.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20261030_071857.]: Entered 'hyperbolic_regression'-Function [20261030_071857.]: 'hyperbolic_regression': minmax = FALSE [20261030_071858.]: Entered 'cubic_regression'-Function [20261030_071858.]: 'cubic_regression': minmax = FALSE [20261030_071858.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20261030_071858.]: Logging df_agg: CpG#2 [20261030_071858.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071858.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20261030_071858.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20261030_071858.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20261030_071858.]: Entered 'hyperbolic_regression'-Function [20261030_071858.]: 'hyperbolic_regression': minmax = FALSE [20261030_071858.]: Entered 'cubic_regression'-Function [20261030_071858.]: 'cubic_regression': minmax = FALSE [20261030_071858.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512351181983175, 10.7524070675129, 25.5219407610414, 36.5271170603888, 50.7910190910562, 64.8687462878094, 77.552548340177, 80.4375942302221, 100)c(0.512351181983175, 1.7475929324871, 0.5219407610414, 0.972882939611203, 0.791019091056199, 2.3687462878094, 2.552548340177, 7.0624057697779, 0)c(NA, 13.9807434598968, 2.0877630441656, 2.59435450562988, 1.5820381821124, 3.78999406049504, 3.40339778690266, 8.07132087974617, 0) [20261030_071858.]: Logging df_agg: CpG#3 [20261030_071858.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071858.]: c(0.512351181983175, 10.7524070675129, 25.5219407610414, 36.5271170603888, 50.7910190910562, 64.8687462878094, 77.552548340177, 80.4375942302221, 100)[20261030_071858.]: c(0.512351181983175, 1.7475929324871, 0.5219407610414, 0.972882939611203, 0.791019091056199, 2.3687462878094, 2.552548340177, 7.0624057697779, 0)[20261030_071858.]: c(NA, 13.9807434598968, 2.0877630441656, 2.59435450562988, 1.5820381821124, 3.78999406049504, 3.40339778690266, 8.07132087974617, 0) [20261030_071858.]: Entered 'hyperbolic_regression'-Function [20261030_071858.]: 'hyperbolic_regression': minmax = FALSE [20261030_071858.]: Entered 'cubic_regression'-Function [20261030_071858.]: 'cubic_regression': minmax = FALSE [20261030_071858.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20261030_071858.]: Logging df_agg: CpG#4 [20261030_071858.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071858.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20261030_071858.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20261030_071858.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20261030_071858.]: Entered 'hyperbolic_regression'-Function [20261030_071858.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071859.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20261030_071859.]: Logging df_agg: CpG#5 [20261030_071859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071859.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20261030_071859.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20261030_071859.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20261030_071859.]: Entered 'hyperbolic_regression'-Function [20261030_071859.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071858.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20261030_071858.]: Logging df_agg: CpG#6 [20261030_071858.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071858.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20261030_071858.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20261030_071858.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20261030_071858.]: Entered 'hyperbolic_regression'-Function [20261030_071858.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071859.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20261030_071859.]: Logging df_agg: CpG#7 [20261030_071859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071859.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20261030_071859.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20261030_071859.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20261030_071859.]: Entered 'hyperbolic_regression'-Function [20261030_071859.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071859.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20261030_071859.]: Logging df_agg: CpG#8 [20261030_071859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071859.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20261030_071859.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20261030_071859.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20261030_071859.]: Entered 'hyperbolic_regression'-Function [20261030_071859.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071859.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20261030_071859.]: Logging df_agg: CpG#9 [20261030_071859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071859.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20261030_071859.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20261030_071859.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20261030_071859.]: Entered 'hyperbolic_regression'-Function [20261030_071859.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071859.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20261030_071859.]: Logging df_agg: row_means [20261030_071859.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071859.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20261030_071859.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20261030_071859.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20261030_071859.]: Entered 'hyperbolic_regression'-Function [20261030_071859.]: 'hyperbolic_regression': minmax = FALSE [20261030_071859.]: Entered 'cubic_regression'-Function [20261030_071859.]: 'cubic_regression': minmax = FALSE [20261030_071901.]: Entered 'solving_equations'-Function [20261030_071901.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20261030_071901.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20261030_071901.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20261030_071901.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20261030_071901.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20261030_071901.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_071901.]: ### Starting with regression calculations ### [20261030_071901.]: Entered 'regression_type1'-Function [20261030_071902.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20261030_071902.]: Logging df_agg: CpG#1 [20261030_071902.]: c(0, 12.5, 25, 37.5)[20261030_071902.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20261030_071902.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20261030_071902.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20261030_071902.]: Entered 'hyperbolic_regression'-Function [20261030_071902.]: 'hyperbolic_regression': minmax = FALSE [20261030_071902.]: Entered 'cubic_regression'-Function [20261030_071902.]: 'cubic_regression': minmax = FALSE [20261030_071902.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20261030_071902.]: Logging df_agg: CpG#2 [20261030_071902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071902.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20261030_071902.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20261030_071902.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20261030_071902.]: Entered 'hyperbolic_regression'-Function [20261030_071902.]: 'hyperbolic_regression': minmax = FALSE [20261030_071902.]: Entered 'cubic_regression'-Function [20261030_071902.]: 'cubic_regression': minmax = FALSE [20261030_071902.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20261030_071902.]: Logging df_agg: CpG#3 [20261030_071902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20261030_071902.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20261030_071902.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20261030_071902.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20261030_071902.]: Entered 'hyperbolic_regression'-Function [20261030_071902.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071903.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20261030_071903.]: Logging df_agg: CpG#4 [20261030_071903.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20261030_071903.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20261030_071903.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20261030_071903.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20261030_071903.]: Entered 'hyperbolic_regression'-Function [20261030_071903.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071903.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782903, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.43714152170971, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894627, 6.80110981502537, 5.101557746111) [20261030_071903.]: Logging df_agg: CpG#5 [20261030_071903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071903.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782903, 81.5490289118528, 94.898442253889)[20261030_071903.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.43714152170971, 5.9509710881472, 5.101557746111)[20261030_071903.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894627, 6.80110981502537, 5.101557746111) [20261030_071903.]: Entered 'hyperbolic_regression'-Function [20261030_071903.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071902.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20261030_071902.]: Logging df_agg: CpG#6 [20261030_071902.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_071902.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20261030_071902.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20261030_071902.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20261030_071902.]: Entered 'hyperbolic_regression'-Function [20261030_071902.]: 'hyperbolic_regression': minmax = FALSE [20261030_071902.]: Entered 'cubic_regression'-Function [20261030_071902.]: 'cubic_regression': minmax = FALSE [20261030_071902.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20261030_071902.]: Logging df_agg: CpG#7 [20261030_071902.]: c(0, 12.5, 25, 37.5)[20261030_071902.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20261030_071902.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20261030_071902.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20261030_071902.]: Entered 'hyperbolic_regression'-Function [20261030_071902.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071903.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20261030_071903.]: Logging df_agg: CpG#8 [20261030_071903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071903.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20261030_071903.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20261030_071903.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20261030_071903.]: Entered 'hyperbolic_regression'-Function [20261030_071903.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071903.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20261030_071903.]: Logging df_agg: CpG#9 [20261030_071903.]: c(0, 12.5, 25, 37.5)[20261030_071903.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20261030_071903.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20261030_071903.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20261030_071903.]: Entered 'hyperbolic_regression'-Function [20261030_071903.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071903.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20261030_071903.]: Logging df_agg: row_means [20261030_071903.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20261030_071903.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20261030_071903.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20261030_071903.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20261030_071903.]: Entered 'hyperbolic_regression'-Function [20261030_071903.]: 'hyperbolic_regression': minmax = FALSE [20261030_071903.]: Entered 'cubic_regression'-Function [20261030_071903.]: 'cubic_regression': minmax = FALSE [20261030_071904.]: Entered 'solving_equations'-Function [20261030_071904.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 [20261030_071904.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 [20261030_071904.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 [20261030_071904.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 [20261030_071904.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 [20261030_071904.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 [20261030_071904.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 [20261030_071904.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 [20261030_071904.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 [20261030_071904.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 [20261030_071904.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20261030_071904.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20261030_071904.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20261030_071904.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20261030_071904.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20261030_071904.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20261030_071904.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20261030_071904.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20261030_071904.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20261030_071904.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20261030_071904.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20261030_071904.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20261030_071904.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9350520476788 [20261030_071904.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844923865836 [20261030_071904.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520992128575 [20261030_071904.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8326201560678 [20261030_071904.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03520496922116 [20261030_071904.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066787041842 [20261030_071904.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2420333974784 [20261030_071904.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0945761370605 [20261030_071904.]: Samplename: Sample#7 Root: 44.095 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8280759694368 [20261030_071904.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666490629152729 [20261030_071905.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20261030_071905.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 [20261030_071905.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 [20261030_071905.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 [20261030_071905.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 [20261030_071905.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 [20261030_071905.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 [20261030_071905.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 [20261030_071905.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 [20261030_071905.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 [20261030_071905.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 [20261030_071905.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20261030_071905.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 [20261030_071905.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 [20261030_071905.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 [20261030_071905.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 [20261030_071905.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 [20261030_071905.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 [20261030_071905.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 [20261030_071905.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 [20261030_071905.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 [20261030_071905.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 [20261030_071905.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20261030_071905.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20261030_071905.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20261030_071905.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20261030_071905.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20261030_071905.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20261030_071905.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20261030_071905.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20261030_071905.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20261030_071905.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20261030_071905.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20261030_071905.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20261030_071905.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 [20261030_071905.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 [20261030_071905.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 [20261030_071905.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 [20261030_071905.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 [20261030_071905.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 [20261030_071905.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 [20261030_071905.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 [20261030_071905.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 [20261030_071905.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 [20261030_071905.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20261030_071905.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 [20261030_071905.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 [20261030_071905.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 [20261030_071905.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 [20261030_071905.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 [20261030_071905.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 [20261030_071905.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 [20261030_071905.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 [20261030_071905.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 [20261030_071905.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 [20261030_071905.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20261030_071905.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20261030_071905.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20261030_071905.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20261030_071905.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20261030_071905.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20261030_071905.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20261030_071905.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20261030_071905.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20261030_071905.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20261030_071905.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20261030_071905.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20261030_071905.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20261030_071905.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20261030_071905.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20261030_071905.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20261030_071905.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20261030_071905.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20261030_071905.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20261030_071905.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20261030_071905.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20261030_071905.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20261030_071905.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20261030_071905.]: Entered 'solving_equations'-Function [20261030_071905.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20261030_071905.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20261030_071905.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20261030_071905.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20261030_071905.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20261030_071905.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20261030_071905.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20261030_071905.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20261030_071905.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20261030_071905.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20261030_071905.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20261030_071905.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20261030_071905.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20261030_071905.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20261030_071905.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20261030_071905.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20261030_071905.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512351181983175 [20261030_071905.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7524070675129 [20261030_071905.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5219407610414 [20261030_071905.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5271170603888 [20261030_071905.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7910190910562 [20261030_071905.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8687462878094 [20261030_071905.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.552548340177 [20261030_071905.]: Samplename: 75 Root: 77.553 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4375942302221 [20261030_071905.]: Samplename: 87.5 Root: 80.438 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704175608685 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20261030_071905.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20261030_071905.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20261030_071905.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20261030_071905.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20261030_071905.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20261030_071905.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20261030_071905.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20261030_071905.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20261030_071905.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20261030_071905.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20261030_071905.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20261030_071905.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20261030_071905.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20261030_071905.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20261030_071905.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20261030_071905.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20261030_071905.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20261030_071905.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20261030_071905.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20261030_071905.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20261030_071905.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20261030_071905.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20261030_071905.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20261030_071905.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20261030_071905.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20261030_071905.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20261030_071905.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20261030_071905.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20261030_071905.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20261030_071905.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20261030_071905.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20261030_071905.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20261030_071905.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20261030_071905.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20261030_071905.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20261030_071905.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20261030_071905.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20261030_071905.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20261030_071905.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20261030_071905.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20261030_071905.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20261030_071905.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20261030_071905.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20261030_071905.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20261030_071905.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20261030_071905.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20261030_071905.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20261030_071905.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20261030_071905.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20261030_071905.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20261030_071905.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20261030_071905.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20261030_071905.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20261030_071905.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20261030_071905.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20261030_071905.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20261030_071905.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_071905.]: Entered 'clean_dt'-Function [20261030_071905.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071905.]: got experimental data [20261030_071905.]: Entered 'clean_dt'-Function [20261030_071905.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071905.]: got calibration data [20261030_071905.]: ### Starting with regression calculations ### [20261030_071905.]: Entered 'regression_type1'-Function [20261030_071905.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071905.]: Logging df_agg: CpG#1 [20261030_071905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071905.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071905.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071905.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071905.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071905.]: Entered 'hyperbolic_regression'-Function [20261030_071905.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: Entered 'cubic_regression'-Function [20261030_071906.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071906.]: Logging df_agg: CpG#2 [20261030_071906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071906.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071906.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071906.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071906.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071906.]: Entered 'hyperbolic_regression'-Function [20261030_071906.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: Entered 'cubic_regression'-Function [20261030_071906.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071906.]: Logging df_agg: CpG#3 [20261030_071906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071906.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071906.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071906.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071906.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071906.]: Entered 'hyperbolic_regression'-Function [20261030_071906.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071907.]: Logging df_agg: CpG#4 [20261030_071907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071907.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071907.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071907.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071907.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071907.]: Entered 'hyperbolic_regression'-Function [20261030_071907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071907.]: Logging df_agg: CpG#5 [20261030_071907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071907.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071907.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071907.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071907.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071907.]: Entered 'hyperbolic_regression'-Function [20261030_071907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071905.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071905.]: Logging df_agg: CpG#6 [20261030_071906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071906.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071906.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071906.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071906.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071906.]: Entered 'hyperbolic_regression'-Function [20261030_071906.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: Entered 'cubic_regression'-Function [20261030_071906.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071906.]: Logging df_agg: CpG#7 [20261030_071906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071906.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071906.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071906.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071906.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071906.]: Entered 'hyperbolic_regression'-Function [20261030_071906.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: Entered 'cubic_regression'-Function [20261030_071906.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071906.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071906.]: Logging df_agg: CpG#8 [20261030_071907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071907.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071907.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071907.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071907.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071907.]: Entered 'hyperbolic_regression'-Function [20261030_071907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071907.]: Logging df_agg: CpG#9 [20261030_071907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071907.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071907.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071907.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071907.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071907.]: Entered 'hyperbolic_regression'-Function [20261030_071907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071907.]: Logging df_agg: row_means [20261030_071907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071907.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071907.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071907.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071907.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071907.]: Entered 'hyperbolic_regression'-Function [20261030_071907.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071907.]: Entered 'cubic_regression'-Function [20261030_071907.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071909.]: ### Starting with regression calculations ### [20261030_071909.]: Entered 'regression_type1'-Function [20261030_071909.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071909.]: Logging df_agg: CpG#1 [20261030_071909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071909.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071909.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071909.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071909.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071909.]: Entered 'hyperbolic_regression'-Function [20261030_071909.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071909.]: Entered 'cubic_regression'-Function [20261030_071909.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071909.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071909.]: Logging df_agg: CpG#2 [20261030_071909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071909.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071909.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071909.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071909.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071909.]: Entered 'hyperbolic_regression'-Function [20261030_071909.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071910.]: Logging df_agg: CpG#3 [20261030_071910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071910.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071910.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071910.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071910.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071910.]: Entered 'hyperbolic_regression'-Function [20261030_071910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071910.]: Logging df_agg: CpG#4 [20261030_071910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071910.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071910.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071910.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071910.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071910.]: Entered 'hyperbolic_regression'-Function [20261030_071910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071910.]: Logging df_agg: CpG#5 [20261030_071910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071910.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071910.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071910.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071910.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071910.]: Entered 'hyperbolic_regression'-Function [20261030_071910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071909.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071909.]: Logging df_agg: CpG#6 [20261030_071909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071909.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071909.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071909.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071909.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071909.]: Entered 'hyperbolic_regression'-Function [20261030_071909.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071910.]: Logging df_agg: CpG#7 [20261030_071910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071910.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071910.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071910.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071910.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071910.]: Entered 'hyperbolic_regression'-Function [20261030_071910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071910.]: Logging df_agg: CpG#8 [20261030_071910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071910.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071910.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071910.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071910.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071910.]: Entered 'hyperbolic_regression'-Function [20261030_071910.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: Entered 'cubic_regression'-Function [20261030_071910.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071910.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071911.]: Logging df_agg: CpG#9 [20261030_071911.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071911.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071911.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071911.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071911.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071911.]: Entered 'hyperbolic_regression'-Function [20261030_071911.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071911.]: Entered 'cubic_regression'-Function [20261030_071911.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071911.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071911.]: Logging df_agg: row_means [20261030_071911.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071911.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071911.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071911.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071911.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071911.]: Entered 'hyperbolic_regression'-Function [20261030_071911.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071911.]: Entered 'cubic_regression'-Function [20261030_071911.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental Saving _problems/test-algorithm_minmax_TRUE-74.R [20261030_071912.]: ### Starting with regression calculations ### [20261030_071912.]: Entered 'regression_type1'-Function [20261030_071913.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071913.]: Logging df_agg: CpG#1 [20261030_071913.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071913.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071913.