* using log directory ‘/data/gannet/ripley/R/packages/tests-gcc-SAN/VIM.Rcheck’ * using R Under development (unstable) (2025-12-05 r89105) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2) GNU Fortran (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2) * running under: Fedora Linux 42 (Workstation Edition) * using session charset: UTF-8 * using option ‘--no-stop-on-test-error’ * checking for file ‘VIM/DESCRIPTION’ ... OK * this is package ‘VIM’ version ‘6.2.6’ * package encoding: UTF-8 * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package ‘VIM’ can be installed ... [115s/110s] WARNING Found the following significant warnings: /data/gannet/ripley/R/gcc-SAN3/include/R_ext/Callbacks.h:32:2: warning: ‘#warning’ before C++23 is a GCC extension [-Wc++23-extensions] See ‘/data/gannet/ripley/R/packages/tests-gcc-SAN/VIM.Rcheck/00install.out’ for details. * used C++ compiler: ‘g++ (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)’ * checking package directory ... 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 compiled code ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... [61s/61s] ERROR Running examples in ‘VIM-Ex.R’ failed The error most likely occurred in: > ### Name: xgboostImpute > ### Title: Xgboost Imputation > ### Aliases: xgboostImpute > > ### ** Examples > > data(sleep) > xgboostImpute(Dream~BodyWgt+BrainWgt,data=sleep) Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger Dream_imp 1 6654.000 5712.00 NA 1.7996682 3.3 38.6 645.0 3 5 3 TRUE 2 1.000 6.60 6.3 2.0000000 8.3 4.5 42.0 3 1 3 FALSE 3 3.385 44.50 NA 3.5961127 12.5 14.0 60.0 1 1 1 TRUE 4 0.920 5.70 NA 0.8757253 16.5 NA 25.0 5 2 3 TRUE 5 2547.000 4603.00 2.1 1.8000000 3.9 69.0 624.0 3 5 4 FALSE 6 10.550 179.50 9.1 0.7000000 9.8 27.0 180.0 4 4 4 FALSE 7 0.023 0.30 15.8 3.9000000 19.7 19.0 35.0 1 1 1 FALSE 8 160.000 169.00 5.2 1.0000000 6.2 30.4 392.0 4 5 4 FALSE 9 3.300 25.60 10.9 3.6000000 14.5 28.0 63.0 1 2 1 FALSE 10 52.160 440.00 8.3 1.4000000 9.7 50.0 230.0 1 1 1 FALSE 11 0.425 6.40 11.0 1.5000000 12.5 7.0 112.0 5 4 4 FALSE 12 465.000 423.00 3.2 0.7000000 3.9 30.0 281.0 5 5 5 FALSE 13 0.550 2.40 7.6 2.7000000 10.3 NA NA 2 1 2 FALSE 14 187.100 419.00 NA 1.7214588 3.1 40.0 365.0 5 5 5 TRUE 15 0.075 1.20 6.3 2.1000000 8.4 3.5 42.0 1 1 1 FALSE 16 3.000 25.00 8.6 0.0000000 8.6 50.0 28.0 2 2 2 FALSE 17 0.785 3.50 6.6 4.1000000 10.7 6.0 42.0 2 2 2 FALSE 18 0.200 5.00 9.5 1.2000000 10.7 10.4 120.0 2 2 2 FALSE 19 1.410 17.50 4.8 1.3000000 6.1 34.0 NA 1 2 1 FALSE 20 60.000 81.00 12.0 6.1000000 18.1 7.0 NA 1 1 1 FALSE 21 529.000 680.00 NA 0.3000000 NA 28.0 400.0 5 5 5 FALSE 22 27.660 115.00 3.3 0.5000000 3.8 20.0 148.0 5 5 5 FALSE 23 0.120 1.00 11.0 3.4000000 14.4 3.9 16.0 3 1 2 FALSE 24 207.000 406.00 NA 1.8088160 12.0 39.3 252.0 1 4 1 TRUE 25 85.000 325.00 4.7 1.5000000 6.2 41.0 310.0 1 3 1 FALSE 26 36.330 119.50 NA 0.5030808 13.0 16.2 63.0 1 1 1 TRUE 27 0.101 4.00 10.4 3.4000000 13.8 9.0 28.0 5 1 3 FALSE 28 1.040 5.50 7.4 0.8000000 8.2 7.6 68.0 5 3 4 FALSE 29 521.000 655.00 2.1 0.8000000 2.9 46.0 336.0 5 5 5 FALSE 30 100.000 157.00 NA 1.0328215 10.8 22.4 100.0 1 1 1 TRUE 31 35.000 56.00 NA 4.6171999 NA 16.3 33.0 3 5 4 TRUE 32 0.005 0.14 7.7 1.4000000 9.1 2.6 21.5 5 2 4 FALSE 33 0.010 0.25 17.9 2.0000000 19.9 24.0 50.0 1 1 1 FALSE 34 62.000 1320.00 6.1 1.9000000 8.0 100.0 267.0 1 1 1 FALSE 35 0.122 3.00 8.2 2.4000000 10.6 NA 30.0 2 1 1 FALSE 36 1.350 8.10 8.4 2.8000000 11.2 NA 45.0 3 1 3 FALSE 37 0.023 0.40 11.9 1.3000000 13.2 3.2 19.0 4 1 3 FALSE 38 0.048 0.33 10.8 2.0000000 12.8 2.0 30.0 4 1 3 FALSE 39 1.700 6.30 13.8 5.6000000 19.4 5.0 12.0 2 1 1 FALSE 40 3.500 10.80 14.3 3.1000000 17.4 6.5 120.0 2 1 1 FALSE 41 250.000 490.00 NA 1.0000000 NA 23.6 440.0 5 5 5 FALSE 42 0.480 15.50 15.2 1.8000000 17.0 12.0 140.0 2 2 2 FALSE 43 10.000 115.00 10.0 0.9000000 10.9 20.2 170.0 4 4 4 FALSE 44 1.620 11.40 11.9 1.8000000 13.7 13.0 17.0 2 1 2 FALSE 45 192.000 180.00 6.5 1.9000000 8.4 27.0 115.0 4 4 4 FALSE 46 2.500 12.10 7.5 0.9000000 8.4 18.0 31.0 5 5 5 FALSE 47 4.288 39.20 NA 2.4006271 12.5 13.7 63.0 2 2 2 TRUE 48 0.280 1.90 10.6 2.6000000 13.2 4.7 21.0 3 1 3 FALSE 49 4.235 50.40 7.4 2.4000000 9.8 9.8 52.0 1 1 1 FALSE 50 6.800 179.00 8.4 1.2000000 9.6 29.0 164.0 2 3 2 FALSE 51 0.750 12.30 5.7 0.9000000 6.6 7.0 225.0 2 2 2 FALSE 52 3.600 21.00 4.9 0.5000000 5.4 6.0 225.0 3 2 3 FALSE 53 14.830 98.20 NA 3.7860343 2.6 17.0 150.0 5 5 5 TRUE 54 55.500 175.00 3.2 0.6000000 3.8 20.0 151.0 5 5 5 FALSE 55 1.400 12.50 NA 1.0766708 11.0 12.7 90.0 2 2 2 TRUE 56 0.060 1.00 8.1 2.2000000 10.3 3.5 NA 3 1 2 FALSE 57 0.900 2.60 11.0 2.3000000 13.3 4.5 60.0 2 1 2 FALSE 58 2.000 12.30 4.9 0.5000000 5.4 7.5 200.0 3 1 3 FALSE 59 0.104 2.50 13.2 2.6000000 15.8 2.3 46.0 3 2 2 FALSE 60 4.190 58.00 9.7 0.6000000 10.3 24.0 210.0 4 3 4 FALSE 61 3.500 3.90 12.8 6.6000000 19.4 3.0 14.0 2 1 1 FALSE 62 4.050 17.00 NA 0.4989381 NA 13.0 38.0 3 1 1 TRUE > xgboostImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep) Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger 1 6654.000 5712.00 2.100814 1.7996682 3.3 38.6 645.0 3 5 3 2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.0 3 1 3 3 3.385 44.50 10.897091 3.5961127 12.5 14.0 60.0 1 1 1 4 0.920 5.70 7.186167 0.8757253 16.5 NA 25.0 5 2 3 5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.0 3 5 4 6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.0 4 4 4 7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.0 1 1 1 8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.0 4 5 4 9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.0 1 2 1 10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.0 1 1 1 11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.0 5 4 4 12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.0 5 5 5 13 0.550 2.40 7.600000 2.7000000 10.3 NA NA 2 1 2 14 187.100 419.00 5.123134 1.7214588 3.1 40.0 365.0 5 5 5 15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.0 1 1 1 16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.0 2 2 2 17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.0 2 2 2 18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.0 2 2 2 19 1.410 17.50 4.800000 1.3000000 6.1 34.0 NA 1 2 1 20 60.000 81.00 12.000000 6.1000000 18.1 7.0 NA 1 1 1 21 529.000 680.00 2.100814 0.3000000 NA 28.0 400.0 5 5 5 22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.0 5 5 5 23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.0 3 1 2 24 207.000 406.00 6.285658 1.8088160 12.0 39.3 252.0 1 4 1 25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.0 1 3 1 26 36.330 119.50 3.301962 0.5030808 13.0 16.2 63.0 1 1 1 27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.0 5 1 3 28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.0 5 3 4 29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.0 5 5 5 30 100.000 157.00 4.835842 1.0328215 10.8 22.4 100.0 1 1 1 31 35.000 56.00 9.414713 4.6171999 NA 16.3 33.0 3 5 4 32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.5 5 2 4 33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.0 