* using log directory 'd:/Rcompile/CRANpkg/local/4.5/censored.Rcheck' * using R Under development (unstable) (2024-06-07 r86704 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.2.0 GNU Fortran (GCC) 13.2.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * checking for file 'censored/DESCRIPTION' ... OK * this is package 'censored' version '0.3.1' * 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 hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'censored' can be installed ... OK * checking installed package size ... 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 ... [4s] OK * checking whether the package can be loaded with stated dependencies ... [4s] OK * checking whether the package can be unloaded cleanly ... [3s] OK * checking whether the namespace can be loaded with stated dependencies ... [4s] OK * checking whether the namespace can be unloaded cleanly ... [3s] OK * checking loading without being on the library search path ... [3s] OK * checking whether startup messages can be suppressed ... [3s] 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 ... [11s] OK * checking Rd files ... [1s] OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... [1s] OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking examples ... [14s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... [208s] ERROR Running 'testthat.R' [208s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(censored) Loading required package: parsnip Loading required package: survival > > test_check("censored") [ FAIL 25 | WARN 24 | SKIP 13 | PASS 677 ] ══ Skipped tests (13) ══════════════════════════════════════════════════════════ • Installed parsnip is version 1.2.1; but 1.2.1.9001 is required (2): 'test-proportional_hazards.R:18:3', 'test-survival_reg.R:16:3' • On CRAN (11): 'test-bag_tree-rpart.R:92:3', 'test-proportional_hazards-glmnet.R:30:3', 'test-proportional_hazards-glmnet.R:1121:3', 'test-proportional_hazards-glmnet.R:1149:3', 'test-proportional_hazards-glmnet.R:1158:3', 'test-proportional_hazards-glmnet.R:1180:3', 'test-proportional_hazards-glmnet.R:1287:3', 'test-proportional_hazards-survival.R:143:3', 'test-proportional_hazards.R:10:3', 'test-survival_reg-flexsurvspline.R:465:3', 'test-survival_reg.R:9:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-aaa_survival_prob.R:193:3'): survfit_summary_patch_infinite_time() works (coxph) ── prob[c(3, 4), ] (`actual`) not equal to `exp_prob` (`expected`). `dim(actual)`: 2 167 `dim(expected)`: 5 167 actual | expected [1] 1 | 1 [1] [2] 1 | 1 [2] [3] 1 - 0.116689708575876 [3] [4] 1 - 0.116689708575876 [4] [5] 1 | 1 [5] [6] 1 | 1 [6] [7] 1 | 1 [7] [8] 1 - 0.00157777236576145 [8] [9] 1 - 0.00157777236576145 [9] [10] 1 | 1 [10] ... ... ... and 825 more ... ── Failure ('test-aaa_survival_prob.R:199:3'): survfit_summary_patch_infinite_time() works (coxph) ── unname(prob[5, ]) (`actual`) not equal to rep(0, nrow(lung_pred)) (`expected`). actual | expected [1] 0.116690 - 0.000000 [1] [2] 0.001578 - 0.000000 [2] [3] 0.115321 - 0.000000 [3] [4] 0.002849 - 0.000000 [4] [5] 0.020960 - 0.000000 [5] [6] 0.049541 - 0.000000 [6] [7] 0.003456 - 0.000000 [7] [8] 0.052278 - 0.000000 [8] [9] 0.204580 - 0.000000 [9] [10] 0.021485 - 0.000000 [10] ... ... ... and 157 more ... ── Failure ('test-aaa_survival_prob.R:225:3'): survfit_summary_patch_infinite_time() works (coxnet) ── prob[c(3, 4), ] (`actual`) not equal to `exp_prob` (`expected`). `dim(actual)`: 2 5 `dim(expected)`: 5 5 actual | expected [2] 1 | 1 [1] [3] 1 | 1 [2] [4] 1 - 0.0476213688217657 [3] [5] 1 - 0.0476213688217657 [4] [6] 1 | 1 [5] [7] 1 | 1 [6] [8] 1 | 1 [7] [9] 1 - 0.0969319970439288 [8] - 0.0969319970439288 [9] - 1 [10] ... ... ... and 15 more ... ── Failure ('test-aaa_survival_prob.R:231:3'): survfit_summary_patch_infinite_time() works (coxnet) ── unname(prob[5, ]) (`actual`) not equal to rep(0, nrow(lung_pred)) (`expected`). `actual`: 0.05 0.10 0.10 0.05 0.10 `expected`: 0.00 0.00 0.00 0.00 0.00 ── Error ('test-bag_tree-rpart.R:174:3'): survival_prob_survbagg() works ─────── Error in `full_matrix[, -index_missing] <- x`: number of items to replace is not a multiple of replacement length Backtrace: ▆ 1. ├─censored::survival_prob_survbagg(mod, new_data = lung_pred, eval_time = pred_time) at test-bag_tree-rpart.R:174:3 2. │ ├─... %>% dplyr::select(-.row) 3. │ └─censored:::survfit_summary_patch(...) 4. │ └─... %>% ... 5. ├─dplyr::select(., -.row) 6. ├─tidyr::nest(., .pred = c(-.row)) 7. ├─censored:::keep_cols(., output) 8. │ └─dplyr::select(x, dplyr::all_of(cols_to_keep)) 9. ├─censored:::survfit_summary_to_tibble(...) 10. │ └─tibble::tibble(...) 11. │ └─tibble:::tibble_quos(xs, .rows, .name_repair) 12. │ └─rlang::eval_tidy(xs[[j]], mask) 13. ├─base::as.vector(object$surv) 14. └─censored:::survfit_summary_patch_missings(...) 15. └─censored (local) patch_element(...) ── Failure ('test-boost_tree-mboost.R:135:3'): survival_curve_to_prob() works ── prob[c(2, 3, 1), ] (`actual`) not equal to `exp_prob` (`expected`). actual vs expected [, 1] [, 2] [, 3] [, 4] [, 5] [, 6] [, 7] [, 8] [, 9] [, 10] [, 11] [, 12] [, 13] [, 14] [, 15] [, 16] [, 17] [, 18] [, 19] [, 20] [, 21] [, 22] [, 23] [, 24] [, 25] [, 26] [, 27] [, 28] [, 29] [, 30] [, 31] [, 32] [, 33] [, 34] [, 35] [, 36] [, 37] [, 38] [, 39] [, 40] [, 41] [, 42] [, 43] [, 44] [, 45] [, 46] [, 47] [, 48] [, 49] [, 50] [, 51] [, 52] [, 53] [, 54] [, 55] [, 56] [, 57] [, 58] [, 59] [, 60] [, 61] [, 62] [, 63] [, 64] [, 65] [, 66] [, 67] [, 68] [, 69] [, 70] [, 71] [, 72] [, 73] [, 74] [, 75] [, 76] [, 77] [, 78] [, 79] [, 80] [, 81] [, 82] [, 83] [, 84] [, 85] [, 86] [, 87] [, 88] [, 89] [, 90] [, 91] [, 92] [, 93] [, 94] [, 95] [, 96] [, 97] [, 98] [, 99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138] [,139] [,140] [,141] [,142] [,143] [,144] [,145] [,146] [,147] [,148] [,149] [,150] [,151] [,152] [,153] [,154] [,155] [,156] [,157] [,158] [,159] [,160] [,161] [,162] [,163] [,164] [,165] [,166] [,167] - actual[1, ] 0.9103802 0.7542842 0.9099110 0.7740178 0.8445654 0.8769217 0.7805807 0.8789854 0.9329964 