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071913.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071913.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071913.]: Entered 'hyperbolic_regression'-Function [20261030_071913.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071913.]: Entered 'cubic_regression'-Function [20261030_071913.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071914.]: Logging df_agg: CpG#2 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071914.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071914.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071914.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: Entered 'cubic_regression'-Function [20261030_071914.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071914.]: Logging df_agg: CpG#3 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071914.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071914.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071914.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: Entered 'cubic_regression'-Function [20261030_071914.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071914.]: Logging df_agg: CpG#4 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071914.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071914.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071914.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: Entered 'cubic_regression'-Function [20261030_071914.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071914.]: Logging df_agg: CpG#5 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071914.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071914.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071914.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: Entered 'cubic_regression'-Function [20261030_071915.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071913.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071913.]: Logging df_agg: CpG#6 [20261030_071913.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071913.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071913.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071913.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071913.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071913.]: Entered 'hyperbolic_regression'-Function [20261030_071913.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: Entered 'cubic_regression'-Function [20261030_071914.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071914.]: Logging df_agg: CpG#7 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071914.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071914.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071914.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: Entered 'cubic_regression'-Function [20261030_071914.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071914.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071914.]: Logging df_agg: CpG#8 [20261030_071914.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071914.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071914.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071914.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071914.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071914.]: Entered 'hyperbolic_regression'-Function [20261030_071914.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: Entered 'cubic_regression'-Function [20261030_071915.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071915.]: Logging df_agg: CpG#9 [20261030_071915.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071915.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071915.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071915.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071915.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071915.]: Entered 'hyperbolic_regression'-Function [20261030_071915.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: Entered 'cubic_regression'-Function [20261030_071915.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071915.]: Logging df_agg: row_means [20261030_071915.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071915.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071915.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071915.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071915.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071915.]: Entered 'hyperbolic_regression'-Function [20261030_071915.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071915.]: Entered 'cubic_regression'-Function [20261030_071915.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071917.]: Entered 'regression_type1'-Function [20261030_071918.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071918.]: Logging df_agg: CpG#1 [20261030_071918.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071918.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071918.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071918.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071918.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071918.]: Entered 'hyperbolic_regression'-Function [20261030_071918.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: Entered 'cubic_regression'-Function [20261030_071918.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071918.]: Logging df_agg: CpG#2 [20261030_071918.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071918.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071918.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071918.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071918.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071918.]: Entered 'hyperbolic_regression'-Function [20261030_071918.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: Entered 'cubic_regression'-Function [20261030_071919.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071919.]: Logging df_agg: CpG#3 [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071919.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071919.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071919.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: Entered 'cubic_regression'-Function [20261030_071919.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071919.]: Logging df_agg: CpG#4 [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071919.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071919.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071919.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: Entered 'cubic_regression'-Function [20261030_071919.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071919.]: Logging df_agg: CpG#5 [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071919.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071919.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071919.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071920.]: Entered 'cubic_regression'-Function [20261030_071920.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071918.]: Logging df_agg: CpG#6 [20261030_071918.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071918.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071918.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071918.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071918.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071918.]: Entered 'hyperbolic_regression'-Function [20261030_071918.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: Entered 'cubic_regression'-Function [20261030_071918.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071918.]: Logging df_agg: CpG#7 [20261030_071918.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071918.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071918.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071918.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071918.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071918.]: Entered 'hyperbolic_regression'-Function [20261030_071918.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071918.]: Entered 'cubic_regression'-Function [20261030_071918.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071919.]: Logging df_agg: CpG#8 [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071919.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071919.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071919.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: Entered 'cubic_regression'-Function [20261030_071919.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071919.]: Logging df_agg: CpG#9 [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071919.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071919.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071919.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: Entered 'cubic_regression'-Function [20261030_071919.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071919.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071919.]: Logging df_agg: row_means [20261030_071919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071919.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071919.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071919.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071919.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071919.]: Entered 'hyperbolic_regression'-Function [20261030_071919.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071920.]: Entered 'cubic_regression'-Function [20261030_071920.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental Saving _problems/test-algorithm_minmax_TRUE-122.R [20261030_071922.]: Entered 'solving_equations'-Function [20261030_071922.]: Solving cubic regression for CpG#1 Coefficients: -60.1686666666667Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 47.455 --> Root in between the borders! Added to results. Coefficients: -17.658Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 11.627 --> Root in between the borders! Added to results. Coefficients: -25.707Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 17.389 --> Root in between the borders! Added to results. Coefficients: -38.137Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 27.031 --> Root in between the borders! Added to results. Coefficients: -5.722Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 3.634 --> Root in between the borders! Added to results. Coefficients: -12.752Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 8.269 --> Root in between the borders! Added to results. Coefficients: -13.512Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 8.782 --> Root in between the borders! Added to results. Coefficients: -28.54Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 19.5 --> Root in between the borders! Added to results. Coefficients: -67.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 55.217 --> Root in between the borders! Added to results. Coefficients: 0.678Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.423 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20261030_071922.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20261030_071922.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20261030_071922.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20261030_071922.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20261030_071922.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20261030_071922.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20261030_071922.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20261030_071922.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20261030_071922.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20261030_071922.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#3 Coefficients: -51.6933333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 58.732 --> Root in between the borders! Added to results. Coefficients: -15.896Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 16.478 --> Root in between the borders! Added to results. Coefficients: -25.385Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 26.896 --> Root in between the borders! Added to results. Coefficients: -35.72Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 38.828 --> Root in between the borders! Added to results. Coefficients: -4.13Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 4.175 --> Root in between the borders! Added to results. Coefficients: -13.64Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 14.069 --> Root in between the borders! Added to results. Coefficients: -14.98Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 15.497 --> Root in between the borders! Added to results. Coefficients: -26.974Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 28.688 --> Root in between the borders! Added to results. Coefficients: -61.934Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 72.668 --> Root in between the borders! Added to results. Coefficients: 0.63Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.631 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving cubic regression for CpG#4 Coefficients: -58.152Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 57.957 --> Root in between the borders! Added to results. Coefficients: -17.912Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 15.856 --> Root in between the borders! Added to results. Coefficients: -28.9603333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 26.373 --> Root in between the borders! Added to results. Coefficients: -41.382Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 39.035 --> Root in between the borders! Added to results. Coefficients: -6.322Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 5.446 --> Root in between the borders! Added to results. Coefficients: -13.822Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 12.115 --> Root in between the borders! Added to results. Coefficients: -17.142Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 15.146 --> Root in between the borders! Added to results. Coefficients: -27.75Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 25.189 --> Root in between the borders! Added to results. Coefficients: -68.064Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 70.473 --> Root in between the borders! Added to results. Coefficients: -1.672Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 Root: 1.425 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20261030_071922.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20261030_071922.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20261030_071922.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20261030_071922.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20261030_071922.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20261030_071922.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20261030_071922.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20261030_071922.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20261030_071922.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20261030_071922.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#7 Coefficients: -41.9606666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 44.834 --> Root in between the borders! Added to results. Coefficients: -10.442Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 9.38 --> Root in between the borders! Added to results. Coefficients: -21.2611428571429Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 20.062 --> Root in between the borders! Added to results. Coefficients: -27.7265Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 27.055 --> Root in between the borders! Added to results. Coefficients: -4.764Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 4.182 --> Root in between the borders! Added to results. Coefficients: -10.394Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 9.335 --> Root in between the borders! Added to results. Coefficients: -12.204Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 11.046 --> Root in between the borders! Added to results. Coefficients: -19.734Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 18.483 --> Root in between the borders! Added to results. Coefficients: -48.542Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 54.775 --> Root in between the borders! Added to results. Coefficients: 0.946Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.813 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20261030_071922.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20261030_071922.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20261030_071922.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20261030_071922.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20261030_071922.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20261030_071922.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20261030_071922.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20261030_071922.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20261030_071922.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20261030_071922.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20261030_071922.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20261030_071922.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20261030_071922.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20261030_071922.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20261030_071922.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20261030_071922.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20261030_071922.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20261030_071922.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20261030_071922.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20261030_071922.]: Entered 'solving_equations'-Function [20261030_071922.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071922.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071922.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20261030_071922.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20261030_071922.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20261030_071922.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20261030_071922.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20261030_071922.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20261030_071922.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20261030_071922.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071922.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071922.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20261030_071922.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20261030_071922.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20261030_071922.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20261030_071922.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20261030_071922.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20261030_071922.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20261030_071922.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20261030_071922.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20261030_071922.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20261030_071922.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20261030_071922.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20261030_071922.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20261030_071922.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20261030_071922.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071922.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071922.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20261030_071922.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20261030_071922.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20261030_071922.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20261030_071922.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20261030_071922.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20261030_071922.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20261030_071922.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20261030_071922.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20261030_071922.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071923.]: Entered 'solving_equations'-Function [20261030_071923.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20261030_071923.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20261030_071923.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20261030_071923.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20261030_071923.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20261030_071923.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20261030_071923.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20261030_071923.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20261030_071923.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20261030_071923.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20261030_071923.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20261030_071923.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20261030_071923.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20261030_071923.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20261030_071923.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20261030_071923.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20261030_071923.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20261030_071923.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20261030_071923.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20261030_071923.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20261030_071923.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20261030_071923.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20261030_071923.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20261030_071923.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20261030_071923.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20261030_071923.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20261030_071923.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20261030_071923.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20261030_071923.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20261030_071923.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20261030_071923.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20261030_071923.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20261030_071923.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20261030_071923.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20261030_071923.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20261030_071923.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20261030_071923.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20261030_071923.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20261030_071923.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20261030_071923.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20261030_071923.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20261030_071923.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20261030_071923.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20261030_071923.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20261030_071923.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20261030_071923.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20261030_071923.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20261030_071923.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20261030_071923.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20261030_071923.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20261030_071923.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20261030_071923.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20261030_071923.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20261030_071923.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20261030_071923.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20261030_071923.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20261030_071923.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20261030_071923.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20261030_071923.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20261030_071923.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20261030_071923.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20261030_071923.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20261030_071923.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20261030_071923.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071923.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071923.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20261030_071923.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20261030_071923.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20261030_071923.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20261030_071923.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20261030_071923.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20261030_071923.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20261030_071923.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20261030_071923.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20261030_071923.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071923.]: ### Starting with regression calculations ### [20261030_071923.]: Entered 'regression_type1'-Function [20261030_071925.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20261030_071925.]: Logging df_agg: CpG#1 [20261030_071925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071925.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20261030_071925.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20261030_071925.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20261030_071925.]: Entered 'hyperbolic_regression'-Function [20261030_071925.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071925.]: Entered 'cubic_regression'-Function [20261030_071925.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071925.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20261030_071925.]: Logging df_agg: CpG#2 [20261030_071925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071925.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20261030_071925.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20261030_071925.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20261030_071925.]: Entered 'hyperbolic_regression'-Function [20261030_071925.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: Entered 'cubic_regression'-Function [20261030_071926.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20261030_071926.]: Logging df_agg: CpG#3 [20261030_071926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071926.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20261030_071926.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20261030_071926.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20261030_071926.]: Entered 'hyperbolic_regression'-Function [20261030_071926.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: Entered 'cubic_regression'-Function [20261030_071926.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20261030_071926.]: Logging df_agg: CpG#4 [20261030_071926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071926.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20261030_071926.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20261030_071926.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20261030_071926.]: Entered 'hyperbolic_regression'-Function [20261030_071926.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20261030_071927.]: Logging df_agg: CpG#5 [20261030_071927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071927.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20261030_071927.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20261030_071927.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20261030_071927.]: Entered 'hyperbolic_regression'-Function [20261030_071927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20261030_071926.]: Logging df_agg: CpG#6 [20261030_071926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071926.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20261030_071926.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20261030_071926.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20261030_071926.]: Entered 'hyperbolic_regression'-Function [20261030_071926.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: Entered 'cubic_regression'-Function [20261030_071926.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071926.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20261030_071926.]: Logging df_agg: CpG#7 [20261030_071926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071926.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20261030_071926.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20261030_071926.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20261030_071926.]: Entered 'hyperbolic_regression'-Function [20261030_071926.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20261030_071927.]: Logging df_agg: CpG#8 [20261030_071927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071927.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20261030_071927.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20261030_071927.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20261030_071927.]: Entered 'hyperbolic_regression'-Function [20261030_071927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20261030_071927.]: Logging df_agg: CpG#9 [20261030_071927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071927.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20261030_071927.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20261030_071927.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20261030_071927.]: Entered 'hyperbolic_regression'-Function [20261030_071927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20261030_071927.]: Logging df_agg: row_means [20261030_071927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071927.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20261030_071927.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20261030_071927.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20261030_071927.]: Entered 'hyperbolic_regression'-Function [20261030_071927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071927.]: Entered 'cubic_regression'-Function [20261030_071927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071928.]: Entered 'solving_equations'-Function [20261030_071928.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071928.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071928.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071928.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071928.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071928.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071928.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071929.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071929.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071929.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071929.]: ### Starting with regression calculations ### [20261030_071929.]: Entered 'regression_type1'-Function [20261030_071932.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20261030_071932.]: Logging df_agg: CpG#1 [20261030_071932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071932.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20261030_071932.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20261030_071932.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20261030_071932.]: Entered 'hyperbolic_regression'-Function [20261030_071932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071932.]: Entered 'cubic_regression'-Function [20261030_071932.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071932.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20261030_071932.]: Logging df_agg: CpG#2 [20261030_071932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071932.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20261030_071932.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20261030_071932.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20261030_071932.]: Entered 'hyperbolic_regression'-Function [20261030_071932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: Entered 'cubic_regression'-Function [20261030_071933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20261030_071933.]: Logging df_agg: CpG#3 [20261030_071933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071933.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20261030_071933.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20261030_071933.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20261030_071933.]: Entered 'hyperbolic_regression'-Function [20261030_071933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: Entered 'cubic_regression'-Function [20261030_071933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20261030_071933.]: Logging df_agg: CpG#4 [20261030_071933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071933.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20261030_071933.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20261030_071933.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20261030_071933.]: Entered 'hyperbolic_regression'-Function [20261030_071933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071934.]: Entered 'cubic_regression'-Function [20261030_071934.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071934.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20261030_071934.]: Logging df_agg: CpG#5 [20261030_071934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071934.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20261030_071934.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20261030_071934.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20261030_071934.]: Entered 'hyperbolic_regression'-Function [20261030_071934.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071934.]: Entered 'cubic_regression'-Function [20261030_071934.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071932.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20261030_071932.]: Logging df_agg: CpG#6 [20261030_071932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071932.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20261030_071932.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20261030_071932.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20261030_071932.]: Entered 'hyperbolic_regression'-Function [20261030_071932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071932.]: Entered 'cubic_regression'-Function [20261030_071932.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071932.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20261030_071932.]: Logging df_agg: CpG#7 [20261030_071932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071932.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20261030_071932.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20261030_071932.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20261030_071932.]: Entered 'hyperbolic_regression'-Function [20261030_071932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: Entered 'cubic_regression'-Function [20261030_071933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20261030_071933.]: Logging df_agg: CpG#8 [20261030_071933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071933.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20261030_071933.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20261030_071933.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20261030_071933.]: Entered 'hyperbolic_regression'-Function [20261030_071933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: Entered 'cubic_regression'-Function [20261030_071933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20261030_071933.]: Logging df_agg: CpG#9 [20261030_071933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071933.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20261030_071933.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20261030_071933.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20261030_071933.]: Entered 'hyperbolic_regression'-Function [20261030_071933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: Entered 'cubic_regression'-Function [20261030_071933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071933.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20261030_071933.]: Logging df_agg: row_means [20261030_071933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071933.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20261030_071933.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20261030_071933.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20261030_071933.]: Entered 'hyperbolic_regression'-Function [20261030_071933.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071934.]: Entered 'cubic_regression'-Function [20261030_071934.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071935.]: Entered 'clean_dt'-Function [20261030_071935.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071935.]: got experimental data [20261030_071935.]: Entered 'clean_dt'-Function [20261030_071935.