1 1 1 34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.0 1 1 1 35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.0 2 1 1 36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.0 3 1 3 37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.0 4 1 3 38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.0 4 1 3 39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.0 2 1 1 40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.0 2 1 1 41 250.000 490.00 6.742025 1.0000000 NA 23.6 440.0 5 5 5 42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.0 2 2 2 43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.0 4 4 4 44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.0 2 1 2 45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.0 4 4 4 46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.0 5 5 5 47 4.288 39.20 7.402267 2.4006271 12.5 13.7 63.0 2 2 2 48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.0 3 1 3 49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.0 1 1 1 50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.0 2 3 2 51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.0 2 2 2 52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.0 3 2 3 53 14.830 98.20 10.641701 3.7860343 2.6 17.0 150.0 5 5 5 54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.0 5 5 5 55 1.400 12.50 5.010159 1.0766708 11.0 12.7 90.0 2 2 2 56 0.060 1.00 8.100000 2.2000000 10.3 3.5 NA 3 1 2 57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.0 2 1 2 58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.0 3 1 3 59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.0 3 2 2 60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.0 4 3 4 61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.0 2 1 1 62 4.050 17.00 5.988167 0.4989381 NA 13.0 38.0 3 1 1 Dream_imp NonD_imp 1 TRUE TRUE 2 FALSE FALSE 3 TRUE TRUE 4 TRUE TRUE 5 FALSE FALSE 6 FALSE FALSE 7 FALSE FALSE 8 FALSE FALSE 9 FALSE FALSE 10 FALSE FALSE 11 FALSE FALSE 12 FALSE FALSE 13 FALSE FALSE 14 TRUE TRUE 15 FALSE FALSE 16 FALSE FALSE 17 FALSE FALSE 18 FALSE FALSE 19 FALSE FALSE 20 FALSE FALSE 21 FALSE TRUE 22 FALSE FALSE 23 FALSE FALSE 24 TRUE TRUE 25 FALSE FALSE 26 TRUE TRUE 27 FALSE FALSE 28 FALSE FALSE 29 FALSE FALSE 30 TRUE TRUE 31 TRUE TRUE 32 FALSE FALSE 33 FALSE FALSE 34 FALSE FALSE 35 FALSE FALSE 36 FALSE FALSE 37 FALSE FALSE 38 FALSE FALSE 39 FALSE FALSE 40 FALSE FALSE 41 FALSE TRUE 42 FALSE FALSE 43 FALSE FALSE 44 FALSE FALSE 45 FALSE FALSE 46 FALSE FALSE 47 TRUE TRUE 48 FALSE FALSE 49 FALSE FALSE 50 FALSE FALSE 51 FALSE FALSE 52 FALSE FALSE 53 TRUE TRUE 54 FALSE FALSE 55 TRUE TRUE 56 FALSE FALSE 57 FALSE FALSE 58 FALSE FALSE 59 FALSE FALSE 60 FALSE FALSE 61 FALSE FALSE 62 TRUE TRUE > xgboostImpute(Dream+NonD+Gest~BodyWgt+BrainWgt,data=sleep) Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger 1 6654.000 5712.00 2.100814 1.7996682 3.3 38.6 645.00000 3 5 3 2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.00000 3 1 3 3 3.385 44.50 10.897091 3.5961127 12.5 14.0 60.00000 1 1 1 4 0.920 5.70 7.186167 0.8757253 16.5 NA 25.00000 5 2 3 5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.00000 3 5 4 6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.00000 4 4 4 7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.00000 1 1 1 8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.00000 4 5 4 9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.00000 1 2 1 10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.00000 1 1 1 11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.00000 5 4 4 12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.00000 5 5 5 13 0.550 2.40 7.600000 2.7000000 10.3 NA 28.07713 2 1 2 14 187.100 419.00 5.123134 1.7214588 3.1 40.0 365.00000 5 5 5 15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.00000 1 1 1 16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.00000 2 2 2 17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.00000 2 2 2 18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.00000 2 2 2 19 1.410 17.50 4.800000 1.3000000 6.1 34.0 80.31452 1 2 1 20 60.000 81.00 12.000000 6.1000000 18.1 7.0 101.52599 1 1 1 21 529.000 680.00 2.100814 0.3000000 NA 28.0 400.00000 5 5 5 22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.00000 5 5 5 23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.00000 3 1 2 24 207.000 406.00 6.285658 1.8088160 12.0 39.3 252.00000 1 4 1 25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.00000 1 3 1 26 36.330 119.50 3.301962 0.5030808 13.0 16.2 63.00000 1 1 1 27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.00000 5 1 3 28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.00000 5 3 4 29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.00000 5 5 5 30 100.000 157.00 4.835842 1.0328215 10.8 22.4 100.00000 1 1 1 31 35.000 56.00 9.414713 4.6171999 NA 16.3 33.00000 3 5 4 32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.50000 5 2 4 33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.00000 1 1 1 34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.00000 1 1 1 35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.00000 2 1 1 36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.00000 3 1 3 37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.00000 4 1 3 38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.00000 4 1 3 39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.00000 2 1 1 40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.00000 2 1 1 41 250.000 490.00 6.742025 1.0000000 NA 23.6 440.00000 5 5 5 42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.00000 2 2 2 43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.00000 4 4 4 44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.00000 2 1 2 45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.00000 4 4 4 46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.00000 5 5 5 47 4.288 39.20 7.402267 2.4006271 12.5 13.7 63.00000 2 2 2 48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.00000 3 1 3 49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.00000 1 1 1 50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.00000 2 3 2 51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.00000 2 2 2 52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.00000 3 2 3 53 14.830 98.20 10.641701 3.7860343 2.6 17.0 150.00000 5 5 5 54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.00000 5 5 5 55 1.400 12.50 5.010159 1.0766708 11.0 12.7 90.00000 2 2 2 56 0.060 1.00 8.100000 2.2000000 10.3 3.5 22.96904 3 1 2 57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.00000 2 1 2 58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.00000 3 1 3 59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.00000 3 2 2 60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.00000 4 3 4 61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.00000 2 1 1 62 4.050 17.00 5.988167 0.4989381 NA 13.0 38.00000 3 1 1 Dream_imp NonD_imp Gest_imp 1 TRUE TRUE FALSE 2 FALSE FALSE FALSE 3 