0.8454788 0.8701158 0.8171299 0.8616782 0.9131473 0.9482294 0.8750778 0.8577350 0.57279756 0.8228662 0.57587145 0.8755929 0.7147331 0.8545351 0.7948608 0.8154802 0.9388881 0.7124153 0.9124812 0.7714263 0.8489300 0.9691474 0.8764436 0.6809675 0.9011381 0.7976538 0.9111504 0.9385433 0.9393309 0.8773333 0.8096754 0.9205599 0.8976789 0.8181411 0.8481088 0.9389168 0.8670105 0.7247699 0.55515076 0.9301883 0.9061740 0.8817514 0.9185888 0.9400029 0.6726404 0.9177241 0.9070211 0.9183846 0.8772951 0.9024039 0.8698772 0.9180678 0.8818387 0.9453629 0.8829735 0.8419924 0.9285349 0.9250596 0.9479855 0.55702615 0.8890051 0.7654463 0.9048415 0.57132261 0.8190954 0.9434805 0.8919563 0.8742719 0.9503544 0.8220661 0.7661498 0.9095895 0.8481730 0.8393330 0.6342458 0.7129282 0.7655493 0.7671221 0.8571919 0.9008065 0.8935952 0.6694724 0.9057919 0.9047772 0.8354173 0.8474379 0.8094462 0.9488264 0.9039458 0.8675590 0.8990319 0.7494288 0.9151300 0.7586056 0.8497927 0.7701539 0.8755484 0.9188605 0.8880494 0.8597642 0.9129739 0.8263213 0.7799224 0.7083957 0.8106286 0.9147683 0.9773122 0.9160153 0.8342533 0.7589334 0.8326340 0.9626265 0.9440400 0.7794466 0.8912150 0.839679 0.9431004 0.8209557 0.9613426 0.7872477 0.9262243 0.7042794 0.9124742 0.9263978 0.9172117 0.8821504 0.9543538 0.9685326 0.8257571 0.9016051 0.8423449 0.9522675 0.8795257 0.8239087 0.8349236 0.9051715 0.9507558 0.9174439 0.8701180 0.9415775 0.9020059 0.9764315 0.9394771 0.9592819 0.8506519 0.9154016 0.8398385 0.7698971 0.8125142 0.6382995 0.6957880 0.9573907 0.8843008 0.8994966 0.9352318 0.9065093 0.7910004 0.9378070 + expected[1, ] 0.6532470 0.2783730 0.6517215 0.3129615 0.4648209 0.5512263 0.3251771 0.5571339 0.7301415 0.4671049 0.5320897 0.4001698 0.5090885 0.6622999 0.7857844 0.5459898 0.4986084 0.07990004 0.4130685 0.08186306 0.5474486 0.2180468 0.4902283 0.3530408 0.3965191 0.7512855 0.2148585 0.6601118 0.3082376 0.4758143 0.8675183 0.5498650 0.1750799 0.6237076 0.3587016 0.6557570 0.7500353 0.7528936 0.5524009 0.3838792 0.6870332 0.6129232 0.4024205 0.4737305 0.7513899 0.5235324 0.2322814 0.06932917 0.7202286 0.6396710 0.5651288 0.6803873 0.7553395 0.1655786 0.6774877 0.6423873 0.6797016 0.5522919 0.6276905 0.5314284 0.6786392 0.5653826 0.7750696 0.5686896 0.4584333 0.7144414 0.7023950 0.7848682 0.07039765 0.5865206 0.2975494 0.6354165 0.07897125 0.4045536 0.7680954 0.5954024 0.5437132 0.7938020 0.4112503 0.2987914 0.6506780 0.4738932 0.4519035 0.1268375 0.2155609 0.2977309 0.3005150 0.4971785 0.6226673 0.6003799 0.1620713 0.6384489 0.6352117 0.4424213 0.4720333 0.3833867 0.7880304 0.6325691 0.5250359 0.6171239 0.2703388 0.6688463 0.2856791 0.4780109 0.3059388 0.5473226 0.6813004 0.5836668 0.5039803 0.6617296 0.4209927 0.3239353 0.2094154 0.3859329 0.6676483 0.9011593 0.6717857 0.4396326 0.2862394 0.4357762 0.8413601 0.7701629 0.3230402 0.5931615 0.452749 0.7666930 0.4087370 0.8362832 0.3379638 0.7064142 0.2039534 0.6600888 0.7070146 0.6757739 0.5662893 0.8090645 0.8650253 0.4196906 0.6251748 0.4593044 0.8010744 0.5586885 0.4154470 