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_071935.]: got calibration data [20261030_071935.]: ### Starting with regression calculations ### [20261030_071935.]: Entered 'regression_type1'-Function [20261030_071935.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071935.]: Logging df_agg: CpG#1 [20261030_071935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071935.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071935.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071935.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071935.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071935.]: Entered 'hyperbolic_regression'-Function [20261030_071935.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: Entered 'cubic_regression'-Function [20261030_071936.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071936.]: Logging df_agg: CpG#2 [20261030_071936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071936.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071936.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071936.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071936.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071936.]: Entered 'hyperbolic_regression'-Function [20261030_071936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: Entered 'cubic_regression'-Function [20261030_071936.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071936.]: Logging df_agg: CpG#3 [20261030_071936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071936.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071936.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071936.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071936.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071936.]: Entered 'hyperbolic_regression'-Function [20261030_071936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: Entered 'cubic_regression'-Function [20261030_071937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071937.]: Logging df_agg: CpG#4 [20261030_071937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071937.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071937.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071937.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071937.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071937.]: Entered 'hyperbolic_regression'-Function [20261030_071937.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: Entered 'cubic_regression'-Function [20261030_071937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071938.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071938.]: Logging df_agg: CpG#5 [20261030_071938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071938.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071938.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071938.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071938.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071938.]: Entered 'hyperbolic_regression'-Function [20261030_071938.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071938.]: Entered 'cubic_regression'-Function [20261030_071938.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071935.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071935.]: Logging df_agg: CpG#6 [20261030_071935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071935.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071935.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071935.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071935.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071935.]: Entered 'hyperbolic_regression'-Function [20261030_071935.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: Entered 'cubic_regression'-Function [20261030_071936.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071936.]: Logging df_agg: CpG#7 [20261030_071936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071936.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071936.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071936.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071936.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071936.]: Entered 'hyperbolic_regression'-Function [20261030_071936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: Entered 'cubic_regression'-Function [20261030_071936.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071936.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071936.]: Logging df_agg: CpG#8 [20261030_071936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071936.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071936.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071936.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071936.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071936.]: Entered 'hyperbolic_regression'-Function [20261030_071936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: Entered 'cubic_regression'-Function [20261030_071937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071937.]: Logging df_agg: CpG#9 [20261030_071937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071937.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071937.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071937.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071937.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071937.]: Entered 'hyperbolic_regression'-Function [20261030_071937.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: Entered 'cubic_regression'-Function [20261030_071937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071937.]: Logging df_agg: row_means [20261030_071937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071937.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071937.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071937.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071937.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071937.]: Entered 'hyperbolic_regression'-Function [20261030_071937.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071937.]: Entered 'cubic_regression'-Function [20261030_071937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071939.]: Entered 'regression_type1'-Function [20261030_071940.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071940.]: Logging df_agg: CpG#1 [20261030_071940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071940.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_071940.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_071940.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_071940.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_071940.]: Entered 'hyperbolic_regression'-Function [20261030_071940.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071940.]: Entered 'cubic_regression'-Function [20261030_071940.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071940.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071940.]: Logging df_agg: CpG#2 [20261030_071940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071940.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_071940.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_071940.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_071940.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_071940.]: Entered 'hyperbolic_regression'-Function [20261030_071940.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: Entered 'cubic_regression'-Function [20261030_071941.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071941.]: Logging df_agg: CpG#3 [20261030_071941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071941.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_071941.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_071941.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_071941.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_071941.]: Entered 'hyperbolic_regression'-Function [20261030_071941.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: Entered 'cubic_regression'-Function [20261030_071941.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071941.]: Logging df_agg: CpG#4 [20261030_071941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071941.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_071941.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_071941.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_071941.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_071941.]: Entered 'hyperbolic_regression'-Function [20261030_071941.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: Entered 'cubic_regression'-Function [20261030_071942.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071942.]: Logging df_agg: CpG#5 [20261030_071942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071942.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_071942.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_071942.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_071942.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_071942.]: Entered 'hyperbolic_regression'-Function [20261030_071942.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: Entered 'cubic_regression'-Function [20261030_071942.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071940.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071940.]: Logging df_agg: CpG#6 [20261030_071940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071940.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_071940.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_071940.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_071940.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_071940.]: Entered 'hyperbolic_regression'-Function [20261030_071940.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071940.]: Entered 'cubic_regression'-Function [20261030_071940.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071941.]: Logging df_agg: CpG#7 [20261030_071941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071941.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_071941.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_071941.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_071941.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_071941.]: Entered 'hyperbolic_regression'-Function [20261030_071941.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: Entered 'cubic_regression'-Function [20261030_071941.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071941.]: Logging df_agg: CpG#8 [20261030_071941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071941.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_071941.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_071941.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_071941.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_071941.]: Entered 'hyperbolic_regression'-Function [20261030_071941.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071941.]: Entered 'cubic_regression'-Function [20261030_071941.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071942.]: Logging df_agg: CpG#9 [20261030_071942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071942.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_071942.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_071942.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_071942.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_071942.]: Entered 'hyperbolic_regression'-Function [20261030_071942.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: Entered 'cubic_regression'-Function [20261030_071942.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071942.]: Logging df_agg: row_means [20261030_071942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071942.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_071942.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_071942.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_071942.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_071942.]: Entered 'hyperbolic_regression'-Function [20261030_071942.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071942.]: Entered 'cubic_regression'-Function [20261030_071942.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071943.]: Entered 'solving_equations'-Function [20261030_071943.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20261030_071943.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20261030_071943.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20261030_071943.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20261030_071943.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20261030_071943.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20261030_071943.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20261030_071943.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20261030_071943.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20261030_071943.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20261030_071943.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20261030_071943.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20261030_071943.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20261030_071943.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20261030_071943.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20261030_071943.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20261030_071943.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20261030_071943.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20261030_071943.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20261030_071943.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20261030_071943.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20261030_071943.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20261030_071943.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20261030_071943.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20261030_071943.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20261030_071943.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20261030_071943.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20261030_071943.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20261030_071943.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20261030_071943.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20261030_071943.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20261030_071943.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20261030_071943.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20261030_071943.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20261030_071943.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20261030_071943.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20261030_071943.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20261030_071943.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20261030_071943.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20261030_071943.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20261030_071943.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20261030_071943.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20261030_071943.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071943.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20261030_071943.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20261030_071943.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20261030_071943.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20261030_071943.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20261030_071943.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20261030_071943.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20261030_071943.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20261030_071943.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071943.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071944.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20261030_071944.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20261030_071944.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20261030_071944.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20261030_071944.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20261030_071944.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20261030_071944.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20261030_071944.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20261030_071944.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071944.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071944.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20261030_071944.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20261030_071944.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20261030_071944.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20261030_071944.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20261030_071944.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20261030_071944.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20261030_071944.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20261030_071944.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071944.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071944.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20261030_071944.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20261030_071944.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20261030_071944.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20261030_071944.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20261030_071944.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20261030_071944.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20261030_071944.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20261030_071944.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20261030_071944.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071944.]: ### Starting with regression calculations ### [20261030_071944.]: Entered 'regression_type1'-Function [20261030_071944.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20261030_071944.]: Logging df_agg: CpG#1 [20261030_071944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071944.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20261030_071944.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20261030_071944.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20261030_071944.]: Entered 'hyperbolic_regression'-Function [20261030_071944.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071945.]: Entered 'cubic_regression'-Function [20261030_071945.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071945.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20261030_071945.]: Logging df_agg: CpG#2 [20261030_071945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071945.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20261030_071945.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20261030_071945.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20261030_071945.]: Entered 'hyperbolic_regression'-Function [20261030_071945.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: Entered 'cubic_regression'-Function [20261030_071946.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20261030_071946.]: Logging df_agg: CpG#3 [20261030_071946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071946.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20261030_071946.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20261030_071946.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20261030_071946.]: Entered 'hyperbolic_regression'-Function [20261030_071946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: Entered 'cubic_regression'-Function [20261030_071946.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20261030_071946.]: Logging df_agg: CpG#4 [20261030_071946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071946.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20261030_071946.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20261030_071946.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20261030_071946.]: Entered 'hyperbolic_regression'-Function [20261030_071946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: Entered 'cubic_regression'-Function [20261030_071946.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20261030_071946.]: Logging df_agg: CpG#5 [20261030_071946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071946.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20261030_071946.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20261030_071946.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20261030_071946.]: Entered 'hyperbolic_regression'-Function [20261030_071946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071947.]: Entered 'cubic_regression'-Function [20261030_071947.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071944.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20261030_071944.]: Logging df_agg: CpG#6 [20261030_071944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071944.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20261030_071944.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20261030_071944.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20261030_071944.]: Entered 'hyperbolic_regression'-Function [20261030_071945.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071945.]: Entered 'cubic_regression'-Function [20261030_071945.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071945.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20261030_071945.]: Logging df_agg: CpG#7 [20261030_071945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071945.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20261030_071945.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20261030_071945.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20261030_071945.]: Entered 'hyperbolic_regression'-Function [20261030_071945.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: Entered 'cubic_regression'-Function [20261030_071946.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20261030_071946.]: Logging df_agg: CpG#8 [20261030_071946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071946.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20261030_071946.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20261030_071946.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20261030_071946.]: Entered 'hyperbolic_regression'-Function [20261030_071946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: Entered 'cubic_regression'-Function [20261030_071946.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071946.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20261030_071946.]: Logging df_agg: CpG#9 [20261030_071946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071946.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20261030_071946.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20261030_071946.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20261030_071946.]: Entered 'hyperbolic_regression'-Function [20261030_071946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071947.]: Entered 'cubic_regression'-Function [20261030_071947.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071947.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20261030_071947.]: Logging df_agg: row_means [20261030_071947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071947.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20261030_071947.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20261030_071947.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20261030_071947.]: Entered 'hyperbolic_regression'-Function [20261030_071947.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071947.]: Entered 'cubic_regression'-Function [20261030_071947.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071948.]: Entered 'solving_equations'-Function [20261030_071948.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20261030_071948.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071948.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071948.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20261030_071949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071949.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20261030_071949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071949.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20261030_071949.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071949.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20261030_071949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_071949.]: ### Starting with regression calculations ### [20261030_071949.]: Entered 'regression_type1'-Function [20261030_071950.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20261030_071950.]: Logging df_agg: CpG#1 [20261030_071950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071950.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20261030_071950.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20261030_071950.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20261030_071950.]: Entered 'hyperbolic_regression'-Function [20261030_071950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071950.]: Entered 'cubic_regression'-Function [20261030_071950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071950.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20261030_071950.]: Logging df_agg: CpG#2 [20261030_071950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071950.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20261030_071950.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20261030_071950.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20261030_071950.]: Entered 'hyperbolic_regression'-Function [20261030_071950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: Entered 'cubic_regression'-Function [20261030_071951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20261030_071951.]: Logging df_agg: CpG#3 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20261030_071951.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20261030_071951.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: Entered 'cubic_regression'-Function [20261030_071951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20261030_071951.]: Logging df_agg: CpG#4 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20261030_071951.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20261030_071951.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: Entered 'cubic_regression'-Function [20261030_071951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20261030_071951.]: Logging df_agg: CpG#5 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20261030_071951.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20261030_071951.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071952.]: Entered 'cubic_regression'-Function [20261030_071952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071950.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20261030_071950.]: Logging df_agg: CpG#6 [20261030_071950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071950.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20261030_071950.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20261030_071950.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20261030_071950.]: Entered 'hyperbolic_regression'-Function [20261030_071950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071950.]: Entered 'cubic_regression'-Function [20261030_071950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20261030_071951.]: Logging df_agg: CpG#7 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20261030_071951.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20261030_071951.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: Entered 'cubic_regression'-Function [20261030_071951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20261030_071951.]: Logging df_agg: CpG#8 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20261030_071951.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20261030_071951.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: Entered 'cubic_regression'-Function [20261030_071951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071951.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20261030_071951.]: Logging df_agg: CpG#9 [20261030_071951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071951.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20261030_071951.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20261030_071951.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20261030_071951.]: Entered 'hyperbolic_regression'-Function [20261030_071951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071952.]: Entered 'cubic_regression'-Function [20261030_071952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071952.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20261030_071952.]: Logging df_agg: row_means [20261030_071952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_071952.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20261030_071952.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20261030_071952.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20261030_071952.]: Entered 'hyperbolic_regression'-Function [20261030_071952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071952.]: Entered 'cubic_regression'-Function [20261030_071952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_071953.]: Entered 'solving_equations'-Function [20261030_071953.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 [20261030_071953.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 [20261030_071953.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 [20261030_071953.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 [20261030_071953.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 [20261030_071953.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 [20261030_071953.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 [20261030_071953.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 [20261030_071953.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 [20261030_071953.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20261030_071953.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20261030_071953.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20261030_071953.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20261030_071953.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20261030_071953.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20261030_071953.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20261030_071953.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20261030_071953.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20261030_071953.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20261030_071953.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 [20261030_071953.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 [20261030_071953.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 [20261030_071953.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 [20261030_071953.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 [20261030_071953.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 [20261030_071953.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 [20261030_071953.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 [20261030_071953.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 [20261030_071953.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 [20261030_071953.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 [20261030_071953.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 [20261030_071953.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 [20261030_071953.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 [20261030_071953.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 [20261030_071953.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 [20261030_071953.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 [20261030_071953.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 [20261030_071953.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 [20261030_071953.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. [20261030_071953.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20261030_071953.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20261030_071953.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20261030_071953.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20261030_071953.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20261030_071953.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20261030_071953.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20261030_071953.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20261030_071953.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20261030_071953.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20261030_071953.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 [20261030_071953.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 [20261030_071953.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 [20261030_071953.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 [20261030_071953.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 [20261030_071953.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 [20261030_071953.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 [20261030_071953.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 [20261030_071953.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 [20261030_071953.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20261030_071953.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20261030_071953.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20261030_071953.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20261030_071953.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20261030_071953.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20261030_071953.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20261030_071953.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20261030_071953.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20261030_071953.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20261030_071953.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20261030_071953.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20261030_071953.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20261030_071953.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20261030_071953.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20261030_071953.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20261030_071953.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20261030_071953.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20261030_071953.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20261030_071953.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20261030_071953.]: Entered 'solving_equations'-Function [20261030_071953.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20261030_071953.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20261030_071953.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20261030_071953.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20261030_071953.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20261030_071953.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20261030_071953.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20261030_071953.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20261030_071953.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20261030_071953.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20261030_071953.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20261030_071953.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20261030_071953.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20261030_071953.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20261030_071953.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20261030_071953.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20261030_071953.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20261030_071953.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20261030_071953.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20261030_071953.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20261030_071953.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20261030_071953.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20261030_071953.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20261030_071953.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20261030_071953.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20261030_071953.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20261030_071953.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20261030_071953.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20261030_071953.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20261030_071953.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20261030_071953.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20261030_071953.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20261030_071953.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20261030_071953.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20261030_071953.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20261030_071953.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20261030_071953.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20261030_071953.