TRUE TRUE FALSE 4 TRUE TRUE FALSE 5 FALSE FALSE FALSE 6 FALSE FALSE FALSE 7 FALSE FALSE FALSE 8 FALSE FALSE FALSE 9 FALSE FALSE FALSE 10 FALSE FALSE FALSE 11 FALSE FALSE FALSE 12 FALSE FALSE FALSE 13 FALSE FALSE TRUE 14 TRUE TRUE FALSE 15 FALSE FALSE FALSE 16 FALSE FALSE FALSE 17 FALSE FALSE FALSE 18 FALSE FALSE FALSE 19 FALSE FALSE TRUE 20 FALSE FALSE TRUE 21 FALSE TRUE FALSE 22 FALSE FALSE FALSE 23 FALSE FALSE FALSE 24 TRUE TRUE FALSE 25 FALSE FALSE FALSE 26 TRUE TRUE FALSE 27 FALSE FALSE FALSE 28 FALSE FALSE FALSE 29 FALSE FALSE FALSE 30 TRUE TRUE FALSE 31 TRUE TRUE FALSE 32 FALSE FALSE FALSE 33 FALSE FALSE FALSE 34 FALSE FALSE FALSE 35 FALSE FALSE FALSE 36 FALSE FALSE FALSE 37 FALSE FALSE FALSE 38 FALSE FALSE FALSE 39 FALSE FALSE FALSE 40 FALSE FALSE FALSE 41 FALSE TRUE FALSE 42 FALSE FALSE FALSE 43 FALSE FALSE FALSE 44 FALSE FALSE FALSE 45 FALSE FALSE FALSE 46 FALSE FALSE FALSE 47 TRUE TRUE FALSE 48 FALSE FALSE FALSE 49 FALSE FALSE FALSE 50 FALSE FALSE FALSE 51 FALSE FALSE FALSE 52 FALSE FALSE FALSE 53 TRUE TRUE FALSE 54 FALSE FALSE FALSE 55 TRUE TRUE FALSE 56 FALSE FALSE TRUE 57 FALSE FALSE FALSE 58 FALSE FALSE FALSE 59 FALSE FALSE FALSE 60 FALSE FALSE FALSE 61 FALSE FALSE FALSE 62 TRUE TRUE FALSE > > sleepx <- sleep > sleepx$Pred <- as.factor(LETTERS[sleepx$Pred]) > sleepx$Pred[1] <- NA > xgboostImpute(Pred~BodyWgt+BrainWgt,data=sleepx) Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized), : Passed unrecognized parameters: num_class, verbose. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version. Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '", : Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version. Error in prescreen.objective(objective) : Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'. Calls: xgboostImpute -> -> prescreen.objective Execution halted * checking tests ... [69s/70s] ERROR Running ‘test_imputeRobust.R’ Running ‘tinytest.R’ [68s/68s] Running the tests in ‘tests/tinytest.R’ failed. Complete output: > if ( requireNamespace("tinytest", quietly=TRUE) ){ + tinytest::test_package("VIM") + } Loading required package: colorspace Loading required package: grid VIM is ready to use. Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues Attaching package: 'VIM' The following object is masked from 'package:datasets': sleep test_IRMI_ordered.R........... 0 tests test_IRMI_ordered.R........... 0 tests v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 test_IRMI_ordered.R........... 1 tests OK test_IRMI_ordered.R........... 2 tests OK v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 Start: AIC=1381.35 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1361.3 1379.3 - v2 1 1361.4 1379.4 - co 1 1361.5 1379.5 1361.3 1381.3 - m 1 1366.1 1384.1 - v1 1 1367.5 1385.5 - c 4 1374.9 1386.9 Step: AIC=1379.35 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1361.4 1377.4 - co 1 1361.5 1377.5 1361.3 1379.3 - m 1 1366.1 1382.1 - v1 1 1367.5 1383.5 - c 4 1375.0 1385.0 Step: AIC=1377.37 y ~ v1 + m + c + co Df Deviance AIC - co 1 1361.5 1375.5 1361.4 1377.4 - m 1 1366.2 1380.2 - v1 1 1367.5 1381.5 - c 4 1375.0 1383.0 Step: AIC=1375.54 y ~ v1 + m + c Df Deviance AIC 1361.5 1375.5 - m 1 1366.3 1378.3 - v1 1 1367.6 1379.6 - c 4 1375.2 1381.2 Start: AIC=1295.01 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1275.2 1293.2 - v2 1 1275.3 1293.3 - co 1 1275.9 1293.9 1275.0 1295.0 - m 1 1297.2 1315.2 - v1 1 1315.5 1333.5 - c 4 1326.7 1338.7 Step: AIC=1293.15 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1275.5 1291.5 - co 1 1276.1 1292.1 1275.2 1293.2 - m 1 1297.3 1313.3 - v1 1 1315.5 1331.5 - c 4 1327.0 1337.0 Step: AIC=1291.48 y ~ v1 + m + c + co Df Deviance AIC - co 1 1276.4 1290.4 1275.5 1291.5 - m 1 1297.8 1311.8 - v1 1 1315.7 1329.7 - c 4 1327.5 1335.5 Step: AIC=1290.37 y ~ v1 + m + c Df Deviance AIC 1276.4 1290.4 - m 1 1298.6 1310.6 - v1 1 1316.0 1328.0 - c 4 1328.5 1334.5 Start: AIC=1291.05 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1271.2 1289.2 - v2 1 1271.6 1289.6 - co 1 1271.9 1289.9 1271.0 1291.0 - m 1 1292.3 1310.3 - v1 1 1316.2 1334.2 - c 4 1322.6 1334.6 Step: AIC=1289.16 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1271.7 1287.7 - co 1 1272.0 1288.0 1271.2 1289.2 - m 1 1292.4 1308.4 - v1 1 1316.2 1332.2 - c 4 1322.8 1332.8 Step: AIC=1287.72 y ~ v1 + m + c + co Df Deviance AIC - co 1 1272.5 1286.5 1271.7 1287.7 - m 1 1293.2 1307.2 - v1 1 1316.5 1330.5 - c 4 1323.7 1331.7 Step: AIC=1286.53 y ~ v1 + m + c Df Deviance AIC 1272.5 1286.5 - m 1 1293.9 1305.9 - v1 1 1316.7 1328.7 - c 4 1324.7 1330.7 Start: AIC=1289.76 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1269.8 1287.8 - v2 1 1270.3 1288.3 - co 1 1270.4 1288.4 1269.8 1289.8 - m 1 1289.4 1307.4 - v1 1 1314.2 1332.2 - c 4 1324.7 1336.7 Step: AIC=1287.76 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1270.3 1286.3 - co 1 1270.4 1286.4 1269.8 1287.8 - m 1 1289.4 1305.4 - v1 1 1314.3 1330.3 - c 4 1324.7 1334.7 Step: AIC=1286.33 y ~ v1 + m + c + co Df Deviance AIC - co 1 1270.9 1284.9 1270.3 1286.3 - m 1 1290.2 1304.2 - v1 1 1314.6 1328.6 - c 4 1325.6 1333.6 Step: AIC=1284.92 y ~ v1 + m + c Df Deviance AIC 1270.9 1284.9 - m 1 1290.7 1302.7 - v1 1 1314.7 1326.7 - c 4 1326.4 1332.4 Start: AIC=1289.28 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1269.3 1287.3 - co 1 1269.8 1287.8 - v2 1 1270.1 1288.1 1269.3 1289.3 - m 1 1287.3 1305.3 - v1 1 1314.1 1332.1 - c 4 1325.2 1337.2 Step: AIC=1287.3 y ~ v1 + v2 + m + c + co Df Deviance AIC - co 1 1269.9 1285.9 - v2 1 1270.1 1286.1 1269.3 1287.3 - m 1 1287.4 1303.4 - v1 1 1314.2 1330.2 - c 4 1325.2 1335.2 Step: AIC=1285.86 y ~ v1 + v2 + m + c Df Deviance AIC - v2 1 1270.7 1284.7 1269.9 1285.9 - m 1 1287.8 1301.8 - v1 1 1314.2 1328.2 - c 4 1326.0 1334.0 Step: AIC=1284.67 y ~ v1 + m + c Df Deviance AIC 1270.7 1284.7 - m 1 1288.9 1300.9 - v1 1 1314.8 1326.8 - c 4 1327.2 1333.2 test_IRMI_ordered.R........... 3 tests OK test_IRMI_ordered.R........... 4 tests OK v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 Start: AIC=1381.35 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1361.3 1379.3 - v2 1 1361.4 1379.4 - co 1 1361.5 1379.5 1361.3 1381.3 - m 1 1366.1 1384.1 - v1 1 1367.5 1385.5 - c 4 1374.9 1386.9 Step: AIC=1379.35 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1361.4 1377.4 - co 1 1361.5 1377.5 1361.3 1379.3 - m 1 1366.1 1382.1 - v1 1 1367.5 1383.5 - c 4 1375.0 1385.0 Step: AIC=1377.37 y ~ v1 + m + c + co Df Deviance AIC - co 1 1361.5 1375.5 1361.4 1377.4 - m 1 1366.2 1380.2 - v1 1 1367.5 1381.5 - c 4 1375.0 1383.0 Step: AIC=1375.54 y ~ v1 + m + c Df Deviance AIC 1361.5 1375.5 - m 1 1366.3 1378.3 - v1 1 1367.6 1379.6 - c 4 1375.2 1381.2 Start: AIC=1295.01 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1275.2 1293.2 - v2 1 1275.3 1293.3 - co 1 1275.9 1293.9 1275.0 1295.0 - m 1 1297.2 1315.2 - v1 1 1315.5 1333.5 - c 4 1326.7 1338.7 Step: AIC=1293.15 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1275.5 1291.5 - co 1 1276.1 1292.1 1275.2 1293.2 - m 1 1297.3 