0.4412369 0.6364681 0.7953235 0.6765499 0.5320957 0.7610943 0.6264358 0.8974826 0.7534252 0.8281844 0.4802066 0.6697470 0.4531392 0.3054764 0.3900209 0.1305556 0.1930370 0.8208058 0.5725767 0.6185718 0.7381085 0.6407452 0.3453316 0.7473703 - actual[2, ] 0.8045596 0.5204340 0.8035995 0.5525127 0.6762067 0.7377253 0.5634234 0.7417526 0.8516098 0.6779017 0.7245323 0.6264152 0.7083640 0.8102347 0.8841591 0.7341378 0.7008787 0.27511862 0.6366470 0.27855013 0.7351388 0.4593998 0.6948378 0.5875837 0.6234901 0.8641167 0.4559568 0.8088666 0.5482377 0.6843277 0.9299900 0.7367942 0.4106892 0.7857688 0.5923767 0.8061370 0.8633820 0.8650608 0.7385277 0.6132592 0.8255491 0.7788004 0.6282121 0.6827955 0.8641780 0.7185578 0.4744794 0.25588476 0.8456852 0.7959763 0.7471697 0.8214610 0.8664950 0.3991515 0.8196712 0.7977008 0.8210381 0.7384532 0.7883275 0.7240723 0.8203824 0.7473411 0.8779812 0.7495704 0.6714449 0.8422079 0.8349253 0.8836324 0.25789126 0.7614826 0.5384451 0.7932682 0.27348065 0.6299104 0.8739375 0.7673501 0.7325728 0.8887549 0.6352143 0.5395918 0.8029421 0.6829153 0.6665434 0.3483532 0.4567175 0.5386128 0.5411792 0.6998515 0.7850992 0.7706196 0.3948110 0.7951993 0.7931376 0.6593636 0.6815452 0.6128573 0.8854488 0.7914507 0.7196110 0.7815218 0.5127079 0.8143150 0.5273656 0.6859394 0.5461457 0.7350525 0.8220238 0.7595881 0.7047250 0.8098784 0.6428555 0.5623235 0.4500206 0.6149326 0.8135698 0.9482366 0.8161407 0.6572378 0.5278936 0.6542870 0.9155615 0.8751382 0.5615294 0.7658738 0.667180 0.8731222 0.6332288 0.9127359 0.5746314 0.8373619 0.4439874 0.8088522 0.8377253 0.8186117 0.7479530 0.8974414 0.9286242 0.6418393 0.7867123 0.6720963 0.8929040 0.7428089 0.6385166 0.6584616 0.7939384 0.8896245 0.8190916 0.7245365 0.8698602 0.7875223 0.9462588 0.8653727 0.9082108 0.6875467 0.8148749 0.6674737 0.5457240 0.6182506 0.3535315 0.4316877 0.9040693 0.7521826 0.7824578 0.8563429 0.7966587 0.5809955 0.8618139 + expected[2, ] 0.9103802 0.7542842 0.9099110 0.7740178 0.8445654 0.8769217 0.7805807 0.8789854 0.9329964 0.8454788 0.8701158 0.8171299 0.8616782 0.9131473 0.9482294 0.8750778 0.8577350 0.57279756 0.8228662 0.57587145 0.8755929 0.7147331 0.8545351 0.7948608 0.8154802 0.9388881 0.7124153 0.9124812 0.7714263 0.8489300 0.9691474 0.8764436 0.6809675 0.9011381 0.7976538 0.9111504 0.9385433 0.9393309 0.8773333 0.8096754 0.9205599 0.8976789 0.8181411 0.8481088 0.9389168 0.8670105 0.7247699 0.55515076 0.9301883 0.9061740 0.8817514 0.9185888 0.9400029 0.6726404 0.9177241 0.9070211 0.9183846 0.8772951 0.9024039 0.8698772 0.9180678 0.8818387 0.9453629 0.8829735 0.8419924 0.9285349 0.9250596 0.9479855 0.55702615 0.8890051 0.7654463 0.9048415 0.57132261 0.8190954 0.9434805 0.8919563 0.8742719 0.9503544 0.8220661 0.7661498 0.9095895 0.8481730 0.8393330 0.6342458 0.7129282 0.7655493 0.7671221 0.8571919 0.9008065 0.8935952 0.6694724 0.9057919 0.9047772 0.8354173 0.8474379 0.8094462 0.9488264 0.9039458 0.8675590 0.8990319 0.7494288 0.9151300 0.7586056 0.8497927 0.7701539 0.8755484 0.9188605 0.8880494 