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20261030_071953.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20261030_071953.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20261030_071953.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20261030_071953.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20261030_071953.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20261030_071953.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20261030_071953.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20261030_071953.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20261030_071953.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20261030_071953.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20261030_071953.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20261030_071953.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20261030_071953.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20261030_071953.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20261030_071953.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20261030_071953.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20261030_071953.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20261030_071953.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20261030_071953.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20261030_071953.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20261030_071953.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20261030_071953.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20261030_071953.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20261030_072001.]: on_start: using future::plan("sequential") [20261030_072001.]: Entered 'clean_dt'-Function [20261030_072001.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072001.]: got experimental data [20261030_072001.]: Entered 'clean_dt'-Function [20261030_072001.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072001.]: got calibration data [20261030_072001.]: ### Starting with regression calculations ### [20261030_072001.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072002.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072002.]: Logging df_agg: CpG#1 [20261030_072002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072002.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_072002.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_072002.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_072002.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072002.]: Entered 'hyperbolic_regression'-Function [20261030_072002.]: 'hyperbolic_regression': minmax = FALSE [20261030_072002.]: Entered 'cubic_regression'-Function [20261030_072002.]: 'cubic_regression': minmax = FALSE [20261030_072002.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072002.]: Logging df_agg: CpG#2 [20261030_072002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072002.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_072002.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_072002.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_072002.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072002.]: Entered 'hyperbolic_regression'-Function [20261030_072002.]: 'hyperbolic_regression': minmax = FALSE [20261030_072002.]: Entered 'cubic_regression'-Function [20261030_072002.]: 'cubic_regression': minmax = FALSE [20261030_072002.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072002.]: Logging df_agg: CpG#3 [20261030_072002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072002.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_072002.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_072002.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_072002.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072002.]: Entered 'hyperbolic_regression'-Function [20261030_072002.]: 'hyperbolic_regression': minmax = FALSE [20261030_072002.]: Entered 'cubic_regression'-Function [20261030_072002.]: 'cubic_regression': minmax = FALSE [20261030_072002.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072002.]: Logging df_agg: CpG#4 [20261030_072002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072002.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_072002.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_072002.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_072002.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072002.]: Entered 'hyperbolic_regression'-Function [20261030_072002.]: 'hyperbolic_regression': minmax = FALSE [20261030_072002.]: Entered 'cubic_regression'-Function [20261030_072002.]: 'cubic_regression': minmax = FALSE [20261030_072002.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072002.]: Logging df_agg: CpG#5 [20261030_072002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072002.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_072002.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_072002.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_072002.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072002.]: Entered 'hyperbolic_regression'-Function [20261030_072002.]: 'hyperbolic_regression': minmax = FALSE [20261030_072003.]: Entered 'cubic_regression'-Function [20261030_072003.]: 'cubic_regression': minmax = FALSE [20261030_072003.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072003.]: Logging df_agg: CpG#6 [20261030_072003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072003.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_072003.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_072003.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_072003.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072003.]: Entered 'hyperbolic_regression'-Function [20261030_072003.]: 'hyperbolic_regression': minmax = FALSE [20261030_072003.]: Entered 'cubic_regression'-Function [20261030_072003.]: 'cubic_regression': minmax = FALSE [20261030_072003.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072003.]: Logging df_agg: CpG#7 [20261030_072003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072003.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_072003.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_072003.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_072003.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072003.]: Entered 'hyperbolic_regression'-Function [20261030_072003.]: 'hyperbolic_regression': minmax = FALSE [20261030_072003.]: Entered 'cubic_regression'-Function [20261030_072003.]: 'cubic_regression': minmax = FALSE [20261030_072003.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072003.]: Logging df_agg: CpG#8 [20261030_072003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072003.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_072003.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_072003.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_072003.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072003.]: Entered 'hyperbolic_regression'-Function [20261030_072003.]: 'hyperbolic_regression': minmax = FALSE [20261030_072003.]: Entered 'cubic_regression'-Function [20261030_072003.]: 'cubic_regression': minmax = FALSE [20261030_072003.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072003.]: Logging df_agg: CpG#9 [20261030_072003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072003.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_072003.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_072003.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_072003.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072003.]: Entered 'hyperbolic_regression'-Function [20261030_072003.]: 'hyperbolic_regression': minmax = FALSE [20261030_072003.]: Entered 'cubic_regression'-Function [20261030_072003.]: 'cubic_regression': minmax = FALSE [20261030_072003.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072003.]: Logging df_agg: row_means [20261030_072003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072003.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_072003.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20261030_072003.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_072003.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072003.]: Entered 'hyperbolic_regression'-Function [20261030_072003.]: 'hyperbolic_regression': minmax = FALSE [20261030_072004.]: Entered 'cubic_regression'-Function [20261030_072004.]: 'cubic_regression': minmax = FALSE [20261030_072004.]: ### Starting with plotting ### [20261030_072004.]: Creating plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1.png [20261030_072004.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719340309079 , a = -108.567970808522 , b = -937.719340309079 , d = -232.057118693744 [20261030_072004.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120089e-05 , b = -0.00558069687349687 , c = 0.784061853455187 , d = 1.93182659932659 [20261030_072006.]: Creating plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2.png [20261030_072006.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14261665624 , a = -141.862268789227 , b = -2393.14261665624 , d = -268.331588079242 [20261030_072006.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904603e-06 , b = 0.00293158941798941 , c = 0.514821742825076 , d = 9.27667003367005 [20261030_072007.]: Creating plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3.png [20261030_072007.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46854828772 , a = -170.424503308931 , b = -1477.46854828772 , d = -327.190151237115 [20261030_072007.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409347 , c = 0.627831292287959 , d = 3.88705218855219 [20261030_072008.]: Creating plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4.png [20261030_072008.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87797461636 , a = -162.169748496383 , b = -2115.87797461636 , d = -299.662247484741 [20261030_072008.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598365 , d = 6.17055050505049 [20261030_072009.]: Creating plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5.png [20261030_072009.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32736838 , a = -151.440063494562 , b = -1383.32736838 , d = -304.200248842184 [20261030_072009.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193035e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053873 [20261030_072010.]: Creating plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6.png [20261030_072010.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.5930607092 , a = -125.016145862546 , b = -1550.5930607092 , d = -247.279678955355 [20261030_072010.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037017 , c = 0.561022132435467 , d = 6.1479276094276 [20261030_072011.]: Creating plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7.png [20261030_072011.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844234318051 , a = -75.2724788120112 , b = -772.844234318051 , d = -226.673133653446 [20261030_072011.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050506e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454545 [20261030_072012.]: Creating plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8.png [20261030_072012.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.7360320796 , a = -278.017258466908 , b = -3872.7360320796 , d = -438.894248151323 [20261030_072012.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207631e-05 , b = 0.00447978143338143 , c = 0.534897737694405 , d = 9.56981481481482 [20261030_072013.]: Creating plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9.png [20261030_072013.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925482856018 , a = -70.6796085071682 , b = -843.925482856018 , d = -184.48215321449 [20261030_072013.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487094e-05 , b = -0.00542281173641175 , c = 0.67899229597563 , d = 2.72413468013467 [20261030_072014.]: Creating plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans.png [20261030_072014.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75753724836 , a = -127.675023855212 , b = -1525.75753724836 , d = -263.003037481986 [20261030_072014.]: # CpG-site: row_means Cubic: Using a = 2.88923726150394e-05 , b = -0.00062995927529263 , c = 0.600117857944526 , d = 5.17789562289561 [20261030_072015.]: Entered 'solving_equations'-Function [20261030_072015.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222885156455 [20261030_072015.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698495556586 [20261030_072015.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781922821967 [20261030_072015.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730447337805 [20261030_072015.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349370305632 [20261030_072015.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582771479267 [20261030_072015.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090793349633 [20261030_072015.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271919506234 [20261030_072015.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893249339 [20261030_072015.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20261030_072015.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.1366053801563 [20261030_072015.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129699017299 [20261030_072015.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740006048703 [20261030_072015.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449227732 [20261030_072015.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002766251 [20261030_072015.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493558538 [20261030_072015.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668161269 [20261030_072015.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158174118 [20261030_072015.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827254133 [20261030_072015.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20261030_072015.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512356422800093 [20261030_072015.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523883918942 [20261030_072015.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218908380562 [20261030_072015.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270463483738 [20261030_072015.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909246035692 [20261030_072015.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686318435147 [20261030_072015.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188998562 [20261030_072015.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.43746177388 [20261030_072015.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024799432 [20261030_072015.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20261030_072015.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.51950428143365 [20261030_072015.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934141553705 [20261030_072015.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417675263 [20261030_072015.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817129378933 [20261030_072015.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.584318358551 [20261030_072015.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672240213777 [20261030_072015.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549280299908 [20261030_072015.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821799268236 [20261030_072015.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079126586644 [20261030_072015.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20261030_072015.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558625590011 [20261030_072016.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649675281395 [20261030_072016.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821351505 [20261030_072016.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773621111459 [20261030_072016.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659387758448 [20261030_072016.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342274569787 [20261030_072016.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915261004628 [20261030_072016.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887711831 [20261030_072016.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759782017 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.1381638431078 [20261030_072016.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635559741194 [20261030_072016.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107450277878 [20261030_072016.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073684875 [20261030_072016.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570768027049 [20261030_072016.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944628929 [20261030_072016.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689057274465 [20261030_072016.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086469622 [20261030_072016.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261636978136 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238206831164 [20261030_072016.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993156416433 [20261030_072016.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951789394559 [20261030_072016.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310424180287 [20261030_072016.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588744693427 [20261030_072016.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364952689567 [20261030_072016.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361097875729 [20261030_072016.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328240316899 [20261030_072016.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004236510647 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068204507635 [20261030_072016.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535128598895 [20261030_072016.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762622748802 [20261030_072016.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122077013407 [20261030_072016.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842657248288 [20261030_072016.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732313170861 [20261030_072016.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326979275666 [20261030_072016.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.342723302295 [20261030_072016.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406416234 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340377083887 [20261030_072016.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082189016604 [20261030_072016.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.916456534699 [20261030_072016.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334777985113 [20261030_072016.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895637612 [20261030_072016.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930528019551 [20261030_072016.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055762352 [20261030_072016.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294955118961 [20261030_072016.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466514 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290941118544918 [20261030_072016.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412410207034 [20261030_072016.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081504806604 [20261030_072016.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723372689 [20261030_072016.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348828589515 [20261030_072016.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135212954033 [20261030_072016.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.534271088471 [20261030_072016.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411228932089 [20261030_072016.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942400102 [20261030_072016.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_072016.]: ### Starting with regression calculations ### [20261030_072016.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072016.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698495556586, 24.4781922821967, 38.1730447337805, 52.3349370305632, 65.4582771479267, 75.0090793349633, 81.5271919506234, 100)c(0, 0.330150444341401, 0.521807717803298, 0.6730447337805, 2.3349370305632, 2.95827714792669, 0.00907933496330315, 5.9728080493766, 0)c(NA, 2.64120355473121, 2.08723087121319, 1.794785956748, 4.66987406112639, 4.73324343668271, 0.0121057799510709, 6.82606634214468, 0) [20261030_072016.]: Logging df_agg: CpG#1 [20261030_072016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072016.]: c(0, 12.1698495556586, 24.4781922821967, 38.1730447337805, 52.3349370305632, 65.4582771479267, 75.0090793349633, 81.5271919506234, 100)[20261030_072016.]: c(0, 0.330150444341401, 0.521807717803298, 0.6730447337805, 2.3349370305632, 2.95827714792669, 0.00907933496330315, 5.9728080493766, 0)[20261030_072016.]: c(NA, 2.64120355473121, 2.08723087121319, 1.794785956748, 4.66987406112639, 4.73324343668271, 0.0121057799510709, 6.82606634214468, 0) [20261030_072016.]: Entered 'hyperbolic_regression'-Function [20261030_072016.]: 'hyperbolic_regression': minmax = FALSE [20261030_072016.]: Entered 'cubic_regression'-Function [20261030_072016.]: 'cubic_regression': minmax = FALSE [20261030_072016.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.1366053801563, 11.4129699017299, 26.1740006048703, 35.1050449227732, 47.6855002766251, 67.1440493558538, 75.7644668161269, 84.4054158174118, 100)c(1.1366053801563, 1.0870300982701, 1.1740006048703, 2.3949550772268, 2.3144997233749, 4.6440493558538, 0.764466816126898, 3.09458418258821, 0)c(NA, 8.6962407861608, 4.6960024194812, 6.38654687260479, 4.6289994467498, 7.43047896936607, 1.0192890881692, 3.53666763724366, 0) [20261030_072016.]: Logging df_agg: CpG#2 [20261030_072016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072016.]: c(1.1366053801563, 11.4129699017299, 26.1740006048703, 35.1050449227732, 47.6855002766251, 67.1440493558538, 75.7644668161269, 84.4054158174118, 100)[20261030_072016.]: c(1.1366053801563, 1.0870300982701, 1.1740006048703, 2.3949550772268, 2.3144997233749, 4.6440493558538, 0.764466816126898, 3.09458418258821, 0)[20261030_072016.]: c(NA, 8.6962407861608, 4.6960024194812, 6.38654687260479, 4.6289994467498, 7.43047896936607, 1.0192890881692, 3.53666763724366, 0) [20261030_072016.]: Entered 'hyperbolic_regression'-Function [20261030_072016.]: 'hyperbolic_regression': minmax = FALSE [20261030_072016.]: Entered 'cubic_regression'-Function [20261030_072016.]: 'cubic_regression': minmax = FALSE [20261030_072016.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512356422800093, 10.7523883918942, 25.5218908380562, 36.5270463483738, 50.7909246035692, 64.8686318435147, 77.5524188998562, 80.43746177388, 100)c(0.512356422800093, 1.7476116081058, 0.521890838056201, 0.972953651626199, 0.790924603569202, 2.36863184351471, 2.55241889985621, 7.06253822612, 0)c(NA, 13.9808928648464, 2.08756335222481, 2.5945430710032, 1.5818492071384, 3.78981094962353, 3.40322519980828, 8.07147225842285, 0) [20261030_072016.]: Logging df_agg: CpG#3 [20261030_072016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072016.]: c(0.512356422800093, 10.7523883918942, 25.5218908380562, 36.5270463483738, 50.7909246035692, 64.8686318435147, 77.5524188998562, 80.43746177388, 100)[20261030_072016.]: c(0.512356422800093, 1.7476116081058, 0.521890838056201, 0.972953651626199, 0.790924603569202, 2.36863184351471, 2.55241889985621, 7.06253822612, 0)[20261030_072016.]: c(NA, 13.9808928648464, 2.08756335222481, 2.5945430710032, 1.5818492071384, 3.78981094962353, 3.40322519980828, 8.07147225842285, 0) [20261030_072016.]: Entered 'hyperbolic_regression'-Function [20261030_072016.]: 'hyperbolic_regression': minmax = FALSE [20261030_072016.]: Entered 'cubic_regression'-Function [20261030_072016.]: 'cubic_regression': minmax = FALSE [20261030_072016.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934141553705, 24.2685417675263, 38.0817129378933, 48.584318358551, 67.672240213777, 74.1549280299908, 82.8821799268236, 100)c(0, 0.00658584462949996, 0.731458232473699, 0.5817129378933, 1.415681641449, 5.172240213777, 0.845071970009201, 4.6178200731764, 0)c(NA, 0.0526867570359997, 2.9258329298948, 1.5512345010488, 2.831363282898, 8.2755843420432, 1.12676262667893, 5.27750865505875, 0) [20261030_072016.]: Logging df_agg: CpG#4 [20261030_072016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072016.]: c(0, 12.4934141553705, 24.2685417675263, 38.0817129378933, 48.584318358551, 67.672240213777, 74.1549280299908, 82.8821799268236, 100)[20261030_072016.]: c(0, 0.00658584462949996, 0.731458232473699, 0.5817129378933, 1.415681641449, 5.172240213777, 0.845071970009201, 4.6178200731764, 0)[20261030_072016.]: c(NA, 0.0526867570359997, 2.9258329298948, 1.5512345010488, 2.831363282898, 8.2755843420432, 1.12676262667893, 5.27750865505875, 0) [20261030_072016.]: Entered 'hyperbolic_regression'-Function [20261030_072016.]: 'hyperbolic_regression': minmax = FALSE [20261030_072017.]: Entered 'cubic_regression'-Function [20261030_072017.]: 'cubic_regression': minmax = FALSE [20261030_072017.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558625590011, 10.1649675281395, 23.9830821351505, 37.2773621111459, 50.8659387758448, 62.4342274569787, 76.3915261004628, 86.1597887711831, 100)c(2.41558625590011, 2.3350324718605, 1.0169178648495, 0.222637888854102, 0.865938775844803, 0.0657725430213034, 1.3915261004628, 1.3402112288169, 0)c(NA, 18.680259774884, 4.067671459398, 0.593701036944272, 1.73187755168961, 0.105236068834085, 1.8553681339504, 1.53166997579074, 0) [20261030_072017.]: Logging df_agg: CpG#5 [20261030_072017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072017.]: c(2.41558625590011, 10.1649675281395, 23.9830821351505, 37.2773621111459, 50.8659387758448, 62.4342274569787, 76.3915261004628, 86.1597887711831, 100)[20261030_072017.]: c(2.41558625590011, 2.3350324718605, 1.0169178648495, 0.222637888854102, 0.865938775844803, 0.0657725430213034, 1.3915261004628, 1.3402112288169, 0)[20261030_072017.]: c(NA, 18.680259774884, 4.067671459398, 0.593701036944272, 1.73187755168961, 0.105236068834085, 1.8553681339504, 1.53166997579074, 0) [20261030_072017.]: Entered 'hyperbolic_regression'-Function [20261030_072017.]: 'hyperbolic_regression': minmax = FALSE [20261030_072017.]: Entered 'cubic_regression'-Function [20261030_072017.]: 'cubic_regression': minmax = FALSE [20261030_072017.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.1381638431078, 11.8635559741194, 26.5107450277878, 35.3205073684875, 50.0570768027049, 64.9602944628929, 73.6689057274465, 87.1266086469622, 100)c(0.1381638431078, 0.6364440258806, 1.5107450277878, 2.1794926315125, 0.0570768027048985, 2.4602944628929, 1.3310942725535, 0.373391353037803, 0)c(NA, 5.0915522070448, 6.04298011115119, 5.81198035070001, 0.114153605409797, 3.93647114062865, 1.77479236340467, 0.426732974900347, 0) [20261030_072017.]: Logging df_agg: CpG#6 [20261030_072017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072017.]: c(0.1381638431078, 11.8635559741194, 26.5107450277878, 35.3205073684875, 50.0570768027049, 64.9602944628929, 73.6689057274465, 87.1266086469622, 100)[20261030_072017.]: c(0.1381638431078, 0.6364440258806, 1.5107450277878, 2.1794926315125, 0.0570768027048985, 2.4602944628929, 1.3310942725535, 0.373391353037803, 0)[20261030_072017.]: c(NA, 5.0915522070448, 6.04298011115119, 5.81198035070001, 0.114153605409797, 3.93647114062865, 1.77479236340467, 0.426732974900347, 0) [20261030_072017.]: Entered 'hyperbolic_regression'-Function [20261030_072017.]: 'hyperbolic_regression': minmax = FALSE [20261030_072017.]: Entered 'cubic_regression'-Function [20261030_072017.]: 'cubic_regression': minmax = FALSE [20261030_072017.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993156416433, 24.5951789394559, 37.8310424180287, 53.5588744693427, 65.9364952689567, 75.7361097875729, 79.4328240316899, 100)c(0, 2.3006843583567, 0.404821060544101, 0.331042418028701, 3.5588744693427, 3.4364952689567, 0.7361097875729, 8.0671759683101, 0)c(NA, 18.4054748668536, 1.61928424217641, 0.882779781409871, 7.11774893868539, 5.49839243033073, 0.981479716763867, 9.21962967806868, 0) [20261030_072017.]: Logging df_agg: CpG#7 [20261030_072017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072017.]: c(0, 10.1993156416433, 24.5951789394559, 37.8310424180287, 53.5588744693427, 65.9364952689567, 75.7361097875729, 79.4328240316899, 100)[20261030_072017.]: c(0, 2.3006843583567, 0.404821060544101, 0.331042418028701, 3.5588744693427, 3.4364952689567, 0.7361097875729, 8.0671759683101, 0)[20261030_072017.]: c(NA, 18.4054748668536, 1.61928424217641, 0.882779781409871, 7.11774893868539, 5.49839243033073, 0.981479716763867, 9.21962967806868, 0) [20261030_072017.]: Entered 'hyperbolic_regression'-Function [20261030_072017.]: 'hyperbolic_regression': minmax = FALSE [20261030_072017.]: Entered 'cubic_regression'-Function [20261030_072017.]: 'cubic_regression': minmax = FALSE [20261030_072017.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068204507635, 9.27535128598895, 25.4762622748802, 34.0122077013407, 51.7842657248288, 64.6732313170861, 78.4326979275666, 81.342723302295, 100)c(2.80068204507635, 3.22464871401105, 0.476262274880199, 3.4877922986593, 1.7842657248288, 2.17323131708611, 3.43269792756659, 6.157276697705, 0)c(NA, 25.7971897120884, 1.9050490995208, 9.30077946309147, 3.5685314496576, 3.47717010733777, 4.57693057008879, 7.03688765452, 0) [20261030_072017.]: Logging df_agg: CpG#8 [20261030_072017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072017.]: c(2.80068204507635, 9.27535128598895, 25.4762622748802, 34.0122077013407, 51.7842657248288, 64.6732313170861, 78.4326979275666, 81.342723302295, 100)[20261030_072017.]: c(2.80068204507635, 3.22464871401105, 0.476262274880199, 3.4877922986593, 1.7842657248288, 2.17323131708611, 3.43269792756659, 6.157276697705, 0)[20261030_072017.]: c(NA, 25.7971897120884, 1.9050490995208, 9.30077946309147, 3.5685314496576, 3.47717010733777, 4.57693057008879, 7.03688765452, 0) [20261030_072017.]: Entered 'hyperbolic_regression'-Function [20261030_072017.]: 'hyperbolic_regression': minmax = FALSE [20261030_072017.]: Entered 'cubic_regression'-Function [20261030_072017.]: 'cubic_regression': minmax = FALSE [20261030_072017.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082189016604, 26.916456534699, 36.8334777985113, 52.0097895637612, 64.8930528019551, 74.5671055762352, 84.5294955118961, 100)c(0, 1.9917810983396, 1.916456534699, 0.666522201488696, 2.0097895637612, 2.39305280195509, 0.432894423764793, 2.9705044881039, 0)c(NA, 15.9342487867168, 7.66582613879601, 1.77739253730319, 4.01957912752241, 3.82888448312815, 0.577192565019724, 3.39486227211874, 0) [20261030_072017.]: Logging df_agg: CpG#9 [20261030_072017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072017.]: c(0, 10.5082189016604, 26.916456534699, 36.8334777985113, 52.0097895637612, 64.8930528019551, 74.5671055762352, 84.5294955118961, 100)[20261030_072017.]: c(0, 1.9917810983396, 1.916456534699, 0.666522201488696, 2.0097895637612, 2.39305280195509, 0.432894423764793, 2.9705044881039, 0)[20261030_072017.]: c(NA, 15.9342487867168, 7.66582613879601, 1.77739253730319, 4.01957912752241, 3.82888448312815, 0.577192565019724, 3.39486227211874, 0) [20261030_072017.]: Entered 'hyperbolic_regression'-Function [20261030_072017.]: 'hyperbolic_regression': minmax = FALSE [20261030_072018.]: Entered 'cubic_regression'-Function [20261030_072018.]: 'cubic_regression': minmax = FALSE [20261030_072018.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941118544918, 11.0412410207034, 25.4081504806604, 36.5243723372689, 50.7348828589515, 65.3135212954033, 75.534271088471, 83.2411228932089, 100)c(0.290941118544918, 1.4587589792966, 0.408150480660399, 0.9756276627311, 0.734882858951501, 2.81352129540331, 0.534271088471002, 4.2588771067911, 0)c(NA, 11.6700718343728, 1.6326019226416, 2.60167376728293, 1.469765717903, 4.50163407264529, 0.712361451294669, 4.86728812204698, 0) [20261030_072018.]: Logging df_agg: row_means [20261030_072018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072018.]: c(0.290941118544918, 11.0412410207034, 25.4081504806604, 36.5243723372689, 50.7348828589515, 65.3135212954033, 75.534271088471, 83.2411228932089, 100)[20261030_072018.]: c(0.290941118544918, 1.4587589792966, 0.408150480660399, 0.9756276627311, 0.734882858951501, 2.81352129540331, 0.534271088471002, 4.2588771067911, 0)[20261030_072018.]: c(NA, 11.6700718343728, 1.6326019226416, 2.60167376728293, 1.469765717903, 4.50163407264529, 0.712361451294669, 4.86728812204698, 0) [20261030_072018.]: Entered 'hyperbolic_regression'-Function [20261030_072018.]: 'hyperbolic_regression': minmax = FALSE [20261030_072018.]: Entered 'cubic_regression'-Function [20261030_072018.]: 'cubic_regression': minmax = FALSE [20261030_072018.]: ### Starting with plotting ### [20261030_072018.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_h.png [20261030_072018.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.852207768743 , a = 985.268160170173 , b = -657.852207768743 , d = 903.454792358803 [20261030_072018.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463776838884e-06 , b = -0.00180592143144771 , c = 1.11404022893582 , d = -0.858599967693894 [20261030_072019.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_h.png [20261030_072019.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63276280165 , a = 30277.0392662436 , b = 3399.63276280165 , d = 30353.5003020625 [20261030_072019.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097550980838e-05 , b = 0.00383753289616697 , c = 0.851607747023709 , d = 0.957127604620066 [20261030_072020.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_h.png [20261030_072020.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.758021885367 , a = 1333.6193913174 , b = -945.758021885367 , d = 1252.95818153845 [20261030_072020.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948607580961e-05 , b = 0.000937180156222013 , c = 0.998630629350923 , d = -0.383337807957439 [20261030_072021.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_h.png [20261030_072021.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.052028859475 , a = 1637.2665120021 , b = -754.052028859475 , d = 1558.27223127118 [20261030_072021.]: # CpG-site: CpG#4 Cubic: Using a = -3.257805702966e-06 , b = -0.000129595260947781 , c = 1.03077244428543 , d = -0.370343878907492 [20261030_072022.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_h.png [20261030_072022.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44339998382 , a = -5621.45928800605 , b = -1917.44339998382 , d = -5734.47881660132 [20261030_072022.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782829663942e-05 , b = 0.00493527176236547 , c = 0.800194609540016 , d = 1.38653013056492 [20261030_072023.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_h.png [20261030_072023.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66252469774 , a = 13775.6357224863 , b = -1255.66252469774 , d = 13690.5251295029 [20261030_072023.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201662863092e-06 , b = 0.000482210662087885 , c = 0.985293133184742 , d = 0.0293569619495391 [20261030_072024.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_h.png [20261030_072024.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05385324919 , a = 722.083713545107 , b = -1065.05385324919 , d = 634.401798379138 [20261030_072024.]: # CpG-site: CpG#7 Cubic: Using a = -1.04112502699485e-06 , b = -0.0013015002479837 , c = 1.12399515639242 , d = -1.598590187871 [20261030_072025.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_h.png [20261030_072025.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5565140161051 , a = 4784.9059803446 , b = 68.5565140161051 , d = 4739.11890818949 [20261030_072025.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730494370232e-05 , b = 0.00676525549313422 , c = 0.747405758416739 , d = 1.52337142614912 [20261030_072026.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_h.png [20261030_072026.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70194302753 , a = 1273.73019297548 , b = -1022.70194302753 , d = 1181.86476254991 [20261030_072026.]: # CpG-site: CpG#9 Cubic: Using a = 4.24789814190347e-06 , b = -0.00144169594189664 , c = 1.09837789981588 , d = -0.978988028018603 [20261030_072027.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_h.png [20261030_072027.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.7373358315 , a = 1740.63817173598 , b = -1166.7373358315 , d = 1653.86106544587 [20261030_072027.]: # CpG-site: row_means Cubic: Using a = -8.71186846250866e-06 , b = 0.0007155537913097 , c = 1.00248250357172 , d = -0.420553729592525 [20261030_072028.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_h.png [20261030_072029.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_h.png [20261030_072029.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_h.png [20261030_072030.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_h.png [20261030_072031.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_h.png [20261030_072032.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_h.png [20261030_072033.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_h.png [20261030_072034.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_h.png [20261030_072034.