1313.3 - v1 1 1315.5 1331.5 - c 4 1327.0 1337.0 Step: AIC=1291.48 y ~ v1 + m + c + co Df Deviance AIC - co 1 1276.4 1290.4 1275.5 1291.5 - m 1 1297.8 1311.8 - v1 1 1315.7 1329.7 - c 4 1327.5 1335.5 Step: AIC=1290.37 y ~ v1 + m + c Df Deviance AIC 1276.4 1290.4 - m 1 1298.6 1310.6 - v1 1 1316.0 1328.0 - c 4 1328.5 1334.5 Start: AIC=1291.05 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1271.2 1289.2 - v2 1 1271.6 1289.6 - co 1 1271.9 1289.9 1271.0 1291.0 - m 1 1292.3 1310.3 - v1 1 1316.2 1334.2 - c 4 1322.6 1334.6 Step: AIC=1289.16 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1271.7 1287.7 - co 1 1272.0 1288.0 1271.2 1289.2 - m 1 1292.4 1308.4 - v1 1 1316.2 1332.2 - c 4 1322.8 1332.8 Step: AIC=1287.72 y ~ v1 + m + c + co Df Deviance AIC - co 1 1272.5 1286.5 1271.7 1287.7 - m 1 1293.2 1307.2 - v1 1 1316.5 1330.5 - c 4 1323.7 1331.7 Step: AIC=1286.53 y ~ v1 + m + c Df Deviance AIC 1272.5 1286.5 - m 1 1293.9 1305.9 - v1 1 1316.7 1328.7 - c 4 1324.7 1330.7 Start: AIC=1289.76 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1269.8 1287.8 - v2 1 1270.3 1288.3 - co 1 1270.4 1288.4 1269.8 1289.8 - m 1 1289.4 1307.4 - v1 1 1314.2 1332.2 - c 4 1324.7 1336.7 Step: AIC=1287.76 y ~ v1 + v2 + m + c + co Df Deviance AIC - v2 1 1270.3 1286.3 - co 1 1270.4 1286.4 1269.8 1287.8 - m 1 1289.4 1305.4 - v1 1 1314.3 1330.3 - c 4 1324.7 1334.7 Step: AIC=1286.33 y ~ v1 + m + c + co Df Deviance AIC - co 1 1270.9 1284.9 1270.3 1286.3 - m 1 1290.2 1304.2 - v1 1 1314.6 1328.6 - c 4 1325.6 1333.6 Step: AIC=1284.92 y ~ v1 + m + c Df Deviance AIC 1270.9 1284.9 - m 1 1290.7 1302.7 - v1 1 1314.7 1326.7 - c 4 1326.4 1332.4 Start: AIC=1289.28 y ~ v1 + v2 + m + b + c + co Df Deviance AIC - b 1 1269.3 1287.3 - co 1 1269.8 1287.8 - v2 1 1270.1 1288.1 1269.3 1289.3 - m 1 1287.3 1305.3 - v1 1 1314.1 1332.1 - c 4 1325.2 1337.2 Step: AIC=1287.3 y ~ v1 + v2 + m + c + co Df Deviance AIC - co 1 1269.9 1285.9 - v2 1 1270.1 1286.1 1269.3 1287.3 - m 1 1287.4 1303.4 - v1 1 1314.2 1330.2 - c 4 1325.2 1335.2 Step: AIC=1285.86 y ~ v1 + v2 + m + c Df Deviance AIC - v2 1 1270.7 1284.7 1269.9 1285.9 - m 1 1287.8 1301.8 - v1 1 1314.2 1328.2 - c 4 1326.0 1334.0 Step: AIC=1284.67 y ~ v1 + m + c Df Deviance AIC 1270.7 1284.7 - m 1 1288.9 1300.9 - v1 1 1314.8 1326.8 - c 4 1327.2 1333.2 test_IRMI_ordered.R........... 5 tests OK test_IRMI_ordered.R........... 6 tests OK v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 test_IRMI_ordered.R........... 7 tests OK test_IRMI_ordered.R........... 8 tests OK test_IRMI_ordered.R........... 9 tests OK v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 test_IRMI_ordered.R........... 10 tests OK test_IRMI_ordered.R........... 11 tests OK v1 v2 co v1 v2 co -2.948811 -2.994395 1.000000 3.012030 3.327006 21.000000 test_IRMI_ordered.R........... 12 tests OK test_IRMI_ordered.R........... 13 tests OK 18.0s test_aggFunctions.R........... 0 tests kNN ordered test_aggFunctions.R........... 0 tests test_aggFunctions.R........... 0 tests test_aggFunctions.R........... 0 tests test_aggFunctions.R........... 0 tests test_aggFunctions.R........... 0 tests test_aggFunctions.R........... 1 tests OK test_aggFunctions.R........... 2 tests OK test_aggFunctions.R........... 3 tests OK test_aggFunctions.R........... 4 tests OK test_aggFunctions.R........... 5 tests OK test_aggFunctions.R........... 6 tests OK 56ms Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union test_data_frame.R............. 0 tests test_data_frame.R............. 0 tests b c b c 1 1 5 4 a c a c 1 1 5 4 a b a b 1 1 5 5 test_data_frame.R............. 0 tests b c b c 1 1 5 4 a c a c 1 1 5 4 a b a b 1 1 5 5 test_data_frame.R............. 0 tests test_data_frame.R............. 1 tests OK test_data_frame.R............. 1 tests OK test_data_frame.R............. 1 tests OK test_data_frame.R............. 2 tests OK b c b c 1 1 5 4 a c a c 1 1 5 4 a b a b 1 1 5 5 test_data_frame.R............. 2 tests OK b c b c 1 1 5 4 a c a c 1 1 5 4 a b a b 1 1 5 5 test_data_frame.R............. 2 tests OK test_data_frame.R............. 3 tests OK 3.4s test_gowerDind.R.............. 0 tests test_gowerDind.R.............. 0 tests x y x y -2.130349 -2.271632 2.658793 2.642011 test_gowerDind.R.............. 0 tests test_gowerDind.R.............. 0 tests x y x y -2.130349 -2.271632 2.658793 2.642011 test_gowerDind.R.............. 0 tests test_gowerDind.R.............. 0 tests test_gowerDind.R.............. 1 tests OK test_gowerDind.R.............. 1 tests OK test_gowerDind.R.............. 1 tests OK test_gowerDind.R.............. 1 tests OK test_gowerDind.R.............. 1 tests OK test_gowerDind.R.............. 2 tests OK 0.2s test_graphics.R............... 0 tests test_graphics.R............... 0 tests test_graphics.R............... 0 tests test_graphics.R............... 0 tests test_graphics.R............... 0 tests test_graphics.R............... 0 tests test_graphics.R............... 0 tests Missings in variables: Variable Count NonD 14 Dream 12 Sleep 4 Span 4 Gest 4 test_graphics.R............... 0 tests test_graphics.R............... 1 tests OK BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp 0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred 1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp 0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred 1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Span Gest Pred Exp 0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Span Gest Pred 1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp 0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred 1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp 0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred 1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000 Exp Danger 5.000 5.000 test_graphics.R............... 1 tests OK test_graphics.R............... 1 tests OK test_graphics.R............... 1 tests OK Imputed missings in variables: Variable Count NonD 14 Dream 12 Sleep 4 Span 4 Gest 4 test_graphics.R............... 1 tests OK test_graphics.R............... 2 tests OK test_graphics.R............... 2 tests OK test_graphics.R............... 2 tests OK test_graphics.R............... 3 tests OK Exp Exp 1 5 test_graphics.R............... 3 tests OK test_graphics.R............... 3 tests OK test_graphics.R............... 3 tests OK test_graphics.R............... 4 tests OK test_graphics.R............... 4 tests OK test_graphics.R............... 5 tests OK Ca Bi Ca Bi 1.10e+02 6.00e-03 4.17e+04 3.89e+00 Ca As Ca As 110.0 0.1 41700.0 30.7 test_graphics.R............... 5 tests OK test_graphics.R............... 6 tests OK test_graphics.R............... 6 tests OK test_graphics.R............... 6 tests OK test_graphics.R............... 6 tests OK test_graphics.R............... 7 tests OK Humidity Humidity 71.6 94.8 Air.Temp Air.Temp 21.42 28.50 test_graphics.R............... 7 tests OK test_graphics.R............... 7 tests OK test_graphics.R............... 7 tests OK test_graphics.R............... 