0.8597642 0.9129739 0.8263213 0.7799224 0.7083957 0.8106286 0.9147683 0.9773122 0.9160153 0.8342533 0.7589334 0.8326340 0.9626265 0.9440400 0.7794466 0.8912150 0.839679 0.9431004 0.8209557 0.9613426 0.7872477 0.9262243 0.7042794 0.9124742 0.9263978 0.9172117 0.8821504 0.9543538 0.9685326 0.8257571 0.9016051 0.8423449 0.9522675 0.8795257 0.8239087 0.8349236 0.9051715 0.9507558 0.9174439 0.8701180 0.9415775 0.9020059 0.9764315 0.9394771 0.9592819 0.8506519 0.9154016 0.8398385 0.7698971 0.8125142 0.6382995 0.6957880 0.9573907 0.8843008 0.8994966 0.9352318 0.9065093 0.7910004 0.9378070 - actual[3, ] 0.6532470 0.2783730 0.6517215 0.3129615 0.4648209 0.5512263 0.3251771 0.5571339 0.7301415 0.4671049 0.5320897 0.4001698 0.5090885 0.6622999 0.7857844 0.5459898 0.4986084 0.07990004 0.4130685 0.08186306 0.5474486 0.2180468 0.4902283 0.3530408 0.3965191 0.7512855 0.2148585 0.6601118 0.3082376 0.4758143 0.8675183 0.5498650 0.1750799 0.6237076 0.3587016 0.6557570 0.7500353 0.7528936 0.5524009 0.3838792 0.6870332 0.6129232 0.4024205 0.4737305 0.7513899 0.5235324 0.2322814 0.06932917 0.7202286 0.6396710 0.5651288 0.6803873 0.7553395 0.1655786 0.6774877 0.6423873 0.6797016 0.5522919 0.6276905 0.5314284 0.6786392 0.5653826 0.7750696 0.5686896 0.4584333 0.7144414 0.7023950 0.7848682 0.07039765 0.5865206 0.2975494 0.6354165 0.07897125 0.4045536 0.7680954 0.5954024 0.5437132 0.7938020 0.4112503 0.2987914 0.6506780 0.4738932 0.4519035 0.1268375 0.2155609 0.2977309 0.3005150 0.4971785 0.6226673 0.6003799 0.1620713 0.6384489 0.6352117 0.4424213 0.4720333 0.3833867 0.7880304 0.6325691 0.5250359 0.6171239 0.2703388 0.6688463 0.2856791 0.4780109 0.3059388 0.5473226 0.6813004 0.5836668 0.5039803 0.6617296 0.4209927 0.3239353 0.2094154 0.3859329 0.6676483 0.9011593 0.6717857 0.4396326 0.2862394 0.4357762 0.8413601 0.7701629 0.3230402 0.5931615 0.452749 0.7666930 0.4087370 0.8362832 0.3379638 0.7064142 0.2039534 0.6600888 0.7070146 0.6757739 0.5662893 0.8090645 0.8650253 0.4196906 0.6251748 0.4593044 0.8010744 0.5586885 0.4154470 0.4412369 0.6364681 0.7953235 0.6765499 0.5320957 0.7610943 0.6264358 0.8974826 0.7534252 0.8281844 0.4802066 0.6697470 0.4531392 0.3054764 0.3900209 0.1305556 0.1930370 0.8208058 0.5725767 0.6185718 0.7381085 0.6407452 0.3453316 0.7473703 + expected[3, ] 0.8045596 0.5204340 0.8035995 0.5525127 0.6762067 0.7377253 0.5634234 0.7417526 0.8516098 0.6779017 0.7245323 0.6264152 0.7083640 0.8102347 0.8841591 0.7341378 0.7008787 0.27511862 0.6366470 0.27855013 0.7351388 0.4593998 0.6948378 0.5875837 0.6234901 0.8641167 0.4559568 0.8088666 0.5482377 0.6843277 0.9299900 0.7367942 0.4106892 0.7857688 0.5923767 0.8061370 0.8633820 0.8650608 0.7385277 0.6132592 0.8255491 0.7788004 0.6282121 0.6827955 0.8641780 0.7185578 0.4744794 0.25588476 0.8456852 0.7959763 0.7471697 0.8214610 0.8664950 0.3991515 0.8196712 0.7977008 0.8210381 0.7384532 0.7883275 0.7240723 0.8203824 0.7473411 0.8779812 0.7495704 0.6714449 0.8422079 0.8349253 0.8836324 0.25789126 0.7614826 0.5384451 0.7932682 0.27348065 0.6299104 