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_h.png [20261030_072035.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_h.png [20261030_072036.]: Entered 'solving_equations'-Function [20261030_072036.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067341Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400674Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329948Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299661Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.9393299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.3373299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.5773299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#3 Coefficients: -0.902947811447812Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144781Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447811Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949513Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828285Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461269Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946127Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572396Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723906Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545455Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212122Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185184Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865332Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: Solving cubic regression for row_means Coefficients: -0.771215488215503Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747476Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548822Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363637Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072036.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20261030_072036.]: ### Starting with regression calculations ### [20261030_072036.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072037.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072037.]: Logging df_agg: CpG#1 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)[20261030_072037.]: c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)[20261030_072037.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072037.]: Entered 'cubic_regression'-Function [20261030_072037.]: 'cubic_regression': minmax = FALSE [20261030_072037.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072037.]: Logging df_agg: CpG#2 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)[20261030_072037.]: c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)[20261030_072037.]: c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072037.]: Entered 'cubic_regression'-Function [20261030_072037.]: 'cubic_regression': minmax = FALSE [20261030_072037.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072037.]: Logging df_agg: CpG#3 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)[20261030_072037.]: c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)[20261030_072037.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072037.]: Entered 'cubic_regression'-Function [20261030_072037.]: 'cubic_regression': minmax = FALSE [20261030_072037.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072037.]: Logging df_agg: CpG#4 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)[20261030_072037.]: c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)[20261030_072037.]: c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072037.]: Entered 'cubic_regression'-Function [20261030_072037.]: 'cubic_regression': minmax = FALSE [20261030_072037.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072037.]: Logging df_agg: CpG#5 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20261030_072037.]: c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20261030_072037.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072037.]: Entered 'cubic_regression'-Function [20261030_072037.]: 'cubic_regression': minmax = FALSE [20261030_072037.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072037.]: Logging df_agg: CpG#6 [20261030_072037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072037.]: c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20261030_072037.]: c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20261030_072037.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072037.]: Entered 'hyperbolic_regression'-Function [20261030_072037.]: 'hyperbolic_regression': minmax = FALSE [20261030_072038.]: Entered 'cubic_regression'-Function [20261030_072038.]: 'cubic_regression': minmax = FALSE [20261030_072038.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072038.]: Logging df_agg: CpG#7 [20261030_072038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072038.]: c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20261030_072038.]: c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20261030_072038.]: c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072038.]: Entered 'hyperbolic_regression'-Function [20261030_072038.]: 'hyperbolic_regression': minmax = FALSE [20261030_072038.]: Entered 'cubic_regression'-Function [20261030_072038.]: 'cubic_regression': minmax = FALSE [20261030_072038.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072038.]: Logging df_agg: CpG#8 [20261030_072038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072038.]: c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20261030_072038.]: c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20261030_072038.]: c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072038.]: Entered 'hyperbolic_regression'-Function [20261030_072038.]: 'hyperbolic_regression': minmax = FALSE [20261030_072038.]: Entered 'cubic_regression'-Function [20261030_072038.]: 'cubic_regression': minmax = FALSE [20261030_072038.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072038.]: Logging df_agg: CpG#9 [20261030_072038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072038.]: c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)[20261030_072038.]: c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)[20261030_072038.]: c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072038.]: Entered 'hyperbolic_regression'-Function [20261030_072038.]: 'hyperbolic_regression': minmax = FALSE [20261030_072038.]: Entered 'cubic_regression'-Function [20261030_072038.]: 'cubic_regression': minmax = FALSE [20261030_072038.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072038.]: Logging df_agg: row_means [20261030_072038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072038.]: c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20261030_072038.]: c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20261030_072038.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072038.]: Entered 'hyperbolic_regression'-Function [20261030_072038.]: 'hyperbolic_regression': minmax = FALSE [20261030_072038.]: Entered 'cubic_regression'-Function [20261030_072038.]: 'cubic_regression': minmax = FALSE [20261030_072039.]: ### Starting with plotting ### [20261030_072039.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_c.png [20261030_072039.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77645761828 , a = 2039.49228856999 , b = -1164.77645761828 , d = 1955.8293332672 [20261030_072039.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240359e-05 , b = 0.00462173155013787 , c = 0.850326258865831 , d = 0.500073874211072 [20261030_072040.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_c.png [20261030_072040.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.134101093299 , a = 4988.86726167724 , b = -464.134101093299 , d = 4925.34026404526 [20261030_072040.]: # CpG-site: CpG#2 Cubic: Using a = -1.4081179690632e-05 , b = 0.00190849431754492 , c = 0.933455870941473 , d = 0.363096210099609 [20261030_072041.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_c.png [20261030_072041.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.612011473723 , a = 2026.53566343764 , b = -745.612011473723 , d = 1953.28538860797 [20261030_072041.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857189e-05 , b = 0.00366863707889094 , c = 0.880665171220891 , d = 0.51077930883367 [20261030_072042.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_c.png [20261030_072042.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.069598408899 , a = 2871.14474066332 , b = -833.069598408899 , d = 2796.81957244883 [20261030_072042.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643008e-05 , b = 0.00309017851940703 , c = 0.897073107777642 , d = 0.443455761841832 [20261030_072042.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_c.png [20261030_072042.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.134750453522 , a = 12477.6019723069 , b = 222.134750453522 , d = 12432.2700020072 [20261030_072042.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185614 [20261030_072043.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_c.png [20261030_072043.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.435206833527 , a = 21300.0759776074 , b = -700.435206833527 , d = 21235.4833216435 [20261030_072043.]: # CpG-site: CpG#6 Cubic: Using a = -5.83833515137487e-06 , b = 0.000828447163218502 , c = 0.970047764475167 , d = 0.15807470141223 [20261030_072044.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_c.png [20261030_072044.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91093816872 , a = 1477.91230553123 , b = -1144.91093816872 , d = 1394.7007509676 [20261030_072044.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614046e-05 , b = 0.00607993130565127 , c = 0.805411493452391 , d = 0.615125382790208 [20261030_072045.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_c.png [20261030_072045.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.370295463955 , a = 2377.38585028793 , b = -349.370295463955 , d = 2315.21326131442 [20261030_072045.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669641e-05 , b = 0.00353621593528019 , c = 0.877916844709072 , d = 0.698684606493051 [20261030_072046.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_c.png [20261030_072046.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91376776616 , a = 3745.1768102209 , b = -1536.91376776616 , d = 3656.26868706678 [20261030_072046.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.0037847824718234 , c = 0.871825263051794 , d = 0.455475740974124 [20261030_072047.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_c.png [20261030_072047.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.645911964404 , a = 3198.4680697565 , b = -820.645911964404 , d = 3124.2127020333 [20261030_072047.]: # CpG-site: row_means Cubic: Using a = -2.34328362100012e-05 , b = 0.00319139073176272 , c = 0.891842168032126 , d = 0.493517689787157 [20261030_072048.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_c.png [20261030_072049.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_c.png [20261030_072050.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_c.png [20261030_072051.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_c.png [20261030_072051.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_c.png [20261030_072052.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_c.png [20261030_072053.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_c.png [20261030_072053.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_c.png [20261030_072054.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_c.png [20261030_072055.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_c.png [20261030_072056.]: Entered 'solving_equations'-Function [20261030_072056.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673455297126 [20261030_072056.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900185727105 [20261030_072056.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415076417 [20261030_072056.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652088291919 [20261030_072056.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007137229186 [20261030_072056.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059603670892 [20261030_072056.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837970188565 [20261030_072056.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.503424453908 [20261030_072056.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987903023091 [20261030_072056.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512696865248 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20261030_072056.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930357167 [20261030_072056.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924275513818 [20261030_072056.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882457945 [20261030_072056.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337907553949 [20261030_072056.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020488186713 [20261030_072056.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480265004217 [20261030_072056.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133087218399 [20261030_072056.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745106748 [20261030_072056.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715364359 [20261030_072056.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579803881893 [20261030_072056.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20261030_072056.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254703304 [20261030_072056.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844382103408 [20261030_072056.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520192064625 [20261030_072056.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.832518120942 [20261030_072056.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519246009465 [20261030_072056.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316092289 [20261030_072056.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820490978 [20261030_072056.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.094492365799 [20261030_072056.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382701505 [20261030_072056.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66648251669523 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20261030_072056.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495280913975 [20261030_072056.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568552315137 [20261030_072056.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089841170169 [20261030_072056.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435239904998 [20261030_072056.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087038550451 [20261030_072056.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830448687375 [20261030_072056.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.13377680023 [20261030_072056.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.832109762613 [20261030_072056.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189967171 [20261030_072056.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42231993070205 [20261030_072056.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20261030_072056.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914988746 [20261030_072056.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206936824248 [20261030_072056.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738766304811 [20261030_072056.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324387415021 [20261030_072056.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373718329541 [20261030_072056.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115956598 [20261030_072056.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417916780309 [20261030_072056.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945076164571 [20261030_072056.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214379832 [20261030_072056.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.3464752923827 [20261030_072056.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20261030_072056.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593643225 [20261030_072056.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459673476 [20261030_072056.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474394183031 [20261030_072056.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517686545 [20261030_072056.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856054945892 [20261030_072056.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901595801491 [20261030_072056.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309264226971 [20261030_072056.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560574992 [20261030_072056.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831307439 [20261030_072056.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516204343295 [20261030_072056.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20261030_072056.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565595532014 [20261030_072056.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4887393500642 [20261030_072056.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712263452975 [20261030_072056.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142678235973 [20261030_072056.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212226118607 [20261030_072056.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485965769 [20261030_072056.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095134226302 [20261030_072056.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755075689401 [20261030_072056.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628732137106 [20261030_072056.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16456607780584 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20261030_072056.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605866618 [20261030_072056.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250957328233 [20261030_072056.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068389489785 [20261030_072056.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637439700193 [20261030_072056.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464440766366 [20261030_072056.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608206657 [20261030_072056.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571438252356 [20261030_072056.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759627311807 [20261030_072056.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671466425 [20261030_072056.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044914293851 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20261030_072056.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416678318344 [20261030_072056.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441664360729 [20261030_072056.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704619901374 [20261030_072056.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986713088 [20261030_072056.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231183894712 [20261030_072056.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284778119012 [20261030_072056.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653202318814 [20261030_072056.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631220864 [20261030_072056.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364755272 [20261030_072056.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610122937072 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20261030_072056.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692798955755 [20261030_072056.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620044419709 [20261030_072056.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175086401 [20261030_072056.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972049244462 [20261030_072056.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704058335196 [20261030_072056.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102594678068 [20261030_072056.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228661171954 [20261030_072056.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105102451192 [20261030_072056.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551574832 [20261030_072056.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237019907344009 [20261030_072056.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20261030_072057.]: on_start: using future::plan("sequential") [20261030_072057.]: Entered 'clean_dt'-Function [20261030_072057.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072057.]: got experimental data [20261030_072057.]: Entered 'clean_dt'-Function [20261030_072057.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072057.]: got calibration data [20261030_072057.]: ### Starting with regression calculations ### [20261030_072057.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072057.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072057.]: Logging df_agg: CpG#1 [20261030_072057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072057.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_072057.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_072057.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_072057.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072057.]: Entered 'hyperbolic_regression'-Function [20261030_072057.]: 'hyperbolic_regression': minmax = FALSE [20261030_072057.]: Entered 'cubic_regression'-Function [20261030_072057.]: 'cubic_regression': minmax = FALSE [20261030_072057.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072057.]: Logging df_agg: CpG#2 [20261030_072057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072057.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_072057.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_072057.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_072057.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072057.]: Entered 'hyperbolic_regression'-Function [20261030_072057.]: 'hyperbolic_regression': minmax = FALSE [20261030_072057.]: Entered 'cubic_regression'-Function [20261030_072057.]: 'cubic_regression': minmax = FALSE [20261030_072057.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072057.]: Logging df_agg: CpG#3 [20261030_072057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072057.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_072057.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_072057.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_072057.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072057.]: Entered 'hyperbolic_regression'-Function [20261030_072057.]: 'hyperbolic_regression': minmax = FALSE [20261030_072058.]: Entered 'cubic_regression'-Function [20261030_072058.]: 'cubic_regression': minmax = FALSE [20261030_072058.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072058.]: Logging df_agg: CpG#4 [20261030_072058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072058.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_072058.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_072058.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_072058.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072058.]: Entered 'hyperbolic_regression'-Function [20261030_072058.]: 'hyperbolic_regression': minmax = FALSE [20261030_072058.]: Entered 'cubic_regression'-Function [20261030_072058.]: 'cubic_regression': minmax = FALSE [20261030_072058.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072058.]: Logging df_agg: CpG#5 [20261030_072058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072058.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_072058.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_072058.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_072058.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072058.]: Entered 'hyperbolic_regression'-Function [20261030_072058.]: 'hyperbolic_regression': minmax = FALSE [20261030_072058.]: Entered 'cubic_regression'-Function [20261030_072058.]: 'cubic_regression': minmax = FALSE [20261030_072058.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072058.]: Logging df_agg: CpG#6 [20261030_072058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072058.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_072058.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_072058.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_072058.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072058.]: Entered 'hyperbolic_regression'-Function [20261030_072058.]: 'hyperbolic_regression': minmax = FALSE [20261030_072058.]: Entered 'cubic_regression'-Function [20261030_072058.]: 'cubic_regression': minmax = FALSE [20261030_072058.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072058.]: Logging df_agg: CpG#7 [20261030_072058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072058.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_072058.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_072058.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_072058.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072058.]: Entered 'hyperbolic_regression'-Function [20261030_072058.]: 'hyperbolic_regression': minmax = FALSE [20261030_072058.]: Entered 'cubic_regression'-Function [20261030_072058.]: 'cubic_regression': minmax = FALSE [20261030_072058.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072058.]: Logging df_agg: CpG#8 [20261030_072058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072058.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_072058.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_072058.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_072058.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072058.]: Entered 'hyperbolic_regression'-Function [20261030_072058.]: 'hyperbolic_regression': minmax = FALSE [20261030_072059.]: Entered 'cubic_regression'-Function [20261030_072059.]: 'cubic_regression': minmax = FALSE [20261030_072059.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072059.]: Logging df_agg: CpG#9 [20261030_072059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072059.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_072059.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_072059.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_072059.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072059.]: Entered 'hyperbolic_regression'-Function [20261030_072059.]: 'hyperbolic_regression': minmax = FALSE [20261030_072059.]: Entered 'cubic_regression'-Function [20261030_072059.]: 'cubic_regression': minmax = FALSE [20261030_072059.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072059.]: Logging df_agg: row_means [20261030_072059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072059.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_072059.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20261030_072059.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_072059.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072059.]: Entered 'hyperbolic_regression'-Function [20261030_072059.]: 'hyperbolic_regression': minmax = FALSE [20261030_072059.]: Entered 'cubic_regression'-Function [20261030_072059.]: 'cubic_regression': minmax = FALSE [20261030_072100.]: ### Starting with plotting ### [20261030_072100.]: Creating plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1.png [20261030_072100.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.718359366829 , a = -108.568181725073 , b = -937.718359366829 , d = -232.057347970568 [20261030_072100.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120089e-05 , b = -0.00558069687349687 , c = 0.784061853455187 , d = 1.93182659932659 [20261030_072101.]: Creating plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2.png [20261030_072101.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14256405691 , a = -141.862249482691 , b = -2393.14256405691 , d = -268.331566791619 [20261030_072101.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904603e-06 , b = 0.00293158941798941 , c = 0.514821742825076 , d = 9.27667003367005 [20261030_072102.]: Creating plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3.png [20261030_072102.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46856559994 , a = -170.424349024266 , b = -1477.46856559994 , d = -327.189953349656 [20261030_072102.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409347 , c = 0.627831292287959 , d = 3.88705218855219 [20261030_072103.]: Creating plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4.png [20261030_072103.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87813540635 , a = -162.169938624215 , b = -2115.87813540635 , d = -299.662470220131 [20261030_072103.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598365 , d = 6.17055050505049 [20261030_072104.]: Creating plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5.png [20261030_072104.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734581106 , a = -151.440080633905 , b = -1383.32734581106 , d = -304.200270746921 [20261030_072104.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193035e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053873 [20261030_072105.]: Creating plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6.png [20261030_072105.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59305223951 , a = -125.01614885031 , b = -1550.59305223951 , d = -247.279682181181 [20261030_072105.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037017 , c = 0.561022132435467 , d = 6.1479276094276 [20261030_072106.]: Creating plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7.png [20261030_072106.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843309025212 , a = -75.272668455603 , b = -772.843309025212 , d = -226.673431945785 [20261030_072106.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050506e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454545 [20261030_072107.]: Creating plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8.png [20261030_072107.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73674225058 , a = -278.01736246951 , b = -3872.73674225058 , d = -438.89437107864 [20261030_072107.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207631e-05 , b = 0.00447978143338143 , c = 0.534897737694405 , d = 9.56981481481482 [20261030_072108.]: Creating plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9.png [20261030_072108.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925306074442 , a = -70.6796313463885 , b = -843.925306074442 , d = -184.482177790954 [20261030_072108.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487094e-05 , b = -0.00542281173641175 , c = 0.67899229597563 , d = 2.72413468013467 [20261030_072109.]: Creating plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans.png [20261030_072109.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75753208775 , a = -127.675078347398 , b = -1525.75753208775 , d = -263.003101448007 [20261030_072109.]: # CpG-site: row_means Cubic: Using a = 2.88923726150394e-05 , b = -0.00062995927529263 , c = 0.600117857944526 , d = 5.17789562289561 [20261030_072110.]: Entered 'solving_equations'-Function [20261030_072110.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220973445083 [20261030_072110.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698560311466 [20261030_072110.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781906073847 [20261030_072110.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730368648153 [20261030_072110.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349259403572 [20261030_072110.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582659628182 [20261030_072110.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090698286744 [20261030_072110.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271844354527 [20261030_072110.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400896725463 [20261030_072110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20261030_072110.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660452845563 [20261030_072110.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129695239176 [20261030_072110.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740007313973 [20261030_072110.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050452534769 [20261030_072110.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855007657984 [20261030_072110.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440497926442 [20261030_072110.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644671142073 [20261030_072110.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054159052776 [20261030_072110.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272027945 [20261030_072110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512351365312449 [20261030_072111.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523860241582 [20261030_072111.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218913541827 [20261030_072111.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270482487648 [20261030_072111.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909273265796 [20261030_072111.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686343029596 [20261030_072111.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524202068927 [20261030_072111.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374626977192 [20261030_072111.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704021256215 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519495725301386 [20261030_072111.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934179859301 [20261030_072111.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685424163245 [20261030_072111.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817111793916 [20261030_072111.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843157271579 [20261030_072111.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722381135589 [20261030_072111.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549267316565 [20261030_072111.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821802056848 [20261030_072111.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079132344567 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558694357453 [20261030_072111.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649679376148 [20261030_072111.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821562069 [20261030_072111.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773618886979 [20261030_072111.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659384364475 [20261030_072111.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342271231358 [20261030_072111.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915259020048 [20261030_072111.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887511867 [20261030_072111.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760141434 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164060232507 [20261030_072111.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635560799499 [20261030_072111.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107450301415 [20261030_072111.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.320507327622 [20261030_072111.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767214785 [20261030_072111.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602943815022 [20261030_072111.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689056648838 [20261030_072111.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086409488 [20261030_072111.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637059463 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235571135334 [20261030_072111.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993291616594 [20261030_072111.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951804645082 [20261030_072111.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310368035118 [20261030_072111.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588652148436 [20261030_072111.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364868488799 [20261030_072111.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361043390111 [20261030_072111.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328202345848 [20261030_072111.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004250078195 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068311785576 [20261030_072111.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535186997198 [20261030_072111.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762619392848 [20261030_072111.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122070556743 [20261030_072111.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842648198367 [20261030_072111.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732305504944 [20261030_072111.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326976116343 [20261030_072111.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427231217478 [20261030_072111.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964407595969 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403344998222 [20261030_072111.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082209182036 [20261030_072111.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164564257264 [20261030_072111.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334768343792 [20261030_072111.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879174528 [20261030_072111.