8 tests OK test_graphics.R............... 8 tests OK test_graphics.R............... 9 tests OK Bi Bi 0.006 3.890 As As 0.1 30.7 test_graphics.R............... 9 tests OK test_graphics.R............... 10 tests OK test_graphics.R............... 10 tests OK test_graphics.R............... 10 tests OK test_graphics.R............... 11 tests OK BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep 0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900 BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep 0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900 BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream 0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600 test_graphics.R............... 11 tests OK test_graphics.R............... 11 tests OK test_graphics.R............... 12 tests OK test_graphics.R............... 12 tests OK test_graphics.R............... 12 tests OK test_graphics.R............... 13 tests OK BodyWgt BrainWgt Dream Sleep Span Gest BodyWgt BrainWgt 0.005 0.140 0.000 2.600 2.000 12.000 6654.000 5712.000 Dream Sleep Span Gest 6.600 19.900 100.000 645.000 BodyWgt BrainWgt NonD Sleep Span Gest BodyWgt BrainWgt 0.005 0.140 2.100 2.600 2.000 12.000 6654.000 5712.000 NonD Sleep Span Gest 17.900 19.900 100.000 645.000 BodyWgt BrainWgt NonD Dream Span Gest BodyWgt BrainWgt 0.005 0.140 2.100 0.000 2.000 12.000 6654.000 5712.000 NonD Dream Span Gest 17.900 6.600 100.000 645.000 BodyWgt BrainWgt NonD Dream Sleep Gest BodyWgt BrainWgt 0.005 0.140 2.100 0.000 2.600 12.000 6654.000 5712.000 NonD Dream Sleep Gest 17.900 6.600 19.900 645.000 BodyWgt BrainWgt NonD Dream Sleep Span BodyWgt BrainWgt 0.005 0.140 2.100 0.000 2.600 2.000 6654.000 5712.000 NonD Dream Sleep Span 17.900 6.600 19.900 100.000 test_graphics.R............... 13 tests OK test_graphics.R............... 13 tests OK test_graphics.R............... 14 tests OK test_graphics.R............... 14 tests OK test_graphics.R............... 15 tests OK BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp 0.005 0.140 0.000 2.600 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred 1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp 0.005 0.140 2.100 2.600 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred 1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Span Gest Pred Exp 0.005 0.140 2.100 0.000 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Span Gest Pred 1.000 6654.000 5712.000 17.900 6.600 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp 0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred 1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Sleep Span Pred Exp 0.005 0.140 2.100 0.000 2.600 2.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Sleep Span Pred 1.000 6654.000 5712.000 17.900 6.600 19.900 100.000 5.000 Exp Danger 5.000 5.000 test_graphics.R............... 15 tests OK test_graphics.R............... 16 tests OK test_graphics.R............... 17 tests OK Al_XRF Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF Al_XRF 2.920 0.030 0.590 0.360 0.120 0.015 0.080 0.004 17.050 0.053 12.080 Ca_XRF Fe_XRF K_XRF Mg_XRF Mn_XRF Na_XRF P_XRF Si_XRF Ti_XRF 6.760 12.350 5.240 7.320 0.356 4.870 0.589 40.270 1.900 test_graphics.R............... 17 tests OK test_graphics.R............... 18 tests OK test_graphics.R............... 19 tests OK Humidity Humidity 71.6 94.8 Air.Temp Air.Temp 21.42 28.50 test_graphics.R............... 20 tests OK test_graphics.R............... 21 tests OK Humidity Humidity 71.6 94.8 Air.Temp Air.Temp 21.42 28.50 test_graphics.R............... 22 tests OK test_graphics.R............... 22 tests OK test_graphics.R............... 22 tests OK test_graphics.R............... 23 tests OK BodyWgt BrainWgt Dream Sleep BodyWgt BrainWgt Dream Sleep 0.005 0.140 0.000 2.600 6654.000 5712.000 6.600 19.900 BodyWgt BrainWgt NonD Sleep BodyWgt BrainWgt NonD Sleep 0.005 0.140 2.100 2.600 6654.000 5712.000 17.900 19.900 BodyWgt BrainWgt NonD Dream BodyWgt BrainWgt NonD Dream 0.005 0.140 2.100 0.000 6654.000 5712.000 17.900 6.600 test_graphics.R............... 23 tests OK test_graphics.R............... 23 tests OK test_graphics.R............... 24 tests OK test_graphics.R............... 24 tests OK test_graphics.R............... 24 tests OK test_graphics.R............... 25 tests OK Humidity Humidity 71.6 94.8 Air.Temp Air.Temp 21.42 28.50 test_graphics.R............... 25 tests OK Exp Exp 1 5 test_graphics.R............... 25 tests OK test_graphics.R............... 26 tests OK test_graphics.R............... 27 tests OK test_graphics.R............... 28 tests OK Humidity Humidity 71.6 94.8 Air.Temp Air.Temp 21.42 28.50 test_graphics.R............... 29 tests OK CaO CaO -1.3010300 0.9758911 test_graphics.R............... 30 tests OK test_graphics.R............... 30 tests OK test_graphics.R............... 31 tests OK test_graphics.R............... 31 tests OK test_graphics.R............... 32 tests OK 21.1s hotdeck test_hotdeck.R................ 0 tests Attaching package: 'data.table' The following objects are masked from 'package:dplyr': between, first, last The following object is masked from 'package:base': %notin% test_hotdeck.R................ 0 tests test_hotdeck.R................ 0 tests test_hotdeck.R................ 0 tests test_hotdeck.R................ 0 tests test_hotdeck.R................ 0 tests test_hotdeck.R................ 1 tests OK test_hotdeck.R................ 1 tests OK test_hotdeck.R................ 1 tests OK test_hotdeck.R................ 2 tests OK test_hotdeck.R................ 2 tests OK test_hotdeck.R................ 3 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 4 tests OK test_hotdeck.R................ 5 tests OK test_hotdeck.R................ 5 tests OK test_hotdeck.R................ 6 tests OK test_hotdeck.R................ 6 tests OK test_hotdeck.R................ 7 tests OK test_hotdeck.R................ 7 tests OK test_hotdeck.R................ 8 tests OK test_hotdeck.R................ 8 tests OK test_hotdeck.R................ 9 tests OK test_hotdeck.R................ 10 tests OK test_hotdeck.R................ 10 tests OK test_hotdeck.R................ 10 tests OK test_hotdeck.R................ 11 tests OK test_hotdeck.R................ 12 tests OK test_hotdeck.R................ 13 tests OK test_hotdeck.R................ 14 tests OK test_hotdeck.R................ 15 tests OK test_hotdeck.R................ 15 tests OK test_hotdeck.R................ 15 tests OK test_hotdeck.R................ 16 tests OK test_hotdeck.R................ 17 tests OK test_hotdeck.R................ 18 tests OK test_hotdeck.R................ 19 tests OK 2.1s test_impNA.R.................. 0 tests test_impNA.R.................. 0 tests test_impNA.R.................. 0 tests test_impNA.R.................. 0 tests BodyWgt BrainWgt Dream Sleep Span Gest Pred Exp 0.005 0.140 0.000 2.900 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt Dream Sleep Span Gest Pred 1.000 6654.000 5712.000 6.600 