0.8739375 0.7673501 0.7325728 0.8887549 0.6352143 0.5395918 0.8029421 0.6829153 0.6665434 0.3483532 0.4567175 0.5386128 0.5411792 0.6998515 0.7850992 0.7706196 0.3948110 0.7951993 0.7931376 0.6593636 0.6815452 0.6128573 0.8854488 0.7914507 0.7196110 0.7815218 0.5127079 0.8143150 0.5273656 0.6859394 0.5461457 0.7350525 0.8220238 0.7595881 0.7047250 0.8098784 0.6428555 0.5623235 0.4500206 0.6149326 0.8135698 0.9482366 0.8161407 0.6572378 0.5278936 0.6542870 0.9155615 0.8751382 0.5615294 0.7658738 0.667180 0.8731222 0.6332288 0.9127359 0.5746314 0.8373619 0.4439874 0.8088522 0.8377253 0.8186117 0.7479530 0.8974414 0.9286242 0.6418393 0.7867123 0.6720963 0.8929040 0.7428089 0.6385166 0.6584616 0.7939384 0.8896245 0.8190916 0.7245365 0.8698602 0.7875223 0.9462588 0.8653727 0.9082108 0.6875467 0.8148749 0.6674737 0.5457240 0.6182506 0.3535315 0.4316877 0.9040693 0.7521826 0.7824578 0.8563429 0.7966587 0.5809955 0.8618139 ── Failure ('test-boost_tree-mboost.R:156:3'): survival_curve_to_prob() works ── prob[c(2, 4), ] (`actual`) not equal to `exp_prob` (`expected`). `dim(actual)`: 2 167 `dim(expected)`: 4 167 actual | expected [1] 1 | 1 [1] [2] 0.116689708575876 - 1 [2] [3] 1 - 0.116689708575876 [3] [4] 0.00157777236576145 - 1 [4] [5] 1 | 1 [5] [6] 0.115321313359293 - 1 [6] [7] 1 - 0.00157777236576145 [7] [8] 0.00284879449168926 - 1 [8] [9] 1 | 1 [9] [10] 0.020960236539855 - 1 [10] ... ... ... and 658 more ... ── Error ('test-partykit.R:27:3'): survival_prob_partykit() works for ctree ──── Error in `tibble::tibble(.row = rep(seq_len(n_obs), each = length(eval_time)), .eval_time = rep(eval_time, times = n_obs), .pred_survival = as.vector(object$surv), .pred_lower = as.vector(object$lower), .pred_upper = as.vector(object$upper), .pred_hazard_cumulative = as.vector(object$cumhaz))`: Tibble columns must have compatible sizes. * Size 18: Existing data. * Size 30: Column `.pred_survival`. i Only values of size one are recycled. Backtrace: ▆ 1. ├─... %>% tidyr::unnest(cols = .pred) at test-partykit.R:27:3 2. ├─tidyr::unnest(., cols = .pred) 3. ├─censored::survival_prob_partykit(mod, new_data = lung_pred, eval_time = pred_time) 4. │ └─... %>% dplyr::select(-.row) 5. ├─dplyr::select(., -.row) 6. ├─tidyr::nest(., .pred = c(-.row)) 7. ├─censored:::keep_cols(., output) 8. │ └─dplyr::select(x, dplyr::all_of(cols_to_keep)) 9. └─censored:::survfit_summary_to_tibble(...) 10. └─tibble::tibble(...) 11. └─tibble:::tibble_quos(xs, .rows, .name_repair) 12. └─tibble:::vectbl_recycle_rows(...) 13. └─tibble:::abort_incompatible_size(...) 14. └─tibble:::tibble_abort(...) 