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930511712503 [20261030_072111.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671043162452 [20261030_072111.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949554973 [20261030_072111.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147796224 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290943886363803 [20261030_072111.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412420881831 [20261030_072111.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081498359231 [20261030_072111.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243708074472 [20261030_072111.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348807563485 [20261030_072111.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135192566817 [20261030_072111.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342694879881 [20261030_072111.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411218376464 [20261030_072111.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666943394801 [20261030_072111.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20261030_072111.]: ### Starting with regression calculations ### [20261030_072111.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072111.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698560311466, 24.4781906073847, 38.1730368648153, 52.3349259403572, 65.4582659628182, 75.0090698286744, 81.5271844354527, 100)c(0, 0.330143968853401, 0.5218093926153, 0.673036864815302, 2.3349259403572, 2.9582659628182, 0.00906982867439865, 5.9728155645473, 0)c(NA, 2.64115175082721, 2.0872375704612, 1.7947649728408, 4.6698518807144, 4.73322554050912, 0.0120931048991982, 6.8260749309112, 0) [20261030_072111.]: Logging df_agg: CpG#1 [20261030_072111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072111.]: c(0, 12.1698560311466, 24.4781906073847, 38.1730368648153, 52.3349259403572, 65.4582659628182, 75.0090698286744, 81.5271844354527, 100)[20261030_072111.]: c(0, 0.330143968853401, 0.5218093926153, 0.673036864815302, 2.3349259403572, 2.9582659628182, 0.00906982867439865, 5.9728155645473, 0)[20261030_072111.]: c(NA, 2.64115175082721, 2.0872375704612, 1.7947649728408, 4.6698518807144, 4.73322554050912, 0.0120931048991982, 6.8260749309112, 0) [20261030_072111.]: Entered 'hyperbolic_regression'-Function [20261030_072111.]: 'hyperbolic_regression': minmax = FALSE [20261030_072111.]: Entered 'cubic_regression'-Function [20261030_072111.]: 'cubic_regression': minmax = FALSE [20261030_072111.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660452845563, 11.4129695239176, 26.1740007313973, 35.1050452534769, 47.6855007657984, 67.1440497926442, 75.7644671142073, 84.4054159052776, 100)c(1.13660452845563, 1.0870304760824, 1.1740007313973, 2.3949547465231, 2.3144992342016, 4.6440497926442, 0.764467114207307, 3.09458409472241, 0)c(NA, 8.6962438086592, 4.6960029255892, 6.38654599072826, 4.6289984684032, 7.43047966823071, 1.01928948560974, 3.53666753682561, 0) [20261030_072111.]: Logging df_agg: CpG#2 [20261030_072111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072111.]: c(1.13660452845563, 11.4129695239176, 26.1740007313973, 35.1050452534769, 47.6855007657984, 67.1440497926442, 75.7644671142073, 84.4054159052776, 100)[20261030_072111.]: c(1.13660452845563, 1.0870304760824, 1.1740007313973, 2.3949547465231, 2.3144992342016, 4.6440497926442, 0.764467114207307, 3.09458409472241, 0)[20261030_072111.]: c(NA, 8.6962438086592, 4.6960029255892, 6.38654599072826, 4.6289984684032, 7.43047966823071, 1.01928948560974, 3.53666753682561, 0) [20261030_072111.]: Entered 'hyperbolic_regression'-Function [20261030_072111.]: 'hyperbolic_regression': minmax = FALSE [20261030_072111.]: Entered 'cubic_regression'-Function [20261030_072111.]: 'cubic_regression': minmax = FALSE [20261030_072111.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512351365312449, 10.7523860241582, 25.5218913541827, 36.5270482487648, 50.7909273265796, 64.8686343029596, 77.5524202068927, 80.4374626977192, 100)c(0.512351365312449, 1.7476139758418, 0.521891354182699, 0.9729517512352, 0.790927326579599, 2.36863430295961, 2.55242020689271, 7.0625373022808, 0)c(NA, 13.9809118067344, 2.0875654167308, 2.59453800329387, 1.5818546531592, 3.78981488473537, 3.40322694252361, 8.07147120260663, 0) [20261030_072111.]: Logging df_agg: CpG#3 [20261030_072111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072111.]: c(0.512351365312449, 10.7523860241582, 25.5218913541827, 36.5270482487648, 50.7909273265796, 64.8686343029596, 77.5524202068927, 80.4374626977192, 100)[20261030_072111.]: c(0.512351365312449, 1.7476139758418, 0.521891354182699, 0.9729517512352, 0.790927326579599, 2.36863430295961, 2.55242020689271, 7.0625373022808, 0)[20261030_072111.]: c(NA, 13.9809118067344, 2.0875654167308, 2.59453800329387, 1.5818546531592, 3.78981488473537, 3.40322694252361, 8.07147120260663, 0) [20261030_072111.]: Entered 'hyperbolic_regression'-Function [20261030_072111.]: 'hyperbolic_regression': minmax = FALSE [20261030_072112.]: Entered 'cubic_regression'-Function [20261030_072112.]: 'cubic_regression': minmax = FALSE [20261030_072112.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934179859301, 24.2685424163245, 38.0817111793916, 48.5843157271579, 67.6722381135589, 74.1549267316565, 82.8821802056848, 100)c(0, 0.00658201406990067, 0.7314575836755, 0.581711179391597, 1.4156842728421, 5.1722381135589, 0.845073268343498, 4.6178197943152, 0)c(NA, 0.0526561125592053, 2.925830334702, 1.55122981171093, 2.8313685456842, 8.27558098169425, 1.12676435779133, 5.27750833636022, 0) [20261030_072112.]: Logging df_agg: CpG#4 [20261030_072112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072112.]: c(0, 12.4934179859301, 24.2685424163245, 38.0817111793916, 48.5843157271579, 67.6722381135589, 74.1549267316565, 82.8821802056848, 100)[20261030_072112.]: c(0, 0.00658201406990067, 0.7314575836755, 0.581711179391597, 1.4156842728421, 5.1722381135589, 0.845073268343498, 4.6178197943152, 0)[20261030_072112.]: c(NA, 0.0526561125592053, 2.925830334702, 1.55122981171093, 2.8313685456842, 8.27558098169425, 1.12676435779133, 5.27750833636022, 0) [20261030_072112.]: Entered 'hyperbolic_regression'-Function [20261030_072112.]: 'hyperbolic_regression': minmax = FALSE [20261030_072112.]: Entered 'cubic_regression'-Function [20261030_072112.]: 'cubic_regression': minmax = FALSE [20261030_072112.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558694357453, 10.1649679376148, 23.9830821562069, 37.2773618886979, 50.8659384364475, 62.4342271231358, 76.3915259020048, 86.1597887511867, 100)c(2.41558694357453, 2.3350320623852, 1.0169178437931, 0.222638111302103, 0.8659384364475, 0.0657728768642016, 1.3915259020048, 1.3402112488133, 0)c(NA, 18.6802564990816, 4.0676713751724, 0.593701630138943, 1.731876872895, 0.105236602982723, 1.85536786933974, 1.53166999864378, 0) [20261030_072112.]: Logging df_agg: CpG#5 [20261030_072112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072112.]: c(2.41558694357453, 10.1649679376148, 23.9830821562069, 37.2773618886979, 50.8659384364475, 62.4342271231358, 76.3915259020048, 86.1597887511867, 100)[20261030_072112.]: c(2.41558694357453, 2.3350320623852, 1.0169178437931, 0.222638111302103, 0.8659384364475, 0.0657728768642016, 1.3915259020048, 1.3402112488133, 0)[20261030_072112.]: c(NA, 18.6802564990816, 4.0676713751724, 0.593701630138943, 1.731876872895, 0.105236602982723, 1.85536786933974, 1.53166999864378, 0) [20261030_072112.]: Entered 'hyperbolic_regression'-Function [20261030_072112.]: 'hyperbolic_regression': minmax = FALSE [20261030_072112.]: Entered 'cubic_regression'-Function [20261030_072112.]: 'cubic_regression': minmax = FALSE [20261030_072112.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164060232507, 11.8635560799499, 26.5107450301415, 35.320507327622, 50.0570767214785, 64.9602943815022, 73.6689056648838, 87.1266086409488, 100)c(0.138164060232507, 0.6364439200501, 1.5107450301415, 2.179492672378, 0.0570767214785022, 2.46029438150219, 1.33109433511621, 0.373391359051197, 0)c(NA, 5.0915513604008, 6.042980120566, 5.81198045967466, 0.114153442957004, 3.93647101040351, 1.77479244682161, 0.426732981772797, 0) [20261030_072112.]: Logging df_agg: CpG#6 [20261030_072112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072112.]: c(0.138164060232507, 11.8635560799499, 26.5107450301415, 35.320507327622, 50.0570767214785, 64.9602943815022, 73.6689056648838, 87.1266086409488, 100)[20261030_072112.]: c(0.138164060232507, 0.6364439200501, 1.5107450301415, 2.179492672378, 0.0570767214785022, 2.46029438150219, 1.33109433511621, 0.373391359051197, 0)[20261030_072112.]: c(NA, 5.0915513604008, 6.042980120566, 5.81198045967466, 0.114153442957004, 3.93647101040351, 1.77479244682161, 0.426732981772797, 0) [20261030_072112.]: Entered 'hyperbolic_regression'-Function [20261030_072112.]: 'hyperbolic_regression': minmax = FALSE [20261030_072112.]: Entered 'cubic_regression'-Function [20261030_072112.]: 'cubic_regression': minmax = FALSE [20261030_072112.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993291616594, 24.5951804645082, 37.8310368035118, 53.5588652148436, 65.9364868488799, 75.7361043390111, 79.4328202345848, 100)c(0, 2.3006708383406, 0.4048195354918, 0.3310368035118, 3.5588652148436, 3.43648684887989, 0.736104339011106, 8.0671797654152, 0)c(NA, 18.4053667067248, 1.6192781419672, 0.882764809364801, 7.1177304296872, 5.49837895820783, 0.981472452014808, 9.21963401761738, 0) [20261030_072112.]: Logging df_agg: CpG#7 [20261030_072112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072112.]: c(0, 10.1993291616594, 24.5951804645082, 37.8310368035118, 53.5588652148436, 65.9364868488799, 75.7361043390111, 79.4328202345848, 100)[20261030_072112.]: c(0, 2.3006708383406, 0.4048195354918, 0.3310368035118, 3.5588652148436, 3.43648684887989, 0.736104339011106, 8.0671797654152, 0)[20261030_072112.]: c(NA, 18.4053667067248, 1.6192781419672, 0.882764809364801, 7.1177304296872, 5.49837895820783, 0.981472452014808, 9.21963401761738, 0) [20261030_072112.]: Entered 'hyperbolic_regression'-Function [20261030_072112.]: 'hyperbolic_regression': minmax = FALSE [20261030_072113.]: Entered 'cubic_regression'-Function [20261030_072113.]: 'cubic_regression': minmax = FALSE [20261030_072113.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068311785576, 9.27535186997198, 25.4762619392848, 34.0122070556743, 51.7842648198367, 64.6732305504944, 78.4326976116343, 81.3427231217478, 100)c(2.80068311785576, 3.22464813002802, 0.4762619392848, 3.4877929443257, 1.7842648198367, 2.1732305504944, 3.4326976116343, 6.1572768782522, 0)c(NA, 25.7971850402242, 1.9050477571392, 9.30078118486853, 3.5685296396734, 3.47716888079103, 4.57693014884573, 7.03688786085966, 0) [20261030_072113.]: Logging df_agg: CpG#8 [20261030_072113.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072113.]: c(2.80068311785576, 9.27535186997198, 25.4762619392848, 34.0122070556743, 51.7842648198367, 64.6732305504944, 78.4326976116343, 81.3427231217478, 100)[20261030_072113.]: c(2.80068311785576, 3.22464813002802, 0.4762619392848, 3.4877929443257, 1.7842648198367, 2.1732305504944, 3.4326976116343, 6.1572768782522, 0)[20261030_072113.]: c(NA, 25.7971850402242, 1.9050477571392, 9.30078118486853, 3.5685296396734, 3.47716888079103, 4.57693014884573, 7.03688786085966, 0) [20261030_072113.]: Entered 'hyperbolic_regression'-Function [20261030_072113.]: 'hyperbolic_regression': minmax = FALSE [20261030_072113.]: Entered 'cubic_regression'-Function [20261030_072113.]: 'cubic_regression': minmax = FALSE [20261030_072113.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082209182036, 26.9164564257264, 36.8334768343792, 52.0097879174528, 64.8930511712503, 74.5671043162452, 84.5294949554973, 100)c(0, 1.9917790817964, 1.9164564257264, 0.666523165620802, 2.0097879174528, 2.39305117125031, 0.432895683754793, 2.9705050445027, 0)c(NA, 15.9342326543712, 7.66582570290559, 1.77739510832214, 4.0195758349056, 3.82888187400049, 0.577194245006391, 3.39486290800309, 0) [20261030_072113.]: Logging df_agg: CpG#9 [20261030_072113.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072113.]: c(0, 10.5082209182036, 26.9164564257264, 36.8334768343792, 52.0097879174528, 64.8930511712503, 74.5671043162452, 84.5294949554973, 100)[20261030_072113.]: c(0, 1.9917790817964, 1.9164564257264, 0.666523165620802, 2.0097879174528, 2.39305117125031, 0.432895683754793, 2.9705050445027, 0)[20261030_072113.]: c(NA, 15.9342326543712, 7.66582570290559, 1.77739510832214, 4.0195758349056, 3.82888187400049, 0.577194245006391, 3.39486290800309, 0) [20261030_072113.]: Entered 'hyperbolic_regression'-Function [20261030_072113.]: 'hyperbolic_regression': minmax = FALSE [20261030_072113.]: Entered 'cubic_regression'-Function [20261030_072113.]: 'cubic_regression': minmax = FALSE [20261030_072113.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290943886363803, 11.0412420881831, 25.4081498359231, 36.5243708074472, 50.7348807563485, 65.3135192566817, 75.5342694879881, 83.2411218376464, 100)c(0.290943886363803, 1.4587579118169, 0.408149835923101, 0.975629192552802, 0.734880756348502, 2.8135192566817, 0.534269487988098, 4.2588781623536, 0)c(NA, 11.6700632945352, 1.6325993436924, 2.60167784680747, 1.469761512697, 4.50163081069072, 0.712359317317464, 4.86728932840411, 0) [20261030_072113.]: Logging df_agg: row_means [20261030_072113.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072113.]: c(0.290943886363803, 11.0412420881831, 25.4081498359231, 36.5243708074472, 50.7348807563485, 65.3135192566817, 75.5342694879881, 83.2411218376464, 100)[20261030_072113.]: c(0.290943886363803, 1.4587579118169, 0.408149835923101, 0.975629192552802, 0.734880756348502, 2.8135192566817, 0.534269487988098, 4.2588781623536, 0)[20261030_072113.]: c(NA, 11.6700632945352, 1.6325993436924, 2.60167784680747, 1.469761512697, 4.50163081069072, 0.712359317317464, 4.86728932840411, 0) [20261030_072113.]: Entered 'hyperbolic_regression'-Function [20261030_072113.]: 'hyperbolic_regression': minmax = FALSE [20261030_072113.]: Entered 'cubic_regression'-Function [20261030_072113.]: 'cubic_regression': minmax = FALSE [20261030_072114.]: ### Starting with plotting ### [20261030_072114.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_h.png [20261030_072114.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.849499354383 , a = 985.272003673375 , b = -657.849499354383 , d = 903.458793163523 [20261030_072114.]: # CpG-site: CpG#1 Cubic: Using a = 5.18473671588004e-06 , b = -0.00180593192451926 , c = 1.11404027243426 , d = -0.858597515865115 [20261030_072115.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_h.png [20261030_072115.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.21608931834 , a = 30274.7410572818 , b = 3399.21608931834 , d = 30351.188023727 [20261030_072115.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097527367259e-05 , b = 0.00383753218106307 , c = 0.851607803474202 , d = 0.957126710581789 [20261030_072116.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_h.png [20261030_072116.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.762312241338 , a = 1333.6152376691 , b = -945.762312241338 , d = 1252.95390817594 [20261030_072116.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948467727018e-05 , b = 0.000937176015956389 , c = 0.998630955695524 , d = -0.38334312795835 [20261030_072116.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_h.png [20261030_072116.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050743548519 , a = 1637.26759645137 , b = -754.050743548519 , d = 1558.27335913207 [20261030_072116.]: # CpG-site: CpG#4 Cubic: Using a = -3.25778275344586e-06 , b = -0.000129597649257477 , c = 1.03077244882705 , d = -0.370342438636323 [20261030_072117.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_h.png [20261030_072117.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420379828 , a = -5621.4648810151 , b = -1917.44420379828 , d = -5734.48443719672 [20261030_072117.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782841580003e-05 , b = 0.00493527221570615 , c = 0.800194569220636 , d = 1.38653087654308 [20261030_072118.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_h.png [20261030_072118.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.62966670338 , a = 13775.9700449233 , b = -1255.62966670338 , d = 13690.8602145159 [20261030_072118.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201708037984e-06 , b = 0.000482210805782809 , c = 0.98529312120376 , d = 0.0293571850501648 [20261030_072119.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_h.png [20261030_072119.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05146276508 , a = 722.085479976214 , b = -1065.05146276508 , d = 634.403653072071 [20261030_072119.]: # CpG-site: CpG#7 Cubic: Using a = -1.04102283090233e-06 , b = -0.00130151195401037 , c = 1.12399528049481 , d = -1.59858562009615 [20261030_072121.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_h.png [20261030_072121.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.6517691785076 , a = 4785.00215779765 , b = 68.6517691785076 , d = 4739.21711982324 [20261030_072121.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730532444813e-05 , b = 0.00676525661962835 , c = 0.747405672454852 , d = 1.52337263958405 [20261030_072122.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_h.png [20261030_072122.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70125664445 , a = 1273.73111111797 , b = -1022.70125664445 , d = 1181.86570516224 [20261030_072122.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791493833691e-06 , b = -0.00144169778111157 , c = 1.09837791254579 , d = -0.978987335731711 [20261030_072123.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_h.png [20261030_072123.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73556122216 , a = 1740.64371737619 , b = -1166.73556122216 , d = 1653.86669200292 [20261030_072123.]: # CpG-site: row_means Cubic: Using a = -8.71187142908206e-06 , b = 0.000715555609534897 , c = 1.00248232298706 , d = -0.420550876295153 [20261030_072124.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_h.png [20261030_072125.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_h.png [20261030_072126.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_h.png [20261030_072126.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_h.png [20261030_072127.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_h.png [20261030_072128.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_h.png [20261030_072129.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_h.png [20261030_072129.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_h.png [20261030_072130.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_h.png [20261030_072131.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_h.png [20261030_072132.]: Entered 'solving_equations'-Function [20261030_072132.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067341Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400674Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329948Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299661Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.9393299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.3373299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.5773299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#3 Coefficients: -0.902947811447812Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144781Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447811Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949513Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828285Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461269Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946127Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572396Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723906Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545455Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212122Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185184Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865332Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: Solving cubic regression for row_means Coefficients: -0.771215488215503Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747476Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548822Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363637Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072132.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20261030_072132.]: ### Starting with regression calculations ### [20261030_072132.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072132.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072132.]: Logging df_agg: CpG#1 [20261030_072132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072132.]: c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)[20261030_072132.]: c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)[20261030_072132.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072132.]: Entered 'hyperbolic_regression'-Function [20261030_072132.]: 'hyperbolic_regression': minmax = FALSE [20261030_072132.]: Entered 'cubic_regression'-Function [20261030_072132.]: 'cubic_regression': minmax = FALSE [20261030_072132.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072132.]: Logging df_agg: CpG#2 [20261030_072132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072132.]: c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)[20261030_072132.]: c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)[20261030_072132.]: c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072132.]: Entered 'hyperbolic_regression'-Function [20261030_072132.]: 'hyperbolic_regression': minmax = FALSE [20261030_072133.]: Entered 'cubic_regression'-Function [20261030_072133.]: 'cubic_regression': minmax = FALSE [20261030_072133.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072133.]: Logging df_agg: CpG#3 [20261030_072133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072133.]: c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)[20261030_072133.]: c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)[20261030_072133.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072133.]: Entered 'hyperbolic_regression'-Function [20261030_072133.]: 'hyperbolic_regression': minmax = FALSE [20261030_072133.]: Entered 'cubic_regression'-Function [20261030_072133.]: 'cubic_regression': minmax = FALSE [20261030_072133.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072133.]: Logging df_agg: CpG#4 [20261030_072133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072133.]: c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)[20261030_072133.]: c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)[20261030_072133.]: c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072133.]: Entered 'hyperbolic_regression'-Function [20261030_072133.]: 'hyperbolic_regression': minmax = FALSE [20261030_072133.]: Entered 'cubic_regression'-Function [20261030_072133.]: 'cubic_regression': minmax = FALSE [20261030_072133.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072133.]: Logging df_agg: CpG#5 [20261030_072133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072133.]: c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20261030_072133.]: c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20261030_072133.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072133.]: Entered 'hyperbolic_regression'-Function [20261030_072133.]: 'hyperbolic_regression': minmax = FALSE [20261030_072133.]: Entered 'cubic_regression'-Function [20261030_072133.]: 'cubic_regression': minmax = FALSE [20261030_072133.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072133.]: Logging df_agg: CpG#6 [20261030_072133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072133.]: c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20261030_072133.]: c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20261030_072133.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072133.]: Entered 'hyperbolic_regression'-Function [20261030_072133.]: 'hyperbolic_regression': minmax = FALSE [20261030_072133.]: Entered 'cubic_regression'-Function [20261030_072133.]: 'cubic_regression': minmax = FALSE [20261030_072133.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072133.]: Logging df_agg: CpG#7 [20261030_072133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072133.]: c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20261030_072133.]: c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20261030_072133.]: c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072133.]: Entered 'hyperbolic_regression'-Function [20261030_072133.]: 'hyperbolic_regression': minmax = FALSE [20261030_072134.]: Entered 'cubic_regression'-Function [20261030_072134.]: 'cubic_regression': minmax = FALSE [20261030_072134.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072134.]: Logging df_agg: CpG#8 [20261030_072134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072134.]: c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20261030_072134.]: c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20261030_072134.]: c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072134.]: Entered 'hyperbolic_regression'-Function [20261030_072134.]: 'hyperbolic_regression': minmax = FALSE [20261030_072134.]: Entered 'cubic_regression'-Function [20261030_072134.]: 'cubic_regression': minmax = FALSE [20261030_072134.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072134.]: Logging df_agg: CpG#9 [20261030_072134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072134.]: c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)[20261030_072134.]: c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)[20261030_072134.]: c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072134.]: Entered 'hyperbolic_regression'-Function [20261030_072134.]: 'hyperbolic_regression': minmax = FALSE [20261030_072134.]: Entered 'cubic_regression'-Function [20261030_072134.]: 'cubic_regression': minmax = FALSE [20261030_072134.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072134.]: Logging df_agg: row_means [20261030_072134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072134.]: c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20261030_072134.]: c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20261030_072134.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072134.]: Entered 'hyperbolic_regression'-Function [20261030_072134.]: 'hyperbolic_regression': minmax = FALSE [20261030_072134.]: Entered 'cubic_regression'-Function [20261030_072134.]: 'cubic_regression': minmax = FALSE [20261030_072135.]: ### Starting with plotting ### [20261030_072135.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_c.png [20261030_072135.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77422189076 , a = 2039.49572658302 , b = -1164.77422189076 , d = 1955.83282278618 [20261030_072135.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240359e-05 , b = 0.00462173155013787 , c = 0.850326258865831 , d = 0.500073874211072 [20261030_072136.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_c.png [20261030_072136.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.164138187358 , a = 4988.78288638834 , b = -464.164138187358 , d = 4925.25508757491 [20261030_072136.]: # CpG-site: CpG#2 Cubic: Using a = -1.4081179690632e-05 , b = 0.00190849431754492 , c = 0.933455870941473 , d = 0.363096210099609 [20261030_072137.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_c.png [20261030_072137.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.607546083625 , a = 2026.53508964034 , b = -745.607546083625 , d = 1953.28487394041 [20261030_072137.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857189e-05 , b = 0.00366863707889094 , c = 0.880665171220891 , d = 0.51077930883367 [20261030_072138.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_c.png [20261030_072138.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077393890846 , a = 2871.15181306048 , b = -833.077393890846 , d = 2796.82656320973 [20261030_072138.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643008e-05 , b = 0.00309017851940703 , c = 0.897073107777642 , d = 0.443455761841832 [20261030_072139.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_c.png [20261030_072139.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.115129368985 , a = 12477.4384463151 , b = 222.115129368985 , d = 12432.1057933598 [20261030_072139.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185614 [20261030_072140.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_c.png [20261030_072140.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.45411411976 , a = 21297.6488466579 , b = -700.45411411976 , d = 21233.05323661 [20261030_072140.]: # CpG-site: CpG#6 Cubic: Using a = -5.83833515137487e-06 , b = 0.000828447163218502 , c = 0.970047764475167 , d = 0.15807470141223 [20261030_072141.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_c.png [20261030_072141.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91142995514 , a = 1477.91406501344 , b = -1144.91142995514 , d = 1394.70251752796 [20261030_072141.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614046e-05 , b = 0.00607993130565127 , c = 0.805411493452391 , d = 0.615125382790208 [20261030_072141.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_c.png [20261030_072141.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.382310123401 , a = 2377.37569349434 , b = -349.382310123401 , d = 2315.20283883756 [20261030_072141.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669641e-05 , b = 0.00353621593528019 , c = 0.877916844709072 , d = 0.698684606493051 [20261030_072142.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_c.png [20261030_072142.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.915730051 , a = 3745.17821866409 , b = -1536.915730051 , d = 3656.27007121528 [20261030_072142.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.0037847824718234 , c = 0.871825263051794 , d = 0.455475740974124 [20261030_072143.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_c.png [20261030_072143.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.65665683604 , a = 3198.4778737634 , b = -820.65665683604 , d = 3124.22239692677 [20261030_072143.]: # CpG-site: row_means Cubic: Using a = -2.34328362100012e-05 , b = 0.00319139073176272 , c = 0.891842168032126 , d = 0.493517689787157 [20261030_072144.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_c.png [20261030_072145.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_c.png [20261030_072146.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_c.png [20261030_072147.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_c.png [20261030_072147.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_c.png [20261030_072148.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_c.png [20261030_072149.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_c.png [20261030_072150.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_c.png [20261030_072150.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_c.png [20261030_072151.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_c.png [20261030_072152.]: Entered 'solving_equations'-Function [20261030_072152.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673374424142 [20261030_072152.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900141359632 [20261030_072152.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525325472316 [20261030_072152.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.46519737091 [20261030_072152.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072253061999 [20261030_072152.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059602539327 [20261030_072152.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837961445493 [20261030_072152.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034145133965 [20261030_072152.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987844213803 [20261030_072152.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510641065597 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20261030_072152.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227933139965 [20261030_072152.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924277975211 [20261030_072152.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810886900216 [20261030_072152.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337912583562 [20261030_072152.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020421921013 [20261030_072152.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480264494599 [20261030_072152.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085882423 [20261030_072152.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176749333691 [20261030_072152.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715763689 [20261030_072152.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579728622373 [20261030_072152.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20261030_072152.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349270862052 [20261030_072152.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390500238 [20261030_072152.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520215422552 [20261030_072152.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325208723584 [20261030_072152.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518943373017 [20261030_072152.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066318999583 [20261030_072152.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826757628 [20261030_072152.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944948392084 [20261030_072152.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.827938357774 [20261030_072152.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666487920112452 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20261030_072152.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495271359229 [20261030_072152.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568550383363 [20261030_072152.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089818123897 [20261030_072152.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435212953953 [20261030_072152.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087083902786 [20261030_072152.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830460052966 [20261030_072152.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337768320821 [20261030_072152.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.832107597467 [20261030_072152.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890199042091 [20261030_072152.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232730744852 [20261030_072152.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20261030_072152.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126912522862 [20261030_072152.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206936492738 [20261030_072152.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738763311591 [20261030_072152.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324383948115 [20261030_072152.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373722466588 [20261030_072152.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.873311642817 [20261030_072152.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417916551909 [20261030_072152.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945073304416 [20261030_072152.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214798146 [20261030_072152.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647598273334 [20261030_072152.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20261030_072152.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593212779 [20261030_072152.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459498577 [20261030_072152.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.84743935463 [20261030_072152.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.842351682284 [20261030_072152.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856068122106 [20261030_072152.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596328389 [20261030_072152.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309264065756 [20261030_072152.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148559911129 [20261030_072152.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831226957 [20261030_072152.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516224617614 [20261030_072152.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20261030_072152.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565551990919 [20261030_072152.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4887402758517 [20261030_072152.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712178951527 [20261030_072152.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142585042607 [20261030_072152.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212346554543 [20261030_072152.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797495945497 [20261030_072152.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095119196672 [20261030_072152.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754997492718 [20261030_072152.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628730439116 [20261030_072152.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645361969085 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20261030_072152.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696600543576 [20261030_072152.