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Sleep Span Gest Pred Exp 0.005 0.140 2.100 2.900 2.000 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Sleep Span Gest Pred 1.000 6654.000 5712.000 17.900 19.900 100.000 645.000 5.000 Exp Danger 5.000 5.000 BodyWgt BrainWgt NonD Dream Sleep Gest Pred Exp 0.005 0.140 2.100 0.000 2.600 12.000 1.000 1.000 Danger BodyWgt BrainWgt NonD Dream Sleep Gest Pred 1.000 6654.000 5712.000 17.900 6.600 19.900 645.000 5.000 Exp Danger 5.000 5.000 test_impNA.R.................. 0 tests test_impNA.R.................. 1 tests OK test_impNA.R.................. 2 tests OK test_impNA.R.................. 2 tests OK test_impNA.R.................. 3 tests OK test_impNA.R.................. 4 tests OK 0.7s impPCA test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests test_impPCA.R................. 0 tests Iterations: 4 test_impPCA.R................. 0 tests test_impPCA.R................. 1 tests OK Iterations: 4 test_impPCA.R................. 1 tests OK test_impPCA.R................. 2 tests OK Iterations: 0 test_impPCA.R................. 2 tests OK test_impPCA.R................. 3 tests OK Iterations: 0 test_impPCA.R................. 3 tests OK test_impPCA.R................. 4 tests OK 0.3s test_irmi_types.R............. 0 tests z z -0.3308959 2.0121804 test_irmi_types.R............. 0 tests test_irmi_types.R............. 1 tests OK test_irmi_types.R............. 2 tests OK test_irmi_types.R............. 2 tests OK test_irmi_types.R............. 2 tests OK test_irmi_types.R............. 2 tests OK test_irmi_types.R............. 2 tests OK test_irmi_types.R............. 3 tests OK test_irmi_types.R............. 4 tests OK test_irmi_types.R............. 4 tests OK test_irmi_types.R............. 4 tests OK test_irmi_types.R............. 4 tests OK test_irmi_types.R............. 5 tests OK test_irmi_types.R............. 6 tests OK test_irmi_types.R............. 6 tests OK test_irmi_types.R............. 6 tests OK num1 num2 num3 num1 num2 num3 -3.087610 -4.001394 -3.237928 3.349508 3.615635 2.820386 test_irmi_types.R............. 6 tests OK test_irmi_types.R............. 7 tests OK test_irmi_types.R............. 8 tests OK 2.6s test_kNN.R.................... 0 tests kNN general test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests y y 1 6 test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests y y 1 6 test_kNN.R.................... 0 tests test_kNN.R.................... 0 tests test_kNN.R.................... 1 tests OK test_kNN.R.................... 1 tests OK test_kNN.R.................... 1 tests OK Detected as categorical variable: x,x_imp,y_imp Detected as ordinal variable: Detected as numerical variable: y 0 items ofvariable:x imputed 6items ofvariable:y imputed Time difference of 0.07133508 secs test_kNN.R.................... 1 tests OK test_kNN.R.................... 2 tests OK test_kNN.R.................... 2 tests OK test_kNN.R.................... 2 tests OK test_kNN.R.................... 2 tests OK y z 1.000000 1.000000 RandomVariableForImputation y -1.372898 6.000000 z RandomVariableForImputation 6.000000 2.212962 z RandomVariableForImputation 1.000000 -1.372898 z RandomVariableForImputation 6.000000 2.212962 y z 1.000000 1.000000 RandomVariableForImputation y -1.372898 6.000000 z RandomVariableForImputation 6.000000 2.212962 test_kNN.R.................... 2 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK test_kNN.R.................... 3 tests OK y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.5949014 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 z y2 1.0000000 1.0000000 z2 m2 1.0000000 -0.2369185 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.5949014 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.5949014 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.5949014 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 z2 m2 1.0000000 -0.2369185 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.5949014 y z 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.5949014 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.5949014 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 z23 -0.2369185 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.5949014 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 m2 z23 1.0393184 6.0000000 m23 RandomVariableForImputation 1.0393184 2.0511976 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 RandomVariableForImputation 1.0000000 -1.5949014 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 m2 y23 1.0393184 6.0000000 z23 RandomVariableForImputation 6.0000000 2.0511976 test_kNN.R.................... 3 tests OK test_kNN.R.................... 4 tests OK test_kNN.R.................... 4 tests OK z z 1 6 test_kNN.R.................... 4 tests OK test_kNN.R.................... 5 tests OK test_kNN.R.................... 5 tests OK z z 1 6 test_kNN.R.................... 5 tests OK test_kNN.R.................... 6 tests OK test_kNN.R.................... 7 tests OK test_kNN.R.................... 7 tests OK test_kNN.R.................... 8 tests OK test_kNN.R.................... 9 tests OK test_kNN.R.................... 9 tests OK test_kNN.R.................... 9 tests OK z z 1 6 y y 1 6 test_kNN.R.................... 9 tests OK test_kNN.R.................... 10 tests OK test_kNN.R.................... 11 tests OK z z 1 6 y y 1 5 test_kNN.R.................... 11 tests OK test_kNN.R.................... 12 tests OK test_kNN.R.................... 13 tests OK test_kNN.R.................... 13 tests OK test_kNN.R.................... 13 tests OK test_kNN.R.................... 13 tests OK y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 z m z m 1.0000000 -0.2369185 6.0000000 1.0393184 y z y z 1 1 6 6 y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 y z m 1.0000000 1.0000000 -0.2369185 yrandomForestFeature y z 1.9946333 6.0000000 6.0000000 m yrandomForestFeature 1.0393184 4.8171000 y z m 1.00000000 1.00000000 -0.23691848 mrandomForestFeature y z -0.03045094 6.00000000 6.00000000 m mrandomForestFeature 1.03931837 0.90111834 test_kNN.R.................... 13 tests OK test_kNN.R.................... 14 tests OK test_kNN.R.................... 15 tests OK test_kNN.R.................... 15 tests OK test_kNN.R.................... 15 tests OK test_kNN.R.................... 15 tests OK y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 z m z m 1.0000000 -0.2369185 6.0000000 1.0393184 y z y z 1 1 6 6 yrandomForestFeature yrandomForestFeature 2.082400 4.727667 mrandomForestFeature mrandomForestFeature -0.05045148 0.87127575 test_kNN.R.................... 15 tests OK test_kNN.R.................... 16 tests OK test_kNN.R.................... 17 tests OK test_kNN.R.................... 17 tests OK test_kNN.R.................... 17 tests OK z y z y 1 1 6 6 z z 1 6 z z 1 6 z yrandomForestFeature z 1.000000 2.032967 6.000000 yrandomForestFeature 4.985967 test_kNN.R.................... 17 tests OK test_kNN.R.................... 18 tests OK test_kNN.R.................... 