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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(hdcuremodels) > > test_check("hdcuremodels") Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658083608 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756012758903343 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 297 Maximum C-statistic: 0.770985658659945 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 434 Maximum C-statistic: 0.734125636924693 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.09 Selected lambda for latency: 0.09 Maximum C-statistic: 0.691812421471135 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.105 Selected lambda for latency: 0.105 Maximum C-statistic: 0.71552008321421 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600785 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 3 Maximum C-statistic: 0.697702519728553 Fitting a final model... Family: Cox Algorithm: EM Family: Cox Algorithm: EM Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600785 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110859 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... mixturecure object fit using Weibull GMIFS algorithm $b_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0 [2,] 0 0 0.02 0 0 0 [3,] 0 0 0.03 0 0 0 [4,] 0 0 0.04 0 0 0 [5,] 0 0 0.05 0 0 0 [6,] 0 0 0.06 0 0 0 1274 more rows 6 more columns $beta_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0.00 [2,] 0 0 0.02 0 0 0.00 [3,] 0 0 0.03 0 0 0.00 [4,] 0 0 0.03 0 0 0.01 [5,] 0 0 0.03 0 0 0.02 [6,] 0 0 0.03 0 0 0.03 1274 more rows 6 more columns $rate 1.74771 1.747557 1.745463 1.776432 1.806308 1.835547 1274 more elements $alpha 0.5120896 0.5130922 0.5131297 0.5149383 0.5167088 0.51841 1274 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 0 6 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658083608 Fitting a final model... mixturecure object fit using Weibull EM algorithm $b 0 0 0.3534216 -0.1938579 0 -0.4778904 6 more elements $beta 0 0 0 0 0 0 6 more elements $rate 5.125488 $alpha 0.897226 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.054 Selected lambda for latency: 0.054 Maximum C-statistic: 0.703222725537594 Fitting a final model... Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 [ FAIL 0 | WARN 1 | SKIP 0 | PASS 557 ] [ FAIL 0 | WARN 1 | SKIP 0 | PASS 557 ] > > proc.time() user system elapsed 202.73 12.10 214.84