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250952999002 [20261030_072152.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068382777843 [20261030_072152.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637431106797 [20261030_072152.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644463470317 [20261030_072152.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607816935 [20261030_072152.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571430635872 [20261030_072152.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759618661333 [20261030_072152.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671484059 [20261030_072152.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.0504476585401 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20261030_072152.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669919584 [20261030_072152.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441663551446 [20261030_072152.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046184464655 [20261030_072152.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969663302 [20261030_072152.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231445649349 [20261030_072152.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284781380442 [20261030_072152.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.865319132481 [20261030_072152.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855617561181 [20261030_072152.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.015536242455 [20261030_072152.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609672638126 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20261030_072152.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692783889209 [20261030_072152.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620035246188 [20261030_072152.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026156614595 [20261030_072152.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972027689399 [20261030_072152.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704197170536 [20261030_072152.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102591917141 [20261030_072152.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228651683956 [20261030_072152.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105083594472 [20261030_072152.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143543798795 [20261030_072152.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237017046776281 [20261030_072152.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20261030_072153.]: on_start: using future::plan("sequential") [20261030_072153.]: Entered 'clean_dt'-Function [20261030_072153.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072153.]: got experimental data [20261030_072153.]: Entered 'clean_dt'-Function [20261030_072153.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072153.]: got calibration data [20261030_072153.]: ### Starting with regression calculations ### [20261030_072153.]: Entered 'regression_type1'-Function [20261030_072153.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072153.]: Logging df_agg: CpG#1 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_072153.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_072153.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_072153.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072153.]: Logging df_agg: CpG#2 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_072153.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_072153.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_072153.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072153.]: Logging df_agg: CpG#3 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_072153.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_072153.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_072153.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072153.]: Logging df_agg: CpG#4 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_072153.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_072153.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_072153.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072153.]: Logging df_agg: CpG#5 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_072153.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_072153.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_072153.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072153.]: Logging df_agg: CpG#6 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_072153.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_072153.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_072153.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072153.]: Logging df_agg: CpG#7 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_072153.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_072153.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_072153.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072153.]: Logging df_agg: CpG#8 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_072153.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_072153.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_072153.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072153.]: Logging df_agg: CpG#9 [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_072153.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_072153.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_072153.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072153.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072153.]: Logging df_agg: row_means [20261030_072153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072153.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_072153.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20261030_072153.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_072153.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072153.]: Entered 'hyperbolic_regression'-Function [20261030_072153.]: 'hyperbolic_regression': minmax = FALSE [20261030_072153.]: Entered 'cubic_regression'-Function [20261030_072153.]: 'cubic_regression': minmax = FALSE [20261030_072154.]: ### Starting with plotting ### [20261030_072154.]: Creating plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1.png [20261030_072154.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -122613865.013139 , a = 49334335.7029579 , b = -122613865.013139 , d = 58975219.0688417 [20261030_072154.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120089e-05 , b = -0.00558069687349687 , c = 0.784061853455187 , d = 1.93182659932659 [20261030_072155.]: Creating plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2.png [20261030_072155.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 162891714.839692 , a = 41030469.8229346 , b = 162891714.839692 , d = 46460363.8173876 [20261030_072155.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904603e-06 , b = 0.00293158941798941 , c = 0.514821742825076 , d = 9.27667003367005 [20261030_072156.]: Creating plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3.png [20261030_072156.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 41626006.0051657 , a = 41686731.9342648 , b = 41626006.0051657 , d = 54595421.3066627 [20261030_072156.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409347 , c = 0.627831292287959 , d = 3.88705218855219 [20261030_072157.]: Creating plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4.png [20261030_072157.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022212.551887 , a = 42779496.9363292 , b = 128022212.551887 , d = 50999438.5551692 [20261030_072157.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598365 , d = 6.17055050505049 [20261030_072158.]: Creating plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5.png [20261030_072158.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 24717712.3979446 , a = 35140113.1415749 , b = 24717712.3979446 , d = 46486787.5182053 [20261030_072158.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193035e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053873 [20261030_072159.]: Creating plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6.png [20261030_072159.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17203716.269979 , a = 41060376.0266804 , b = 17203716.269979 , d = 46869059.985468 [20261030_072159.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037017 , c = 0.561022132435467 , d = 6.1479276094276 [20261030_072200.]: Creating plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7.png [20261030_072200.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -68503828.4057994 , a = 35392554.2047271 , b = -68503828.4057994 , d = 58138169.7620283 [20261030_072200.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050506e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454545 [20261030_072201.]: Creating plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8.png [20261030_072201.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20261030_072201.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207631e-05 , b = 0.00447978143338143 , c = 0.534897737694405 , d = 9.56981481481482 [20261030_072202.]: Creating plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9.png [20261030_072202.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -225499338.127857 , a = 51924739.565819 , b = -225499338.127857 , d = 60495046.3669921 [20261030_072202.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487094e-05 , b = -0.00542281173641175 , c = 0.67899229597563 , d = 2.72413468013467 [20261030_072203.]: Creating plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans.png [20261030_072203.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 39921892.766645 , a = 44479103.515016 , b = 39921892.766645 , d = 55110708.9243519 [20261030_072203.]: # CpG-site: row_means Cubic: Using a = 2.88923726150394e-05 , b = -0.00062995927529263 , c = 0.600117857944526 , d = 5.17789562289561 [20261030_072204.]: Entered 'solving_equations'-Function [20261030_072204.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337065099387 [20261030_072204.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663101211932 [20261030_072204.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900368097653 [20261030_072204.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197154163355 [20261030_072204.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156832854155 [20261030_072204.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074429061326 [20261030_072204.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105730058199 [20261030_072204.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235911684274 [20261030_072204.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279319799 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_072204.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513546404503 [20261030_072204.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.713333449342 [20261030_072204.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.583034106559 [20261030_072204.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277426195958 [20261030_072204.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277176665323 [20261030_072204.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097812796283 [20261030_072204.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036857334245 [20261030_072204.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808452073521 [20261030_072204.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.098734843749 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.2747259647607 [20261030_072204.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004974759019 [20261030_072204.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988494014881 [20261030_072204.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074528673694 [20261030_072204.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168866798584 [20261030_072204.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718158968292 [20261030_072204.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.0913919707044 [20261030_072204.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.602380684892 [20261030_072204.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736002858843 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584327758665 [20261030_072204.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021280729051 [20261030_072204.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2036379860552 [20261030_072204.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960869187341 [20261030_072204.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638166399534 [20261030_072204.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755509319524 [20261030_072204.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670215786352 [20261030_072204.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568487197 [20261030_072204.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655966829078 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411384435881 [20261030_072204.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462057787415 [20261030_072204.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738367243186 [20261030_072204.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311728406042 [20261030_072204.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456894331638 [20261030_072204.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021903481867 [20261030_072204.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100853167933 [20261030_072204.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549720889705 [20261030_072204.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771726613073 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248120128331 [20261030_072204.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907220460242 [20261030_072204.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343919587325 [20261030_072204.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110460936217 [20261030_072204.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745854238605 [20261030_072204.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336277632583 [20261030_072204.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292385524945 [20261030_072204.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655492363431 [20261030_072204.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938362873301 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841345855363 [20261030_072204.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258575007991 [20261030_072204.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672216399023 [20261030_072204.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.428767337219 [20261030_072204.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.523688002235 [20261030_072204.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.55580795697 [20261030_072204.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896054370436 [20261030_072204.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.262862323887 [20261030_072204.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187779960817 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_072204.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 [20261030_072204.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 [20261030_072204.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 [20261030_072204.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 [20261030_072204.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 [20261030_072204.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 [20261030_072204.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 [20261030_072204.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 [20261030_072204.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 [20261030_072204.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981666733111 [20261030_072204.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.968092358338 [20261030_072204.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496840745076 [20261030_072204.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444705337128 [20261030_072204.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944305896681 [20261030_072204.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478941278433 [20261030_072204.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518418132899 [20261030_072204.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189176367421 [20261030_072204.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857344147 [20261030_072204.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_072204.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47355395665994 [20261030_072204.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375578127381 [20261030_072204.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9760271531374 [20261030_072204.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614438759386 [20261030_072204.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185466789905 [20261030_072205.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295840504541 [20261030_072205.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250247349573 [20261030_072205.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721270549024 [20261030_072205.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506146983331 [20261030_072205.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20261030_072205.]: ### Starting with regression calculations ### [20261030_072205.]: Entered 'regression_type1'-Function [20261030_072205.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337065099387, 14.7663101211932, 23.1900368097653, 33.8197154163355, 46.5156832854155, 60.2074429061326, 71.6105730058199, 80.2235911684274)c(6.03337065099387, 2.2663101211932, 1.8099631902347, 3.6802845836645, 3.4843167145845, 2.2925570938674, 3.3894269941801, 7.2764088315726)c(NA, 18.1304809695456, 7.2398527609388, 9.81409222310534, 6.968633429169, 3.66809135018784, 4.51923599224013, 8.31589580751154) [20261030_072205.]: Logging df_agg: CpG#1 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_072205.]: c(6.03337065099387, 14.7663101211932, 23.1900368097653, 33.8197154163355, 46.5156832854155, 60.2074429061326, 71.6105730058199, 80.2235911684274)[20261030_072205.]: c(6.03337065099387, 2.2663101211932, 1.8099631902347, 3.6802845836645, 3.4843167145845, 2.2925570938674, 3.3894269941801, 7.2764088315726)[20261030_072205.]: c(NA, 18.1304809695456, 7.2398527609388, 9.81409222310534, 6.968633429169, 3.66809135018784, 4.51923599224013, 8.31589580751154) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513546404503, 13.713333449342, 24.583034106559, 31.8277426195958, 43.0277176665323, 63.1097812796283, 73.3036857334245, 84.4808452073521, 100)c(6.85513546404503, 1.213333449342, 0.416965893440999, 5.6722573804042, 6.9722823334677, 0.609781279628301, 1.6963142665755, 3.0191547926479, 0)c(NA, 9.706667594736, 1.667863573764, 15.1260196810779, 13.9445646669354, 0.975650047405281, 2.261752355434, 3.45046262016902, 0) [20261030_072205.]: Logging df_agg: CpG#2 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(6.85513546404503, 13.713333449342, 24.583034106559, 31.8277426195958, 43.0277176665323, 63.1097812796283, 73.3036857334245, 84.4808452073521, 100)[20261030_072205.]: c(6.85513546404503, 1.213333449342, 0.416965893440999, 5.6722573804042, 6.9722823334677, 0.609781279628301, 1.6963142665755, 3.0191547926479, 0)[20261030_072205.]: c(NA, 9.706667594736, 1.667863573764, 15.1260196810779, 13.9445646669354, 0.975650047405281, 2.261752355434, 3.45046262016902, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.2747259647607, 12.7004974759019, 24.2988494014881, 33.7074528673694, 47.0168866798584, 61.5718158968292, 76.0913919707044, 79.602380684892, 100)c(5.2747259647607, 0.2004974759019, 0.701150598511902, 3.7925471326306, 2.9831133201416, 0.9281841031708, 1.09139197070439, 7.897619315108, 0)c(NA, 1.6039798072152, 2.80460239404761, 10.1134590203483, 5.9662266402832, 1.48509456507328, 1.45518929427252, 9.02585064583772, 0) [20261030_072205.]: Logging df_agg: CpG#3 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(5.2747259647607, 12.7004974759019, 24.2988494014881, 33.7074528673694, 47.0168866798584, 61.5718158968292, 76.0913919707044, 79.602380684892, 100)[20261030_072205.]: c(5.2747259647607, 0.2004974759019, 0.701150598511902, 3.7925471326306, 2.9831133201416, 0.9281841031708, 1.09139197070439, 7.897619315108, 0)[20261030_072205.]: c(NA, 1.6039798072152, 2.80460239404761, 10.1134590203483, 5.9662266402832, 1.48509456507328, 1.45518929427252, 9.02585064583772, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584327758665, 14.2021280729051, 23.2036379860552, 34.7960869187341, 44.4638166399534, 64.2755509319524, 71.7670215786352, 82.5599568487197, 100)c(5.07584327758665, 1.7021280729051, 1.7963620139448, 2.7039130812659, 5.5361833600466, 1.7755509319524, 3.23297842136481, 4.94004315128031, 0)c(NA, 13.6170245832408, 7.1854480557792, 7.21043488337574, 11.0723667200932, 2.84088149112383, 4.31063789515307, 5.64576360146321, 0) [20261030_072205.]: Logging df_agg: CpG#4 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(5.07584327758665, 14.2021280729051, 23.2036379860552, 34.7960869187341, 44.4638166399534, 64.2755509319524, 71.7670215786352, 82.5599568487197, 100)[20261030_072205.]: c(5.07584327758665, 1.7021280729051, 1.7963620139448, 2.7039130812659, 5.5361833600466, 1.7755509319524, 3.23297842136481, 4.94004315128031, 0)[20261030_072205.]: c(NA, 13.6170245832408, 7.1854480557792, 7.21043488337574, 11.0723667200932, 2.84088149112383, 4.31063789515307, 5.64576360146321, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411384435881, 12.4462057787415, 22.9738367243186, 34.1311728406042, 46.7456894331638, 58.6021903481867, 74.5100853167933, 86.8549720889705, 100)c(6.96411384435881, 0.0537942212584994, 2.0261632756814, 3.3688271593958, 3.2543105668362, 3.8978096518133, 0.489914683206706, 0.645027911029501, 0)c(NA, 0.430353770067995, 8.10465310272561, 8.98353909172213, 6.50862113367241, 6.23649544290128, 0.653219577608941, 0.737174755462287, 0) [20261030_072205.]: Logging df_agg: CpG#5 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(6.96411384435881, 12.4462057787415, 22.9738367243186, 34.1311728406042, 46.7456894331638, 58.6021903481867, 74.5100853167933, 86.8549720889705, 100)[20261030_072205.]: c(6.96411384435881, 0.0537942212584994, 2.0261632756814, 3.3688271593958, 3.2543105668362, 3.8978096518133, 0.489914683206706, 0.645027911029501, 0)[20261030_072205.]: c(NA, 0.430353770067995, 8.10465310272561, 8.98353909172213, 6.50862113367241, 6.23649544290128, 0.653219577608941, 0.737174755462287, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248120128331, 14.2907220460242, 24.7343919587325, 31.7110460936217, 44.7745854238605, 60.1336277632583, 70.3292385524945, 88.2655492363431, 100)c(6.82248120128331, 1.7907220460242, 0.265608041267502, 5.7889539063783, 5.2254145761395, 2.3663722367417, 4.6707614475055, 0.765549236343105, 0)c(NA, 14.3257763681936, 1.06243216507001, 15.4372104170088, 10.450829152279, 3.78619557878671, 6.22768193000733, 0.874913412963549, 0) [20261030_072205.]: Logging df_agg: CpG#6 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(6.82248120128331, 14.2907220460242, 24.7343919587325, 31.7110460936217, 44.7745854238605, 60.1336277632583, 70.3292385524945, 88.2655492363431, 100)[20261030_072205.]: c(6.82248120128331, 1.7907220460242, 0.265608041267502, 5.7889539063783, 5.2254145761395, 2.3663722367417, 4.6707614475055, 0.765549236343105, 0)[20261030_072205.]: c(NA, 14.3257763681936, 1.06243216507001, 15.4372104170088, 10.450829152279, 3.78619557878671, 6.22768193000733, 0.874913412963549, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841345855363, 13.6258575007991, 23.2672216399023, 33.428767337219, 47.523688002235, 60.55580795697, 72.3896054370436, 77.262862323887)c(6.75841345855363, 1.1258575007991, 1.7327783600977, 4.071232662781, 2.476311997765, 1.94419204303, 2.6103945629564, 10.237137676113)c(NA, 9.0068600063928, 6.9311134403908, 10.8566204340827, 4.95262399553, 3.110707268848, 3.48052608394187, 11.6995859155577) [20261030_072205.]: Logging df_agg: CpG#7 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_072205.]: c(6.75841345855363, 13.6258575007991, 23.2672216399023, 33.428767337219, 47.523688002235, 60.55580795697, 72.3896054370436, 77.262862323887)[20261030_072205.]: c(6.75841345855363, 1.1258575007991, 1.7327783600977, 4.071232662781, 2.476311997765, 1.94419204303, 2.6103945629564, 10.237137676113)[20261030_072205.]: c(NA, 9.0068600063928, 6.9311134403908, 10.8566204340827, 4.95262399553, 3.110707268848, 3.48052608394187, 11.6995859155577) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20261030_072205.]: Logging df_agg: CpG#8 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072205.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20261030_072205.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20261030_072205.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981666733111, 14.968092358338, 24.6496840745076, 31.5444705337128, 44.0944305896681, 57.2478941278433, 69.1518418132899, 83.8189176367421)c(8.66981666733111, 2.468092358338, 0.350315925492399, 5.9555294662872, 5.9055694103319, 5.2521058721567, 5.84815818671009, 3.6810823632579)c(NA, 19.744738866704, 1.4012637019696, 15.8814119100992, 11.8111388206638, 8.40336939545072, 7.79754424894679, 4.20695127229474) [20261030_072205.]: Logging df_agg: CpG#9 [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_072205.]: c(8.66981666733111, 14.968092358338, 24.6496840745076, 31.5444705337128, 44.0944305896681, 57.2478941278433, 69.1518418132899, 83.8189176367421)[20261030_072205.]: c(8.66981666733111, 2.468092358338, 0.350315925492399, 5.9555294662872, 5.9055694103319, 5.2521058721567, 5.84815818671009, 3.6810823632579)[20261030_072205.]: c(NA, 19.744738866704, 1.4012637019696, 15.8814119100992, 11.8111388206638, 8.40336939545072, 7.79754424894679, 4.20695127229474) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072205.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47355395665994, 13.5375578127381, 23.9760271531374, 32.9614438759386, 45.8185466789905, 60.9295840504541, 72.9250247349573, 82.8721270549024)c(6.47355395665994, 1.0375578127381, 1.0239728468626, 4.5385561240614, 4.1814533210095, 1.5704159495459, 2.0749752650427, 4.62787294509759)c(NA, 8.3004625019048, 4.09589138745039, 12.1028163308304, 8.362906642019, 2.51266551927343, 2.76663368672359, 5.28899765154011) [20261030_072205.]: Logging df_agg: row_means [20261030_072205.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20261030_072205.]: c(6.47355395665994, 13.5375578127381, 23.9760271531374, 32.9614438759386, 45.8185466789905, 60.9295840504541, 72.9250247349573, 82.8721270549024)[20261030_072205.]: c(6.47355395665994, 1.0375578127381, 1.0239728468626, 4.5385561240614, 4.1814533210095, 1.5704159495459, 2.0749752650427, 4.62787294509759)[20261030_072205.]: c(NA, 8.3004625019048, 4.09589138745039, 12.1028163308304, 8.362906642019, 2.51266551927343, 2.76663368672359, 5.28899765154011) [20261030_072205.]: Entered 'hyperbolic_regression'-Function [20261030_072205.]: 'hyperbolic_regression': minmax = FALSE [20261030_072205.]: Entered 'cubic_regression'-Function [20261030_072205.]: 'cubic_regression': minmax = FALSE [20261030_072206.]: ### Starting with plotting ### [20261030_072206.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_h.png [20261030_072206.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 152155607.60335 , a = 39426240.5630646 , b = 152155607.60335 , d = 44642967.3023488 [20261030_072206.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902847132575e-05 , b = 0.011997383038387 , c = 0.410459257139915 , d = 6.65096598344666 [20261030_072207.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_h.png [20261030_072207.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63517477.9874342 , a = 42700954.6876555 , b = 63517477.9874342 , d = 44878654.4742028 [20261030_072207.]: # CpG-site: CpG#2 Cubic: Using a = -4.57918051522134e-05 , b = 0.0097897544975267 , c = 0.400365510080476 , d = 7.17765313034907 [20261030_072208.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_h.png [20261030_072208.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 66900849.9505808 , a = 41881375.2961668 , b = 66900849.9505808 , d = 44175076.0533406 [20261030_072208.]: # CpG-site: CpG#3 Cubic: Using a = -2.85485970680805e-05 , b = 0.00587955529578106 , c = 0.626870349524531 , d = 4.78057277014695 [20261030_072209.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_h.png [20261030_072209.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 82453566.9761327 , a = 52068755.7260525 , b = 82453566.9761327 , d = 54895693.1638396 [20261030_072209.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135979052046e-05 , b = 0.0049693687139749 , c = 0.637632601583233 , d = 5.04633760907625 [20261030_072210.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_h.png [20261030_072210.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 58097937.7573157 , a = 53163922.8755103 , b = 58097937.7573157 , d = 55155813.3625871 [20261030_072210.]: # CpG-site: CpG#5 Cubic: Using a = -4.67908297968657e-05 , b = 0.00978122944625307 , c = 0.422963348950201 , d = 6.6174842968385 [20261030_072210.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_h.png [20261030_072210.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 74786508.3828831 , a = 51224723.0683234 , b = 74786508.3828831 , d = 53788792.7445516 [20261030_072210.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665666352416e-05 , b = 0.00740475150249675 , c = 0.461019690579009 , d = 7.23067490760395 [20261030_072211.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_h.png [20261030_072211.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 115106530.495007 , a = 26369523.1261913 , b = 115106530.495007 , d = 30315993.0100105 [20261030_072211.]: # CpG-site: CpG#7 Cubic: Using a = -0.0001339377310234 , b = 0.0182624901654704 , c = 0.234529134494671 , d = 7.29090060542389 [20261030_072212.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_h.png [20261030_072212.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 45915737.8669938 , a = 43350813.8476131 , b = 45915737.8669938 , d = 44925021.567023 [20261030_072212.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829792e-05 , b = 0.00998862677056795 , c = 0.502915955578275 , d = 4.90625922077109 [20261030_072213.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_h.png [20261030_072213.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 141270505.734171 , a = 30797885.6291842 , b = 141270505.734171 , d = 35641408.8569786 [20261030_072213.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503641601273e-05 , b = 0.00591895873701417 , c = 0.44574254984227 , d = 8.73330038808108 [20261030_072214.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_h.png [20261030_072214.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 95591289.9469755 , a = 33117616.0641117 , b = 95591289.9469755 , d = 36394988.1728066 [20261030_072214.]: # CpG-site: row_means Cubic: Using a = -7.67916342481087e-05 , b = 0.0124890186900438 , c = 0.369098468916399 , d = 6.83741341184345 [20261030_072215.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_h.png [20261030_072216.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_h.png [20261030_072216.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_h.png [20261030_072217.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_h.png [20261030_072218.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_h.png [20261030_072218.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_h.png [20261030_072219.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_h.png [20261030_072220.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_h.png [20261030_072221.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_h.png [20261030_072221.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_h.png [20261030_072222.]: Entered 'solving_equations'-Function [20261030_072222.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067341Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400674Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20261030_072222.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329948Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299661Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.9393299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.3373299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.5773299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20261030_072222.]: Solving cubic regression for CpG#3 Coefficients: -0.902947811447812Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144781Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447811Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20261030_072222.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949513Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828285Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20261030_072222.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461269Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946127Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20261030_072222.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572396Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723906Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072222.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20261030_072223.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545455Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212122Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072223.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20261030_072223.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185184Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072223.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20261030_072223.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865332Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072223.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20261030_072223.]: Solving cubic regression for row_means Coefficients: -0.771215488215503Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747476Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548822Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363637Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944526Coefficients: -0.00062995927529263Coefficients: 2.88923726150394e-05 [20261030_072223.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20261030_072223.]: ### Starting with regression calculations ### [20261030_072223.]: Entered 'regression_type1'-Function [20261030_072223.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072223.]: Logging df_agg: CpG#1 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)[20261030_072223.]: c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)[20261030_072223.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072223.]: Logging df_agg: CpG#2 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)[20261030_072223.]: c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)[20261030_072223.]: c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072223.]: Logging df_agg: CpG#3 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)[20261030_072223.]: c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)[20261030_072223.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072223.]: Logging df_agg: CpG#4 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)[20261030_072223.]: c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)[20261030_072223.]: c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072223.]: Logging df_agg: CpG#5 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20261030_072223.]: c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20261030_072223.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072223.]: Logging df_agg: CpG#6 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20261030_072223.]: c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20261030_072223.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072223.]: Logging df_agg: CpG#7 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20261030_072223.]: c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20261030_072223.]: c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072223.]: Logging df_agg: CpG#8 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20261030_072223.]: c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20261030_072223.]: c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072223.]: Logging df_agg: CpG#9 [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)[20261030_072223.]: c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)[20261030_072223.]: c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072223.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072223.]: Logging df_agg: row_means [20261030_072223.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072223.]: c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20261030_072223.]: c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20261030_072223.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072223.]: Entered 'hyperbolic_regression'-Function [20261030_072223.]: 'hyperbolic_regression': minmax = FALSE [20261030_072223.]: Entered 'cubic_regression'-Function [20261030_072223.]: 'cubic_regression': minmax = FALSE [20261030_072224.]: ### Starting with plotting ### [20261030_072224.