15. └─rlang::abort(x, class, ..., call = call, parent = parent, use_cli_format = TRUE) ── Error ('test-partykit.R:105:3'): survival_prob_partykit() works for cforest ── Error in `tibble::tibble(.row = rep(seq_len(n_obs), each = length(eval_time)), .eval_time = rep(eval_time, times = n_obs), .pred_survival = as.vector(object$surv), .pred_lower = as.vector(object$lower), .pred_upper = as.vector(object$upper), .pred_hazard_cumulative = as.vector(object$cumhaz))`: Tibble columns must have compatible sizes. * Size 18: Existing data. * Size 30: Column `.pred_survival`. i Only values of size one are recycled. Backtrace: ▆ 1. ├─... %>% tidyr::unnest(cols = .pred) at test-partykit.R:105:3 2. ├─tidyr::unnest(., cols = .pred) 3. ├─censored::survival_prob_partykit(mod, new_data = lung_pred, eval_time = pred_time) 4. │ └─... %>% dplyr::select(-.row) 5. ├─dplyr::select(., -.row) 6. ├─tidyr::nest(., .pred = c(-.row)) 7. ├─censored:::keep_cols(., output) 8. │ └─dplyr::select(x, dplyr::all_of(cols_to_keep)) 9. └─censored:::survfit_summary_to_tibble(...) 10. └─tibble::tibble(...) 11. └─tibble:::tibble_quos(xs, .rows, .name_repair) 12. └─tibble:::vectbl_recycle_rows(...) 13. └─tibble:::abort_incompatible_size(...) 14. └─tibble:::tibble_abort(...) 15. └─rlang::abort(x, class, ..., call = call, parent = parent, use_cli_format = TRUE) ── Failure ('test-proportional_hazards-glmnet.R:779:3'): survival_prob_coxnet() works for single penalty value ── prob_non_na$.pred_survival[c(1, 4)] (`actual`) not equal to c(1, 0) (`expected`). `actual`: 1.00 0.05 `expected`: 1.00 0.00 ── Failure ('test-proportional_hazards-glmnet.R:783:3'): survival_prob_coxnet() works for single penalty value ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob_non_na` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.868250932268071 | 0.868250932268071 [3] [4] 0.0476213688217657 | 0.0476213688217657 [4] - 0.0476213688217657 [5] - 0.0476213688217657 [6] ── Failure ('test-proportional_hazards-glmnet.R:800:3'): survival_prob_coxnet() works for single penalty value ── prob$.pred_survival[c(1, 4)] (`actual`) not equal to c(1, 0) (`expected`). `actual`: 1.00 0.05 `expected`: 1.00 0.00 ── Failure ('test-proportional_hazards-glmnet.R:804:3'): survival_prob_coxnet() works for single penalty value ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.868250932268071 | 0.868250932268071 [3] [4] 0.0476213688217657 | 0.0476213688217657 [4] - 0.0476213688217657 [5] - 0.0476213688217657 [6] ── Failure ('test-proportional_hazards-glmnet.R:861:3'): survival_prob_coxnet() works for multiple penalty values ── prob_non_na$.pred_survival[c(1, 4, 7, 10)] (`actual`) not equal to c(1, 0, 1, 0) (`expected`). `actual`: 1.00 0.05 1.00 0.05 `expected`: 1.00 0.00 1.00 0.00 ── Failure ('test-proportional_hazards-glmnet.R:865:3'): survival_prob_coxnet() works for multiple penalty values ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.868250932268071 | 0.868250932268071 [3] [4] 0.0476213688217657 | 0.0476213688217657 [4] [5] 1 - 0.0476213688217657 [5] [6] 1 - 0.0476213688217657 [6] [7] 0.86774900287724 - 1 [7] [8] 0.0536241013070288 - 1 [8] - 0.86774900287724 [9] - 0.0536241013070288 [10] - 0.0536241013070288 [11] ... ... ... and 1 more ... ── Failure ('test-proportional_hazards-glmnet.R:890:3'): survival_prob_coxnet() works for multiple penalty values ── prob$.pred_survival[c(1, 4, 7, 10)] (`actual`) not equal to c(1, 0, 1, 0) (`expected`). `actual`: 1.00 0.05 1.00 0.05 `expected`: 1.00 0.00 1.00 