19 tests OK test_kNN.R.................... 19 tests OK test_kNN.R.................... 19 tests OK z y z y 1 1 6 6 z z 1 6 z z 1 6 z yrandomForestFeature z 1.000000 1.902000 6.000000 yrandomForestFeature 4.915067 test_kNN.R.................... 19 tests OK test_kNN.R.................... 20 tests OK test_kNN.R.................... 21 tests OK test_kNN.R.................... 21 tests OK test_kNN.R.................... 21 tests OK y y 1 6 test_kNN.R.................... 21 tests OK test_kNN.R.................... 21 tests OK y y 1 6 test_kNN.R.................... 21 tests OK test_kNN.R.................... 21 tests OK test_kNN.R.................... 22 tests OK test_kNN.R.................... 22 tests OK test_kNN.R.................... 22 tests OK Detected as categorical variable: x,x_imp,y_imp Detected as ordinal variable: Detected as numerical variable: y 0 items ofvariable:x imputed 6items ofvariable:y imputed Time difference of 0.07389569 secs test_kNN.R.................... 22 tests OK test_kNN.R.................... 23 tests OK test_kNN.R.................... 23 tests OK test_kNN.R.................... 23 tests OK test_kNN.R.................... 23 tests OK y z 1.000000 1.000000 RandomVariableForImputation y -1.130797 6.000000 z RandomVariableForImputation 6.000000 1.380325 z RandomVariableForImputation 1.000000 -1.130797 z RandomVariableForImputation 6.000000 1.380325 y z 1.000000 1.000000 RandomVariableForImputation y -1.130797 6.000000 z RandomVariableForImputation 6.000000 1.380325 test_kNN.R.................... 23 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK test_kNN.R.................... 24 tests OK y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.6220983 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 z y2 1.0000000 1.0000000 z2 m2 1.0000000 -0.2369185 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.6220983 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.6220983 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.6220983 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 z2 m2 1.0000000 -0.2369185 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.6220983 y z 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 y23 z23 1.0000000 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.6220983 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 m23 1.0000000 -0.2369185 RandomVariableForImputation y -1.6220983 6.0000000 z y2 6.0000000 6.0000000 z2 m2 6.0000000 1.0393184 y23 z23 6.0000000 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 z23 -0.2369185 1.0000000 m23 RandomVariableForImputation -0.2369185 -1.6220983 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 m2 z23 1.0393184 6.0000000 m23 RandomVariableForImputation 1.0393184 1.1502594 y z 1.0000000 1.0000000 y2 z2 1.0000000 1.0000000 m2 y23 -0.2369185 1.0000000 z23 RandomVariableForImputation 1.0000000 -1.6220983 y z 6.0000000 6.0000000 y2 z2 6.0000000 6.0000000 m2 y23 1.0393184 6.0000000 z23 RandomVariableForImputation 6.0000000 1.1502594 test_kNN.R.................... 24 tests OK test_kNN.R.................... 25 tests OK test_kNN.R.................... 25 tests OK z z 1 6 test_kNN.R.................... 25 tests OK test_kNN.R.................... 26 tests OK test_kNN.R.................... 26 tests OK test_kNN.R.................... 27 tests OK test_kNN.R.................... 28 tests OK test_kNN.R.................... 28 tests OK test_kNN.R.................... 28 tests OK test_kNN.R.................... 28 tests OK y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 z m z m 1.0000000 -0.2369185 6.0000000 1.0393184 y z y z 1 1 6 6 y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 y z m 1.0000000 1.0000000 -0.2369185 yrandomForestFeature y z 2.1257000 6.0000000 6.0000000 m yrandomForestFeature 1.0393184 4.6920333 y z m 1.00000000 1.00000000 -0.23691848 mrandomForestFeature y z -0.04213089 6.00000000 6.00000000 m mrandomForestFeature 1.03931837 0.84783840 test_kNN.R.................... 28 tests OK test_kNN.R.................... 29 tests OK test_kNN.R.................... 30 tests OK test_kNN.R.................... 30 tests OK test_kNN.R.................... 30 tests OK test_kNN.R.................... 30 tests OK y z m y z m 1.0000000 1.0000000 -0.2369185 6.0000000 6.0000000 1.0393184 z m z m 1.0000000 -0.2369185 6.0000000 1.0393184 y z y z 1 1 6 6 yrandomForestFeature yrandomForestFeature 2.125167 4.833167 mrandomForestFeature mrandomForestFeature -0.09882801 0.85004137 test_kNN.R.................... 30 tests OK test_kNN.R.................... 31 tests OK test_kNN.R.................... 32 tests OK test_kNN.R.................... 32 tests OK test_kNN.R.................... 32 tests OK z y z y 1 1 6 6 z z 1 6 z z 1 6 z yrandomForestFeature z 1.000000 1.962533 6.000000 yrandomForestFeature 4.974033 test_kNN.R.................... 32 tests OK test_kNN.R.................... 33 tests OK test_kNN.R.................... 34 tests OK test_kNN.R.................... 34 tests OK test_kNN.R.................... 34 tests OK z y z y 1 1 6 6 z z 1 6 z z 1 6 z yrandomForestFeature z 1.000000 1.989633 6.000000 yrandomForestFeature 4.963533 test_kNN.R.................... 34 tests OK test_kNN.R.................... 35 tests OK test_kNN.R.................... 36 tests OK test_kNN.R.................... 36 tests OK col2 col3 col2 col3 3 5 4 6 test_kNN.R.................... 37 tests OK test_kNN.R.................... 37 tests OK test_kNN.R.................... 37 tests OK col2 col3 col2 col3 3 5 4 6 test_kNN.R.................... 38 tests OK test_kNN.R.................... 38 tests OK test_kNN.R.................... 38 tests OK col2 col3 col2 col3 3 5 4 6 test_kNN.R.................... 39 tests OK weighted catFun without missings in the distance variables test_kNN.R.................... 39 tests OK test_kNN.R.................... 39 tests OK test_kNN.R.................... 39 tests OK x y x y 1 1 10 10 test_kNN.R.................... 39 tests OK weighted catFun with missings in the distance variables test_kNN.R.................... 39 tests OK test_kNN.R.................... 39 tests OK x y x y 1 1 10 10 test_kNN.R.................... 39 tests OK x y x y 1 1 10 10 test_kNN.R.................... 39 tests OK 7.5s test_kNN_exact.R.............. 0 tests kNN exact results test_kNN_exact.R.............. 0 tests test_kNN_exact.R.............. 0 tests test_kNN_exact.R.............. 0 tests test_kNN_exact.R.............. 0 tests Detected as categorical variable: Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp Detected as ordinal variable: ord Detected as numerical variable: X1,X2,ClassNum,Row,Row2 0 items ofvariable:Class imputed 0 items ofvariable:X1 imputed X1 X1 1 1 2items ofvariable:X2 imputed 0 items ofvariable:ClassNum imputed 0 items ofvariable:Row imputed 0 items ofvariable:Row2 imputed 0 items ofvariable:ord imputed Time difference of 0.106776 secs test_kNN_exact.R.............. 0 tests test_kNN_exact.R.............. 1 tests OK test_kNN_exact.R.............. 2 tests OK test_kNN_exact.R.............. 