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG1_corrected_c.png [20261030_072224.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78106244653 , a = 2039.49591579156 , b = -1164.78106244653 , d = 1955.83291596878 [20261030_072224.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240359e-05 , b = 0.00462173155013787 , c = 0.850326258865831 , d = 0.500073874211072 [20261030_072225.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG2_corrected_c.png [20261030_072225.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.222164939352 , a = 4988.66619289946 , b = -464.222164939352 , d = 4925.13704784234 [20261030_072225.]: # CpG-site: CpG#2 Cubic: Using a = -1.4081179690632e-05 , b = 0.00190849431754492 , c = 0.933455870941473 , d = 0.363096210099609 [20261030_072225.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG3_corrected_c.png [20261030_072225.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.610227727114 , a = 2026.53507065859 , b = -745.610227727114 , d = 1953.28481682132 [20261030_072225.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857189e-05 , b = 0.00366863707889094 , c = 0.880665171220891 , d = 0.51077930883367 [20261030_072226.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG4_corrected_c.png [20261030_072226.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.061644061801 , a = 2871.13554979021 , b = -833.061644061801 , d = 2796.81044129837 [20261030_072226.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643008e-05 , b = 0.00309017851940703 , c = 0.897073107777642 , d = 0.443455761841832 [20261030_072227.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG5_corrected_c.png [20261030_072227.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.579215668715 , a = 12479.1700091944 , b = 222.579215668715 , d = 12433.8482115721 [20261030_072227.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185614 [20261030_072228.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG6_corrected_c.png [20261030_072228.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.208171123953 , a = 21301.4518255625 , b = -700.208171123953 , d = 21236.8640475514 [20261030_072228.]: # CpG-site: CpG#6 Cubic: Using a = -5.83833515137487e-06 , b = 0.000828447163218502 , c = 0.970047764475167 , d = 0.15807470141223 [20261030_072229.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG7_corrected_c.png [20261030_072229.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90996957143 , a = 1477.92216064115 , b = -1144.90996957143 , d = 1394.71069966527 [20261030_072229.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614046e-05 , b = 0.00607993130565127 , c = 0.805411493452391 , d = 0.615125382790208 [20261030_072230.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG8_corrected_c.png [20261030_072230.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.368509782023 , a = 2377.37946514959 , b = -349.368509782023 , d = 2315.20684352233 [20261030_072230.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669641e-05 , b = 0.00353621593528019 , c = 0.877916844709072 , d = 0.698684606493051 [20261030_072231.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_CpG9_corrected_c.png [20261030_072231.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.92126983064 , a = 3745.12421947974 , b = -1536.92126983064 , d = 3656.21581945553 [20261030_072231.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.0037847824718234 , c = 0.871825263051794 , d = 0.455475740974124 [20261030_072232.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_rowmeans_corrected_c.png [20261030_072232.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.645475573295 , a = 3198.48422003183 , b = -820.645475573295 , d = 3124.22893580889 [20261030_072232.]: # CpG-site: row_means Cubic: Using a = -2.34328362100012e-05 , b = 0.00319139073176272 , c = 0.891842168032126 , d = 0.493517689787157 [20261030_072233.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG1_corrected_c.png [20261030_072234.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG2_corrected_c.png [20261030_072234.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG3_corrected_c.png [20261030_072235.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG4_corrected_c.png [20261030_072236.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG5_corrected_c.png [20261030_072237.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG6_corrected_c.png [20261030_072237.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG7_corrected_c.png [20261030_072238.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG8_corrected_c.png [20261030_072239.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_CpG9_corrected_c.png [20261030_072240.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Testlocus_error_rowmeans_corrected_c.png [20261030_072241.]: Entered 'solving_equations'-Function [20261030_072241.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602579076879 [20261030_072241.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.142096357034 [20261030_072241.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366527157 [20261030_072241.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231207032054 [20261030_072241.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.8735620241573 [20261030_072241.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773645028033 [20261030_072241.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858838331412 [20261030_072241.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639195014 [20261030_072241.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385425031816 [20261030_072241.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287621733851 [20261030_072241.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.493588229571 [20261030_072241.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239296170092 [20261030_072241.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.4228676355667 [20261030_072241.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.181272139342 [20261030_072241.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760372929834 [20261030_072241.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.063868061774 [20261030_072241.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691105554959 [20261030_072241.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.848537559774 [20261030_072241.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213732883416 [20261030_072241.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467802753075 [20261030_072241.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754853613507 [20261030_072241.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.093084500393 [20261030_072241.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204577036727 [20261030_072241.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558105473588 [20261030_072241.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836210631828 [20261030_072241.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384901229488 [20261030_072241.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934353263083 [20261030_072241.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015095813931 [20261030_072241.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873103568343 [20261030_072241.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.4496403659935 [20261030_072241.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016736181719 [20261030_072241.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295904403638 [20261030_072241.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.600843024255 [20261030_072241.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093208284472 [20261030_072241.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.612598134037 [20261030_072241.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537053290314 [20261030_072241.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116364484085 [20261030_072241.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579457809184 [20261030_072241.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182701863866 [20261030_072241.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911324757168 [20261030_072241.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481353960952 [20261030_072241.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396652980196 [20261030_072241.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.461439298495 [20261030_072241.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.381569309021 [20261030_072241.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3536028637085 [20261030_072241.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901399499323 [20261030_072241.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507328483069 [20261030_072241.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0946942569506 [20261030_072241.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841363342565 [20261030_072241.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91648949924253 [20261030_072241.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558194100861 [20261030_072241.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880624107802 [20261030_072241.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657583690511 [20261030_072241.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4693025780386 [20261030_072241.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197880683796 [20261030_072241.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916665943217 [20261030_072241.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757493171828 [20261030_072241.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.11925085451 [20261030_072241.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229699294448 [20261030_072241.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.6626011041377 [20261030_072241.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858982081727 [20261030_072241.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111474823288 [20261030_072241.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834124962043 [20261030_072241.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038561103266 [20261030_072241.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.5840801679664 [20261030_072241.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322994192245 [20261030_072241.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055286134703 [20261030_072241.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.174822392983 [20261030_072241.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968650296574 [20261030_072241.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445050457789 [20261030_072241.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 [20261030_072241.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 [20261030_072241.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 [20261030_072241.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 [20261030_072241.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 [20261030_072241.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 [20261030_072241.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 [20261030_072241.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 [20261030_072241.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 [20261030_072241.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 [20261030_072241.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.3625743072862 [20261030_072241.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725959671593 [20261030_072241.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471092660727 [20261030_072241.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.8507642868172 [20261030_072241.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341044927562 [20261030_072241.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156931634792 [20261030_072241.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730209044524 [20261030_072241.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757563082476 [20261030_072241.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667207120235 [20261030_072241.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593115333539 [20261030_072241.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. [20261030_072241.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404703445064 [20261030_072241.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806152775697 [20261030_072241.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683832460513 [20261030_072241.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554463661372 [20261030_072241.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.033748194858 [20261030_072241.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452143777835 [20261030_072241.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661419200256 [20261030_072241.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839380078139 [20261030_072241.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201077481815 [20261030_072241.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149524940396 [20261030_072241.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got calibration data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got experimental data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got calibration data [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072241.]: got calibration data [20261030_072241.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got calibration data [20261030_072241.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20261030_072241.]: Entered 'clean_dt'-Function [20261030_072241.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072241.]: got experimental data [20261030_072241.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got calibration data [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got experimental data [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20261030_072242.]: got experimental data [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got calibration data [20261030_072242.]: Entered 'cubic_regression'-Function [20261030_072242.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_072242.]: Entered 'cubic_regression'-Function [20261030_072242.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got calibration data [20261030_072242.]: Entered 'hyperbolic_regression'-Function [20261030_072242.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20261030_072242.]: Entered 'hyperbolic_regression'-Function [20261030_072242.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20261030_072242.]: on_start: using future::plan("sequential") [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got experimental data [20261030_072242.]: Entered 'clean_dt'-Function [20261030_072242.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072242.]: got calibration data [20261030_072242.]: ### Starting with regression calculations ### [20261030_072242.]: Entered 'regression_type1'-Function [20261030_072243.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072243.]: Logging df_agg: CpG#1 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_072243.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_072243.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_072243.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072243.]: Logging df_agg: CpG#2 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_072243.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_072243.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_072243.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072243.]: Logging df_agg: CpG#3 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_072243.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_072243.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_072243.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072243.]: Logging df_agg: CpG#4 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_072243.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_072243.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_072243.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072243.]: Logging df_agg: CpG#5 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_072243.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_072243.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_072243.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072243.]: Logging df_agg: CpG#6 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_072243.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_072243.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_072243.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072243.]: Logging df_agg: CpG#7 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_072243.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_072243.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_072243.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072243.]: Logging df_agg: CpG#8 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_072243.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_072243.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_072243.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072243.]: Logging df_agg: CpG#9 [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_072243.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_072243.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_072243.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072243.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072243.]: Logging df_agg: row_means [20261030_072243.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072243.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_072243.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_072243.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_072243.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072243.]: Entered 'hyperbolic_regression'-Function [20261030_072243.]: 'hyperbolic_regression': minmax = FALSE [20261030_072243.]: Entered 'cubic_regression'-Function [20261030_072243.]: 'cubic_regression': minmax = FALSE [20261030_072244.]: ### Starting with plotting ### [20261030_072244.]: Creating plot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG1.png [20261030_072244.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -122613865.013139 , a = 49334335.7029579 , b = -122613865.013139 , d = 58975219.0688417 [20261030_072244.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120089e-05 , b = -0.00558069687349687 , c = 0.784061853455187 , d = 1.93182659932659 [20261030_072245.]: Creating plot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG2.png [20261030_072245.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 162891714.839692 , a = 41030469.8229346 , b = 162891714.839692 , d = 46460363.8173876 [20261030_072245.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904603e-06 , b = 0.00293158941798941 , c = 0.514821742825076 , d = 9.27667003367005 [20261030_072246.]: Creating plot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG3.png [20261030_072246.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 41626006.0051657 , a = 41686731.9342648 , b = 41626006.0051657 , d = 54595421.3066627 [20261030_072246.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409347 , c = 0.627831292287959 , d = 3.88705218855219 [20261030_072247.]: Creating plot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG4.png [20261030_072247.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022212.551887 , a = 42779496.9363292 , b = 128022212.551887 , d = 50999438.5551692 [20261030_072247.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598365 , d = 6.17055050505049 [20261030_072247.]: Creating plot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG5.png [20261030_072247.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 24717712.3979446 , a = 35140113.1415749 , b = 24717712.3979446 , d = 46486787.5182053 [20261030_072247.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193035e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053873 [20261030_072248.]: Creating plot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG6.png [20261030_072248.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17203716.269979 , a = 41060376.0266804 , b = 17203716.269979 , d = 46869059.985468 [20261030_072248.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037017 , c = 0.561022132435467 , d = 6.1479276094276 [20261030_072249.]: Creating plot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG7.png [20261030_072249.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -68503828.4057994 , a = 35392554.2047271 , b = -68503828.4057994 , d = 58138169.7620283 [20261030_072249.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050506e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454545 [20261030_072250.]: Creating plot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG8.png [20261030_072250.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20261030_072250.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207631e-05 , b = 0.00447978143338143 , c = 0.534897737694405 , d = 9.56981481481482 [20261030_072251.]: Creating plot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_CpG9.png [20261030_072251.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -225499338.127857 , a = 51924739.565819 , b = -225499338.127857 , d = 60495046.3669921 [20261030_072251.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487094e-05 , b = -0.00542281173641175 , c = 0.67899229597563 , d = 2.72413468013467 [20261030_072253.]: Creating plot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_rowmeans.png [20261030_072253.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 41156791.4828152 , a = 45854712.402729 , b = 41156791.4828152 , d = 56815128.7822559 [20261030_072253.]: # CpG-site: row_means Cubic: Using a = 2.88923726150394e-05 , b = -0.000629959275292622 , c = 0.600117857944525 , d = 5.17789562289561 [20261030_072255.]: on_start: using future::plan("sequential") [20261030_072255.]: Entered 'clean_dt'-Function [20261030_072255.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072255.]: got experimental data [20261030_072255.]: Entered 'clean_dt'-Function [20261030_072255.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20261030_072255.]: got calibration data [20261030_072255.]: ### Starting with regression calculations ### [20261030_072255.]: Entered 'regression_type1'-Function [20261030_072255.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072255.]: Logging df_agg: CpG#1 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20261030_072255.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20261030_072255.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20261030_072255.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072255.]: Logging df_agg: CpG#2 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20261030_072255.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20261030_072255.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20261030_072255.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072255.]: Logging df_agg: CpG#3 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20261030_072255.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20261030_072255.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20261030_072255.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072255.]: Logging df_agg: CpG#4 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20261030_072255.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20261030_072255.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20261030_072255.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072255.]: Logging df_agg: CpG#5 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20261030_072255.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20261030_072255.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20261030_072255.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072255.]: Logging df_agg: CpG#6 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20261030_072255.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20261030_072255.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20261030_072255.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072255.]: Logging df_agg: CpG#7 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20261030_072255.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20261030_072255.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20261030_072255.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072255.]: Logging df_agg: CpG#8 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20261030_072255.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20261030_072255.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20261030_072255.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072255.]: Logging df_agg: CpG#9 [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20261030_072255.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20261030_072255.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20261030_072255.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072255.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072255.]: Logging df_agg: row_means [20261030_072255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072255.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20261030_072255.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20261030_072255.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20261030_072255.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20261030_072255.]: Entered 'hyperbolic_regression'-Function [20261030_072255.]: 'hyperbolic_regression': minmax = FALSE [20261030_072255.]: Entered 'cubic_regression'-Function [20261030_072255.]: 'cubic_regression': minmax = FALSE [20261030_072256.]: Entered 'solving_equations'-Function [20261030_072256.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067341Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400674Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006734Coefficients: 0.784061853455187Coefficients: -0.00558069687349687Coefficients: 6.53413423120089e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329948Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299661Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.9393299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.3373299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.5773299663299Coefficients: 0.514821742825076Coefficients: 0.00293158941798941Coefficients: 8.04237934904603e-06 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#3 Coefficients: -0.902947811447812Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144781Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447811Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.627831292287959Coefficients: -0.000797009331409347Coefficients: 2.30555869809204e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949513Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828285Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598365Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461269Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946127Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193035e-06 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572396Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723906Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037017Coefficients: 2.38852884399552e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545455Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212122Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050506e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185184Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694405Coefficients: 0.00447978143338143Coefficients: -1.50060965207631e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865332Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641175Coefficients: 7.72300426487094e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: Solving cubic regression for row_means Coefficients: -0.771215488215501Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747476Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548822Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944525Coefficients: -0.000629959275292622Coefficients: 2.88923726150394e-05 [20261030_072256.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20261030_072256.]: ### Starting with regression calculations ### [20261030_072256.]: Entered 'regression_type1'-Function [20261030_072257.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072257.]: Logging df_agg: CpG#1 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(1.33401421032357, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010515, 75.864353455204, 82.2543632311352, 100)[20261030_072257.]: c(1.33401421032357, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010515, 0.864353455203997, 5.2456367688648, 0)[20261030_072257.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168241, 1.15247127360533, 5.9950134501312, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072257.]: Logging df_agg: CpG#2 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(0.548629212600338, 11.5334942360618, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569592, 84.1184829423144, 100)[20261030_072257.]: c(0.548629212600338, 0.9665057639382, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959198, 3.3815170576856, 0)[20261030_072257.]: c(NA, 7.7320461115056, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945597, 3.86459092306926, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072257.]: Logging df_agg: CpG#3 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(1.44072654766675, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.7671229122681, 100)[20261030_072257.]: c(1.44072654766675, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773189, 0)[20261030_072257.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169359, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072257.]: Logging df_agg: CpG#4 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(0.858335161098692, 12.0855313705713, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519512, 74.6152796890845, 83.2536964524017, 100)[20261030_072257.]: c(0.858335161098692, 0.414468629428701, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.5824341519512, 0.384720310915498, 4.2463035475983, 0)[20261030_072257.]: c(NA, 3.3157490354296, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312193, 0.512960414553997, 4.85291834011235, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072257.]: Logging df_agg: CpG#5 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(1.45815885872156, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20261030_072257.]: c(1.45815885872156, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20261030_072257.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072257.]: Logging df_agg: CpG#6 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(0.349289777689696, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20261030_072257.]: c(0.349289777689696, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20261030_072257.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072257.]: Logging df_agg: CpG#7 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(2.12953119975093, 9.97257098314615, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20261030_072257.]: c(2.12953119975093, 2.52742901685385, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20261030_072257.]: c(NA, 20.2194321348308, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072257.]: Logging df_agg: CpG#8 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(2.01554288922102, 9.45759666110019, 26.3496745529898, 34.7279879576045, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20261030_072257.]: c(2.01554288922102, 3.04240333889981, 1.3496745529898, 2.7720120423955, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20261030_072257.]: c(NA, 24.3392267111985, 5.3986982119592, 7.39203211305467, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072257.]: Logging df_agg: CpG#9 [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(1.47485207721511, 10.1196517054928, 24.843641290935, 35.326726011764, 51.8546848506009, 65.2652545321194, 74.9150847744698, 84.6698630277556, 100)[20261030_072257.]: c(1.47485207721511, 2.3803482945072, 0.156358709065, 2.173273988236, 1.8546848506009, 2.7652545321194, 0.0849152255301959, 2.8301369722444, 0)[20261030_072257.]: c(NA, 19.0427863560576, 0.625434836259998, 5.79539730196266, 3.70936970120179, 4.42440725139104, 0.113220300706928, 3.2344422539936, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072257.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072257.]: Logging df_agg: row_means [20261030_072257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20261030_072257.]: c(1.28674218034495, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20261030_072257.]: c(1.28674218034495, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20261030_072257.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20261030_072257.]: Entered 'hyperbolic_regression'-Function [20261030_072257.]: 'hyperbolic_regression': minmax = FALSE [20261030_072257.]: Entered 'cubic_regression'-Function [20261030_072257.]: 'cubic_regression': minmax = FALSE [20261030_072258.]: Creating barplot No. 1 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG1_corrected_h.png [20261030_072259.]: Creating barplot No. 2 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG2_corrected_h.png [20261030_072259.]: Creating barplot No. 3 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG3_corrected_h.png [20261030_072300.]: Creating barplot No. 4 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG4_corrected_h.png [20261030_072301.]: Creating barplot No. 5 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG5_corrected_h.png [20261030_072302.]: Creating barplot No. 6 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG6_corrected_h.png [20261030_072303.]: Creating barplot No. 7 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG7_corrected_h.png [20261030_072303.]: Creating barplot No. 8 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG8_corrected_h.png [20261030_072304.]: Creating barplot No. 9 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_CpG9_corrected_h.png [20261030_072305.]: Creating barplot No. 10 - filename: /tmp/Rtmpv9cJRm/working_dir/Rtmph3oLpa/plotdir/Test_error_rowmeans_corrected_h.png [20261030_072306.]: on_start: using future::plan("sequential") [20261030_072307.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 334 | SKIP 12 | PASS 54 ] ══ Skipped tests (12) ══════════════════════════════════════════════════════════ • On CRAN (12): 'test-algorithm_minmax_FALSE.R:14:1', 'test-algorithm_minmax_FALSE_re.R:14:1', 'test-algorithm_minmax_TRUE.R:14:1', 'test-algorithm_minmax_TRUE_re.R:14:1', 'test-clean_dt.R:8:1', 'test-clean_dt.R:51:1', 'test-create_aggregated.R:10:1', 'test-cubic.R:15:1', 'test-cubic.R:41:1', 'test-hyperbolic.R:14:1', 'test-hyperbolic.R:42:1', 'test-lints.R:12:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-algorithm_minmax_FALSE.R:71:5'): algorithm test, type 1, minmax = FALSE ── Expected `regression_results$result_list` to equal `regression_results_fast$result_list`. Differences: `actual$CpG#8$Coef_hyper$a`: -278.0172585 `expected$CpG#8$Coef_hyper$a`: -278.0172661 `actual$CpG#8$Coef_hyper$d`: -438.8942482 `expected$CpG#8$Coef_hyper$d`: -438.8942562 `actual$CpG#8$Coef_hyper$s`: 8.83145821 `expected$CpG#8$Coef_hyper$s`: 8.83145800 ── Failure ('test-algorithm_minmax_FALSE.R:88:5'): algorithm test, type 1, minmax = FALSE ── Expected `regression_results$result_list` to equal `regression_results_minpack$result_list`. Differences: `actual$CpG#3$Coef_hyper$a`: -170.424487 `expected$CpG#3$Coef_hyper$a`: -170.424503 `actual$CpG#3$Coef_hyper$d`: -327.190130 `expected$CpG#3$Coef_hyper$d`: -327.190151 `actual$CpG#3$Coef_hyper$b1`: 0.694367309 `expected$CpG#3$Coef_hyper$b1`: 0.694367329 `actual$CpG#3$Coef_hyper$s`: 4.54097547 `expected$CpG#3$Coef_hyper$s`: 4.54097518 `actual$CpG#8$Coef_hyper$a`: -278.0172585 `expected$CpG#8$Coef_hyper$a`: -278.0172661 `actual$CpG#8$Coef_hyper$d`: -438.8942482 `expected$CpG#8$Coef_hyper$d`: -438.8942562 `actual$CpG#8$Coef_hyper$s`: 8.83145821 `expected$CpG#8$Coef_hyper$s`: 8.83145800 ── Failure ('test-algorithm_minmax_TRUE.R:71:5'): algorithm test, type 1, minmax = TRUE ── Expected `regression_results$result_list` to equal `regression_results_fast$result_list`. Differences: actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference actual$CpG#9$Coef_cubic$a != expected$CpG#9$Coef_cubic$a but don't know how to show the difference actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference ── Failure ('test-algorithm_minmax_TRUE.R:119:5'): algorithm test, type 1, minmax = TRUE ── Expected `regression_results$result_list` to equal `regression_results2$result_list`. Differences: actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference [ FAIL 4 | WARN 334 | SKIP 12 | PASS 54 ] Error: ! Test failures. Execution halted Error in deferred_run(env) : could not find function "deferred_run" Calls: * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [38s/42s] OK * checking PDF version of manual ... OK * checking HTML version of manual ... OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * DONE Status: 1 ERROR See ‘/data/localhost/ripley/R/packages/tests-MKL/rBiasCorrection.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 7:39.97, 301.21 + 16.38