Running ‘testthat.R’ [187s/219s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # 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: 0.756490658089751
  
  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: 0.756012758893876
  
  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: 0.734125636924605
  
  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: 0.691812421454834
  
  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: 0.71552008323144
  
  Fitting a final model...
  
  Fold 1 out of 2 training...
  
  Fold 2 out of 2 training...
  
  Selected step: 27
  
  Maximum C-statistic: 0.674984053600791
  
  Fitting a final model...
  
  Fold 1 out of 2 training...
  
  Fold 2 out of 2 training...
  
  Selected step: 3
  
  Maximum C-statistic: 0.697702519726813
  
  Fitting a final model...
  
  
  Family: Cox 
  Algorithm: EM 
  
  
  Family: Cox 
  Algorithm: EM 
  
  Saving _problems/test-formula-11.R
  Saving _problems/test-logLik-11.R
  Saving _problems/test-nobs-11.R
  Fold 1 out of 2 training...
  
  Fold 2 out of 2 training...
  
  Selected step: 27
  
  Maximum C-statistic: 0.674984053600791
  
  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: 0.635331032113726
  
  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: 0.756490658089751
  
  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 3 | WARN 0 | SKIP 0 | PASS 552 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-formula.R:11:3'): formula function works correctly ─────────────
  Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent.
  Backtrace:
      ▆
   1. └─testthat::expect(failure_message = <empty>) at test-formula.R:11:3
   2.   └─testthat:::check_character(failure_message)
   3.     └─testthat:::stop_input_type(...)
   4.       └─rlang::abort(message, ..., call = call, arg = arg)
  ── Error ('test-logLik.R:11:3'): logLik function works correctly ───────────────
  Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a <logLik> object.
  Backtrace:
      ▆
   1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3
   2.   └─testthat:::check_bool(ok)
   3.     └─testthat:::stop_input_type(...)
   4.       └─rlang::abort(message, ..., call = call, arg = arg)
  ── Error ('test-nobs.R:11:3'): nobs function works correctly ───────────────────
  Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60.
  Backtrace:
      ▆
   1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3
   2.   └─testthat:::check_bool(ok)
   3.     └─testthat:::stop_input_type(...)
   4.       └─rlang::abort(message, ..., call = call, arg = arg)
  
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ]
  Error:
  ! Test failures.
  Execution halted