Last updated on 2024-06-09 02:56:58 CEST.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| censored | 2 | 11 | |
| dials | 1 | 12 | |
| hardhat | 2 | 11 | |
| rsample | 13 |
Current CRAN status: ERROR: 2, OK: 11
Version: 0.3.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [367s/425s]
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 ────
<tibble_error_incompatible_size/tibble_error/rlang_error/error/condition>
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$cumhaz))`: Tibble columns must have compatible sizes.
* Size 18: Existing data.
* Size 30: Column `.pred_survival`.
i Only values of size one are recycled.
Backtrace:
▆
1. ├─... %>% tidyr::unnest(cols = .pred) at test-partykit.R:27:3
2. ├─tidyr::unnest(., cols = .pred)
3. ├─censored::survival_prob_partykit(mod, new_data = lung_pred, eval_time = pred_time)
4. │ └─... %>% dplyr::select(-.row)
5. ├─dplyr::select(., -.row)
6. ├─tidyr::nest(., .pred = c(-.row))
7. ├─censored:::keep_cols(., output)
8. │ └─dplyr::select(x, dplyr::all_of(cols_to_keep))
9. └─censored:::survfit_summary_to_tibble(...)
10. └─tibble::tibble(...)
11. └─tibble:::tibble_quos(xs, .rows, .name_repair)
12. └─tibble:::vectbl_recycle_rows(...)
13. └─tibble:::abort_incompatible_size(...)
14. └─tibble:::tibble_abort(...)
15. └─rlang::abort(x, class, ..., call = call, parent = parent, use_cli_format = TRUE)
── Error ('test-partykit.R:105:3'): survival_prob_partykit() works for cforest ──
<tibble_error_incompatible_size/tibble_error/rlang_error/error/condition>
Error in `tibble::tibble(.row = rep(seq_len(n_obs), each = length(eval_time)),
.eval_time = rep(eval_time, times = n_obs), .pred_survival = as.vector(object$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
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.3.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [263s/464s]
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 ────
<tibble_error_incompatible_size/tibble_error/rlang_error/error/condition>
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 ──
<tibble_error_incompatible_size/tibble_error/rlang_error/error/condition>
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
Flavor: r-devel-linux-x86_64-debian-gcc
Current CRAN status: NOTE: 1, OK: 12
Version: 1.2.1
Check: Rd cross-references
Result: NOTE
Undeclared packages ‘uwot’, ‘embed’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Current CRAN status: ERROR: 2, OK: 11
Version: 1.4.0
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘forge.Rmd’
...
> test_missing_column <- subset(penguin_test, select = -species)
> forge(test_missing_column, formula_eng)
When sourcing ‘forge.R’:
Error: The following required columns are missing: 'species'.
Execution halted
when running code in ‘mold.Rmd’
...
8 1 3200
9 1 3800
10 1 4400
# ℹ 323 more rows
> mold(~body_mass_g - 1, penguins)
When sourcing ‘mold.R’:
Error: `formula` must not contain the intercept removal term: `- 1`.
Execution halted
when running code in ‘package.Rmd’
...
> data(penguins)
> penguins <- na.omit(penguins)
> knitr::include_graphics("../man/figures/Fitting.png")
When sourcing ‘package.R’:
Error: Cannot find the file(s): "../man/figures/Fitting.png"
Execution halted
‘forge.Rmd’ using ‘UTF-8’... failed
‘mold.Rmd’ using ‘UTF-8’... failed
‘package.Rmd’ using ‘UTF-8’... failed
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Current CRAN status: OK: 13