* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/statuser.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 10:15:36 UTC * using option ‘--no-stop-on-test-error’ * checking for file ‘statuser/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘statuser’ version ‘0.4.0’ * 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 ‘statuser’ can be installed ... [18s/45s] OK * checking package 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 ... [38s/102s] 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 examples ... [4s/10s] OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘testthat.R’ [51s/106s] [51s/106s] ERROR Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the statuser package > # > # This file is run automatically by R CMD check > library(testthat) > library(statuser) ####################################################### This Version 2026 10 09 - 05.55PM > > test_check("statuser") desc_var() says: Multiple grouping variables should not overlap perfectly: 'x1' and 'x2' overlap perfectly desc_var() says: 'y_char' must be numeric; currently character desc_var() says: 'y_char' must be numeric; currently character desc_var() says: 'y_factor' must be numeric; currently factor desc_var() says: 'nonexistent' not found in data desc_var() says: 'nonexistent' not found in data desc_var() says: 'x' must be numeric; currently character Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.05 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01 Local range of values considered for breakpoint 'x': [-0.33, 0.36] t-values for two lines at 'x' = 0.01: t1 = 6.5 t2 = 9.35 We compute t2/(t1+t2) = 0.59 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.59) = 0.05 and set that value, 0.05, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.05 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01 Local range of values considered for breakpoint 'x': [-0.33, 0.36] t-values for two lines at 'x' = 0.01: t1 = 6.5 t2 = 9.35 We compute t2/(t1+t2) = 0.59 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.59) = 0.05 and set that value, 0.05, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.22 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.12 Local range of values considered for breakpoint 'x': [-0.22, 0.49] t-values for two lines at 'x' = 0.12: t1 = 2.92 t2 = 10.2 We compute t2/(t1+t2) = 0.78 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.78) = 0.22 and set that value, 0.22, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.22 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.12 Local range of values considered for breakpoint 'x': [-0.22, 0.49] t-values for two lines at 'x' = 0.12: t1 = 2.92 t2 = 10.2 We compute t2/(t1+t2) = 0.78 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.78) = 0.22 and set that value, 0.22, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.07 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01 Local range of values considered for breakpoint 'x': [-0.41, 0.38] t-values for two lines at 'x' = 0.01: t1 = 5.51 t2 = 7.83 We compute t2/(t1+t2) = 0.59 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.59) = 0.07 and set that value, 0.07, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.1 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.16 Local range of values considered for breakpoint 'x': [-0.21, 0.42] t-values for two lines at 'x' = 0.16: t1 = 7.8 t2 = 7.95 We compute t2/(t1+t2) = 0.5 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.5) = 0.1 and set that value, 0.1, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.05 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0 Local range of values considered for breakpoint 'x': [-0.32, 0.41] t-values for two lines at 'x' = 0: t1 = 8.25 t2 = 7.01 We compute t2/(t1+t2) = 0.46 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.46) = 0.05 and set that value, 0.05, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.08 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01 Local range of values considered for breakpoint 'x': [-0.17, 0.2] t-values for two lines at 'x' = 0.01: t1 = 8.45 t2 = 13.54 We compute t2/(t1+t2) = 0.62 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.62) = 0.08 and set that value, 0.08, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE` Robin Hood calculations (see Simonsohn, 2018) Explaining why x = 0.11 is used as the breakpoint Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.18 Local range of values considered for breakpoint 'x': [0, 0.32] t-values for two lines at 'x' = 0.18: t1 = 11.61 t2 = 8.11 We compute t2/(t1+t2) = 0.41 We then lookup the value of 'x' at that quantile, so we run quantile(x, 0.41) = 0.11 and set that value, 0.11, as the breakpoint. Note: you may turn off this message by setting `quiet=TRUE`Saving _problems/test-interprobe-761.R Saving _problems/test-interprobe-762.R Call: lm2(formula = mpg ~ wt, data = mtcars) estimate SE.robust SE.classical