0.00 ── Failure ('test-proportional_hazards-glmnet.R:894:3'): survival_prob_coxnet() works for multiple penalty values ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.868250932268071 | 0.868250932268071 [3] [4] 0.0476213688217657 | 0.0476213688217657 [4] [5] 1 - 0.0476213688217657 [5] [6] 1 - 0.0476213688217657 [6] [7] 0.86774900287724 - 1 [7] [8] 0.0536241013070288 - 1 [8] - 0.86774900287724 [9] - 0.0536241013070288 [10] - 0.0536241013070288 [11] ... ... ... and 1 more ... ── Failure ('test-proportional_hazards-survival.R:364:3'): survival_prob_coxph() works ── prob_non_na$.pred_survival[c(1, 4)] (`actual`) not equal to c(1, 0) (`expected`). `actual`: 1.00 0.05 `expected`: 1.00 0.00 ── Failure ('test-proportional_hazards-survival.R:368:3'): survival_prob_coxph() works ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob_non_na` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.878779368015034 | 0.878779368015034 [3] [4] 0.0545765319575854 | 0.0545765319575854 [4] - 0.0545765319575854 [5] - 0.0545765319575854 [6] ── Failure ('test-proportional_hazards-survival.R:385:3'): survival_prob_coxph() works ── prob$.pred_survival[c(1, 4)] (`actual`) not equal to c(1, 0) (`expected`). `actual`: 1.00 0.04 `expected`: 1.00 0.00 ── Failure ('test-proportional_hazards-survival.R:389:3'): survival_prob_coxph() works ── ... %>% dplyr::pull(.pred_survival) (`actual`) not equal to `exp_prob` (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.86390334096046 | 0.86390334096046 [3] [4] 0.0371652285221858 | 0.0371652285221858 [4] - 0.0371652285221858 [5] - 0.0371652285221858 [6] ── Failure ('test-proportional_hazards-survival.R:431:3'): survival_prob_coxph() works with confidence intervals ── pred_non_na$.pred_lower[c(1, 4)] (`actual`) not equal to rep(NA_real_, 2) (`expected`). `actual`: NA 0 `expected`: NA NA ── Failure ('test-proportional_hazards-survival.R:435:3'): survival_prob_coxph() works with confidence intervals ── pred_non_na$.pred_upper[c(1, 4)] (`actual`) not equal to rep(NA_real_, 2) (`expected`). `actual`: NA 0 `expected`: NA NA ── Failure ('test-proportional_hazards-survival.R:439:3'): survival_prob_coxph() works with confidence intervals ── ... %>% dplyr::pull(.pred_lower) (`actual`) not equal to exp_pred$lower[, 2] (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.837033719701861 | 0.837033719701861 [3] [4] 0.0214931739971754 | 0.0214931739971754 [4] - 0.0214931739971754 [5] - 0.0214931739971754 [6] ── Failure ('test-proportional_hazards-survival.R:446:3'): survival_prob_coxph() works with confidence intervals ── ... %>% dplyr::pull(.pred_upper) (`actual`) not equal to exp_pred$upper[, 2] (`expected`). actual | expected [2] 1 | 1 [2] [3] 0.922607010293405 | 0.922607010293405 [3] [4] 0.138583433089444 | 0.138583433089444 [4] - 0.138583433089444 [5] - 0.138583433089444 [6] [ FAIL 25 | WARN 24 | SKIP 13 | PASS 677 ] Error: Test failures Execution halted * checking PDF version of manual ... [10m] OK * checking HTML version of manual ... [4s] OK * DONE Status: 1 ERROR