2 tests OK test_kNN_exact.R.............. 3 tests OK test_kNN_exact.R.............. 4 tests OK X1 ClassNum X1 ClassNum 1 1 1 2 test_kNN_exact.R.............. 4 tests OK test_kNN_exact.R.............. 5 tests OK test_kNN_exact.R.............. 6 tests OK test_kNN_exact.R.............. 6 tests OK test_kNN_exact.R.............. 6 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 6 tests OK test_kNN_exact.R.............. 7 tests OK test_kNN_exact.R.............. 8 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 8 tests OK test_kNN_exact.R.............. 9 tests OK test_kNN_exact.R.............. 10 tests OK X1 Row2 X1 Row2 1 1 1 10 X1 Row2 X1 Row2 1 1 1 10 test_kNN_exact.R.............. 10 tests OK test_kNN_exact.R.............. 11 tests OK test_kNN_exact.R.............. 12 tests OK test_kNN_exact.R.............. 12 tests OK test_kNN_exact.R.............. 12 tests OK test_kNN_exact.R.............. 12 tests OK test_kNN_exact.R.............. 13 tests OK test_kNN_exact.R.............. 14 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 14 tests OK test_kNN_exact.R.............. 15 tests OK test_kNN_exact.R.............. 16 tests OK X1 Row2 X1 Row2 1 1 1 10 X1 Row2 X1 Row2 1 1 1 10 test_kNN_exact.R.............. 16 tests OK test_kNN_exact.R.............. 17 tests OK test_kNN_exact.R.............. 18 tests OK test_kNN_exact.R.............. 18 tests OK test_kNN_exact.R.............. 18 tests OK test_kNN_exact.R.............. 18 tests OK Detected as categorical variable: Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp Detected as ordinal variable: ord Detected as numerical variable: X1,X2,ClassNum,Row,Row2 0 items ofvariable:Class imputed 0 items ofvariable:X1 imputed X1 X1 1 1 2items ofvariable:X2 imputed 0 items ofvariable:ClassNum imputed 0 items ofvariable:Row imputed 0 items ofvariable:Row2 imputed 0 items ofvariable:ord imputed Time difference of 0.09526706 secs test_kNN_exact.R.............. 18 tests OK test_kNN_exact.R.............. 19 tests OK test_kNN_exact.R.............. 20 tests OK test_kNN_exact.R.............. 20 tests OK test_kNN_exact.R.............. 21 tests OK test_kNN_exact.R.............. 22 tests OK X1 ClassNum X1 ClassNum 1 1 1 2 test_kNN_exact.R.............. 22 tests OK test_kNN_exact.R.............. 23 tests OK test_kNN_exact.R.............. 24 tests OK test_kNN_exact.R.............. 24 tests OK test_kNN_exact.R.............. 24 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 24 tests OK test_kNN_exact.R.............. 25 tests OK test_kNN_exact.R.............. 26 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 26 tests OK test_kNN_exact.R.............. 27 tests OK test_kNN_exact.R.............. 28 tests OK X1 Row2 X1 Row2 1 1 1 10 X1 Row2 X1 Row2 1 1 1 10 test_kNN_exact.R.............. 28 tests OK test_kNN_exact.R.............. 29 tests OK test_kNN_exact.R.............. 30 tests OK test_kNN_exact.R.............. 30 tests OK test_kNN_exact.R.............. 30 tests OK test_kNN_exact.R.............. 30 tests OK test_kNN_exact.R.............. 31 tests OK test_kNN_exact.R.............. 32 tests OK Row2 Row2 1 10 Row2 Row2 1 10 test_kNN_exact.R.............. 32 tests OK test_kNN_exact.R.............. 33 tests OK test_kNN_exact.R.............. 34 tests OK X1 Row2 X1 Row2 1 1 1 10 X1 Row2 X1 Row2 1 1 1 10 test_kNN_exact.R.............. 34 tests OK test_kNN_exact.R.............. 35 tests OK test_kNN_exact.R.............. 36 tests OK 2.4s test_kNN_iqr.R................ 0 tests kNN iqr test_kNN_iqr.R................ 0 tests test_kNN_iqr.R................ 0 tests test_kNN_iqr.R................ 0 tests test_kNN_iqr.R................ 0 tests y z y z -1.914359 -2.888921 2.307978 2.649167 test_kNN_iqr.R................ 0 tests test_kNN_iqr.R................ 0 tests test_kNN_iqr.R................ 1 tests OK 0.2s test_kNN_ordered.R............ 0 tests kNN ordered test_kNN_ordered.R............ 0 tests test_kNN_ordered.R............ 0 tests test_kNN_ordered.R............ 0 tests test_kNN_ordered.R............ 0 tests y z y z 1 1 6 6 test_kNN_ordered.R............ 0 tests test_kNN_ordered.R............ 1 tests OK test_kNN_ordered.R............ 2 tests OK y z y z 1 1 6 6 test_kNN_ordered.R............ 2 tests OK test_kNN_ordered.R............ 3 tests OK test_kNN_ordered.R............ 4 tests OK y z y z 1 1 6 6 test_kNN_ordered.R............ 4 tests OK test_kNN_ordered.R............ 5 tests OK test_kNN_ordered.R............ 6 tests OK y z y z 1 1 6 6 test_kNN_ordered.R............ 6 tests OK test_kNN_ordered.R............ 7 tests OK test_kNN_ordered.R............ 8 tests OK 0.4s test_matchImpute.R............ 0 tests matchImpute general test_matchImpute.R............ 0 tests test_matchImpute.R............ 0 tests test_matchImpute.R............ 0 tests test_matchImpute.R............ 0 tests test_matchImpute.R............ 0 tests test_matchImpute.R............ 1 tests OK test_matchImpute.R............ 1 tests OK test_matchImpute.R............ 2 tests OK test_matchImpute.R............ 3 tests OK test_matchImpute.R............ 4 tests OK test_matchImpute.R............ 5 tests OK test_matchImpute.R............ 5 tests OK test_matchImpute.R............ 6 tests OK 83ms test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 0 tests test_rangerImpute.R........... 1 tests OK No missings in x. test_rangerImpute.R........... 1 tests OK test_rangerImpute.R........... 2 tests OK test_rangerImpute.R........... 2 tests OK test_rangerImpute.R........... 2 tests OK test_rangerImpute.R........... 3 tests OK test_rangerImpute.R........... 3 tests OK test_rangerImpute.R........... 4 tests OK test_rangerImpute.R........... 4 tests OK test_rangerImpute.R........... 4 tests OK test_rangerImpute.R........... 4 tests OK test_rangerImpute.R........... 5 tests OK 0.3s test_regressionImp.R.......... 0 tests test_regressionImp.R.......... 0 tests test_regressionImp.R.......... 1 tests OK test_regressionImp.R.......... 1 tests OK test_regressionImp.R.......... 2 tests OK test_regressionImp.R.......... 2 tests OK test_regressionImp.R.......... 3 tests OK test_regressionImp.R.......... 3 tests OK test_regressionImp.R.......... 4 tests OK test_regressionImp.R.......... 4 tests OK test_regressionImp.R.......... 4 tests OK test_regressionImp.R.......... 4 tests OK test_regressionImp.R.......... 4 tests OK test_regressionImp.R.......... 5 tests OK 63ms test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 0 tests test_xgboostImpute.R.......... 1 tests OK No missings in x. test_xgboostImpute.R.......... 1 tests OK test_xgboostImpute.R.......... 2 tests OK Error in process.y.margin.and.objective(y, base_margin, objective, params) : Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic Calls: ... xgboostImpute -> -> process.y.margin.and.objective In addition: There were 20 warnings (use warnings() to see them) Execution halted * checking package vignettes ... OK * checking re-building of vignette outputs ... [97s/98s] OK * DONE Status: 2 ERRORs, 1 WARNING