t.value p.value std.estimate intercept 37.29** 2.427 1.878 15.36 <.0001 -- wt -5.344** .738 .559 -7.241 <.0001 -.868 mean missing red.flag intercept -- -- ! wt 3.217 0 ! N = 32 | missing = 0 | df = 30 | R² = 0.753 | SE type: HC3 Note: to see explanations for lm2() output, run: `lm2_notes()` print() and summary() show the same information for lm2() print() and summary() show the same information for lm2() print() and summary() show the same information for lm2() print() and summary() show the same information for lm2() print() and summary() show the same information for lm2()The model reproduces the outcome exactly, so there is no residual variation left to estimate a variance from and every standard error for it is NA. What the variance calculation returns here is zero or rounding error, not precision. The model reproduces the outcome exactly, so there is no residual variation left to estimate a variance from and every standard error for it is NA. What the variance calculation returns here is zero or rounding error, not precision. The model reproduces the outcome exactly, so there is no residual variation left to estimate a variance from and every standard error for it is NA. What the variance calculation returns here is zero or rounding error, not precision. The model reproduces the outcome exactly, so there is no residual variation left to estimate a variance from and every standard error for it is NA. What the variance calculation returns here is zero or rounding error, not precision. Test message Testmessagewithmultipleparts Red message Blue message Cyan message Green message Custom color Plain text Bold text This stops This doesn't stop Part1andPart2 Value:42 Count:5items Test The p-values are based on quantile regressions that assume all observations are independent plot_cdf() says: dropped 10 observations with missing 'y' values plot_cdf() says: dropped 10 observations with missing 'y' values plot_cdf() says: dropped 10 observations with missing 'y' values plot_density() says: dropped 10 observations with missing 'y' values plot_density() says: dropped 10 observations with missing 'y' values plot_freq() says: dropped 1 observations with missing 'x' values plot_freq() says: dropped 1 observations with missing 'x' values plot_freq() says: dropped 2 observations with missing 'value' values plot_freq() says: dropped 2 observations with missing 'value' values tests: 1) A vs B: t(97.5)=-0.03, p=0.979 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) X_A vs X_B: t(96.9)=-1.10, p=0.274 2) Y_A vs Y_B: t(91.1)=-1.99, p=0.049 3) interaction(x1:x2): regression interaction: t(196)=0.45, p=0.654 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) A vs B: t(98.0)=-0.27, p=0.790 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) B vs A: t(98.0)=-0.27, p=0.790 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) B vs A: t(98.0)=-0.27, p=0.790 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. desc_var() says: Some possible group combinations are not observed: x1=B, x2=Y, x3=N tests: 1) A vs B: t(57.5)=2.14, p=0.037 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) X_A vs X_B: t(53.3)=0.44, p=0.661 2) Y_A vs Y_B: t(57.8)=-0.93, p=0.355 3) interaction(x1:x2): regression interaction: t(116)=0.94, p=0.348 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. tests: 1) X_A vs X_B: t(56.0)=0.44, p=0.662 2) Y_A vs Y_B: t(57.6)=-1.33, p=0.189 3) Z_A vs Z_B: t(58.0)=-0.45, p=0.655 You can specify which statistical tests to report with the `tests` argument. Set `quiet=TRUE` to suppress this output. status X Y group A 2 1 B 1 1 status X Y group A 2 1 B 1 1 1. Frequencies y X Y x A 2 1 B 1 1 2. Relative frequencies: by row y X Y Total x A .667 .333 1.000 B .500 .500 1.000 z = high y X Y x A 1 0 B 1 0 z = low y X Y x A 0 1 B 0 1 y X Y x A 1 1 B 1 1 Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Will conduct 20 resamples to estimate expected heterogeneity under null of homogeneity. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Cache for stimulus.plot() has been cleared. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Cache for stimulus.plot() has been cleared. Will run 20 resamples to compute confidence band Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Will conduct 20 resamples to estimate expected heterogeneity under null of homogeneity.... 20 Cache for stimulus.plot() has been cleared. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Will conduct 20 resamples to estimate expected heterogeneity under null of homogeneity. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Cache for stimulus.plot() has been cleared. stimulus.plot() says: 1 observations were dropped because of missing values. Will conduct 20 resamples to estimate expected heterogeneity under null of homogeneity. stimulus.plot() says: Estimating regression: m1=lm(rev~cond+factor(stimulus)+factor(id)) Clustering the standard errors: lmtest::coeftest(m1,vcov=sandwich::vcovCL,type='HC3',cluster=~id)Error in solve.default(diag(length(ij)) - Hij) : system is computationally singular: reciprocal condition number = 7.94341e-17 Couldn't estimate HC3, will try HC1 Clustering the standard errors: lmtest::coeftest(m1,vcov=sandwich::vcovCL,type='HC1',cluster=~id) Estimating random intercepts model: lmerTest::lmer(rev~cond+(1|stimulus)+(1|id)) Estimating random slopes model: lmerTest::lmer(rev~cond+(1+cond|stimulus)+(1|id)) x A B 3 2 Warning: some cells have less than 5 expected observations. This makes the chi-square approximation less accurate; the p-value is questionable.[ FAIL 2 | WARN 0 | SKIP 15 | PASS 1092 ] ══ Skipped tests (15) ══════════════════════════════════════════════════════════ • On CRAN (15): 'test-lm2.R:677:1', 'test-lm2.R:687:1', 'test-lm2.R:696:1', 'test-lm2.R:705:1', 'test-t.test2.R:390:1', 'test-t.test2.R:402:1', 'test-t.test2.R:415:1', 'test-t.test2.R:426:1', 'test-t.test2.R:436:1', 'test-t.test2.R:448:1', 'test-table2.R:386:1', 'test-table2.R:397:1', 'test-table2.R:408:1', 'test-table2.R:419:1', 'test-table2.R:430:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-interprobe.R:761:3'): binary x with default auto matches explicit moderator on x-axis ── Expected `res_auto$simple.slopes` to equal `res_true$simple.slopes`. Differences: actual vs expected std.error actual[2, ] 0.4806084 actual[3, ] 0.4480081 actual[4, ] 0.4159887 - actual[5, ] 0.3846911 + expected[5, ] 0.3846912 actual[6, ] 0.3543034 actual[7, ] 0.3250767 actual[8, ] 0.2973491 actual vs expected std.error actual[20, ] 0.2887660 actual[21, ] 0.3159174 actual[22, ] 0.3447009 - actual[23, ] 0.3747445 + expected[23, ] 0.3747444 actual[24, ] 0.4057714 actual[25, ] 0.4375752 actual[26, ] 0.4700011 actual vs expected std.error actual[27, ] 0.5029322 actual[28, ] 0.5362786 actual[29, ] 0.5699692 - actual[30, ] 0.6039474 + expected[30, ] 0.6039473 actual[31, ] 0.5850788 actual[32, ] 0.5470201 actual[33, ] 0.5093679 actual vs expected std.error actual[57, ] 0.5419925 actual[58, ] 0.5799948 actual[59, ] 0.6183395 - actual[60, ] 0.6569670 + expected[60, ] 0.6569671 actual$std.error | expected$std.error [1] 0.513681970 | 0.513681970 [1] [2] 0.480608398 - 0.480608375 [2] [3] 0.448008109 | 0.448008109 [3] [4] 0.415988715 - 0.415988713 [4] [5] 0.384691150 - 0.384691176 [5] [6] 0.354303365 - 0.354303363 [6] [7] 0.325076714 | 0.325076714 [7] [8] 0.297349085 - 0.297349099 [8] [9] 0.271575846 | 0.271575846 [9] [10] 0.248362267 - 0.248362266 [10] ... ... ... and 3 more ... actual$std.error | expected$std.error [14] 0.198252942 | 0.198252942 [14] [15] 0.200447135 | 0.200447135 [15] [16] 0.208905166 | 0.208905166 [16] [17] 0.222916798 - 0.222916810 [17] [18] 0.241519730 - 0.241519741 [18] [19] 0.263746907 | 0.263746907 [19] [20] 0.288766011 | 0.288766011 [20] [21] 0.315917385 - 0.315917387 [21] [22] 0.344700879 - 0.344700864 [22] [23] 0.374744457 - 0.374744424 [23] ... ... ... and 37 more ... ── Failure ('test-interprobe.R:762:3'): binary x with default auto matches explicit moderator on x-axis ── Expected `res_auto$johnson.neyman` to equal `res_true$johnson.neyman`. Differences: actual vs expected std.error conf.low - actual[1, ] 0.7785797 -3.16171676 + expected[1, ] 0.7785797 -3.16171677 - actual[2, ] 0.7281589 -2.87451077 + expected[2, ] 0.7281589 -2.87451074 - actual[3, ] 0.6783560 -2.58851587 + expected[3, ] 0.6783560 -2.58851589 - actual[4, ] 0.6293135 -2.30401125 + expected[4, ] 0.6293135 -2.30401129 - actual[5, ] 0.5812189 -2.02136416 + expected[5, ] 0.5812188 -2.02136415 - actual[6, ] 0.5343240 -1.74106735 + expected[6, ] 0.5343240 -1.74106736 - actual[7, ] 0.4889694 -1.46378716 + expected[7, ] 0.4889694 -1.46378717 - actual[8, ] 0.4456193 -1.19043274 + expected[8, ] 0.4456193 -1.19043276 actual[9, ] 0.4049126 -0.92225468 actual[10, ] 0.3677227 -0.66096337 and 20 more ... actual$std.error | expected$std.error [1] 0.778579711 - 0.778579718 [1] [2] 0.728158934 - 0.728158914 [2] [3] 0.678356027 - 0.678356037 [3] [4] 0.629313461 - 0.629313482 [4] [5] 0.581218851 - 0.581218847 [5] [6] 0.534323999 - 0.534324003 [6] [7] 0.488969358 - 0.488969360 [7] [8] 0.445619324 - 0.445619334 [8] [9] 0.404912615 | 0.404912615 [9] [10] 0.367722674 - 0.367722673 [10] ... ... ... and 20 more ... actual$conf.low | expected$conf.low [1] -3.161716759 - -3.161716772 [1] [2] -2.874510774 - -2.874510736 [2] [3] -2.588515869 - -2.588515888 [3] [4] -2.304011250 - -2.304011291 [4] [5] -2.021364161 - -2.021364154 [5] [6] -1.741067348 - -1.741067355 [6] [7] -1.463787163 - -1.463787168 [7] [8] -1.190432738 - -1.190432756 [8] [9] -0.922254681 | -0.922254681 [9] [10] -0.660963372 - -0.660963369 [10] ... ... ... and 20 more ... [ FAIL 2 | WARN 0 | SKIP 15 | PASS 1092 ] Error: ! Test failures. Execution halted * 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/statuser.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 6:57.97, 160.01 + 10.96