diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION index 2b91ad70..9402885f 100644 --- a/CRAN-SUBMISSION +++ b/CRAN-SUBMISSION @@ -1,3 +1,3 @@ Version: 0.1.0 -Date: 2023-10-12 03:47:48 UTC -SHA: 8504a3cc7b87142ebabef93939a8812883e14725 +Date: 2023-10-13 20:40:05 UTC +SHA: 62f71c683b4ca6ec3f793b06af65f75d08bab1c1 diff --git a/DESCRIPTION b/DESCRIPTION index 6186479b..a0209353 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: aorsf Title: Accelerated Oblique Random Survival Forests -Version: 0.1.0 +Version: 0.1.1 Authors@R: c( person(given = "Byron", family = "Jaeger", diff --git a/cran-comments.md b/cran-comments.md index a6d682fb..7ae11bdd 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,3 +1,27 @@ +## Version 0.1.1 + +## R CMD check results + +Duration: 4m 8.3s + +0 errors ✔ | 0 warnings ✔ | 0 notes ✔ + +R CMD check succeeded + +## valgrind + +The error spotted by valgrind on initial submission of 0.1.0, "a conditional jump or move depends on uninitialized values has been fixed". + +## Downstream dependencies + +I have also run R CMD check on downstream dependencies of `aorsf`: + +- Rcpp +- data.table. +- collapse + +All packages passed. + ## Version 0.1.0 ## R CMD check results diff --git a/man/orsf.Rd b/man/orsf.Rd index 0991479e..f7200e71 100644 --- a/man/orsf.Rd +++ b/man/orsf.Rd @@ -520,12 +520,12 @@ The AUC values, from highest to lowest: }\if{html}{\out{}} \if{html}{\out{
}}\preformatted{## model times AUC se lower upper -## 1: net 1788 0.9179396 0.02012887 0.8784877 0.9573915 -## 2: accel 1788 0.9106396 0.02076004 0.8699507 0.9513286 -## 3: cph 1788 0.9061167 0.02277540 0.8614777 0.9507556 -## 4: rlt 1788 0.9012605 0.02178982 0.8585533 0.9439678 -## 5: rando 1788 0.8997729 0.02201363 0.8566270 0.9429188 -## 6: pca 1788 0.8996927 0.02245483 0.8556821 0.9437034 +## 1: net 1788 0.9134593 0.02079935 0.8726933 0.9542253 +## 2: cph 1788 0.9109155 0.02111657 0.8695278 0.9523032 +## 3: accel 1788 0.9099638 0.02122647 0.8683607 0.9515669 +## 4: rlt 1788 0.9069752 0.02132529 0.8651783 0.9487720 +## 5: rando 1788 0.9023489 0.02218936 0.8588586 0.9458393 +## 6: pca 1788 0.8994220 0.02201713 0.8562692 0.9425748 }\if{html}{\out{
}} And the indices of prediction accuracy: @@ -534,12 +534,12 @@ And the indices of prediction accuracy: }\if{html}{\out{}} \if{html}{\out{
}}\preformatted{## model times IPA -## 1: net 1788 0.5020652 -## 2: cph 1788 0.4759061 -## 3: accel 1788 0.4743392 -## 4: pca 1788 0.4398468 -## 5: rlt 1788 0.4373910 -## 6: rando 1788 0.4219209 +## 1: net 1788 0.4916815 +## 2: cph 1788 0.4833913 +## 3: accel 1788 0.4749974 +## 4: rlt 1788 0.4630984 +## 5: pca 1788 0.4371223 +## 6: rando 1788 0.4258456 ## 7: Null model 1788 0.0000000 }\if{html}{\out{
}} @@ -651,29 +651,29 @@ glimpse(results) \if{html}{\out{
}}\preformatted{## Rows: 276 ## Columns: 23 -## $ id 16, 29, 43, 62, 79, 82, 103, 105, 111, 114, 115, 139, 141,~ -## $ trt placebo, placebo, d_penicill_main, placebo, d_penicill_mai~ -## $ age 40.44353, 63.87680, 48.87064, 60.70637, 46.51608, 67.31006~ -## $ sex f, f, f, f, f, f, f, f, f, m, f, f, f, f, f, f, f, f, f, f~ -## $ ascites 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~ -## $ hepato 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1~ -## $ spiders 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1~ -## $ edema 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~ -## $ bili 0.7, 0.7, 1.1, 1.3, 0.8, 4.5, 2.5, 1.1, 5.5, 3.2, 0.7, 1.1~ -## $ chol 204, 370, 361, 302, 315, 472, 188, 464, 528, 259, 303, 328~ -## $ albumin 3.66, 3.78, 3.64, 2.75, 4.24, 4.09, 3.67, 4.20, 4.18, 4.30~ -## $ copper 28, 24, 36, 58, 13, 154, 57, 38, 77, 208, 81, 159, 59, 76,~ -## $ alk.phos 685.0, 5833.0, 5430.2, 1523.0, 1637.0, 1580.0, 1273.0, 164~ -## $ ast 72.85, 73.53, 67.08, 43.40, 170.50, 117.80, 119.35, 151.90~ -## $ trig 58, 86, 89, 112, 70, 272, 102, 102, 78, 78, 156, 134, 56, ~ -## $ platelet 198, 390, 203, 329, 426, 412, 110, 348, 467, 268, 307, 142~ -## $ protime 10.8, 10.6, 10.6, 13.2, 10.9, 11.1, 11.1, 10.3, 10.7, 11.7~ -## $ stage 3, 2, 2, 4, 3, 3, 4, 3, 3, 3, 3, 4, 2, 2, 3, 4, 2, 3, 4, 4~ -## $ time 3672, 4509, 4556, 3090, 3707, 3574, 110, 3092, 2350, 3395,~ -## $ status 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0~ -## $ pred_aorsf 0.02210163, 0.12510110, 0.07571520, 0.59580668, 0.12839078~ -## $ pred_rfsrc 0.01861595, 0.15632904, 0.07635485, 0.62281617, 0.19145913~ -## $ pred_ranger 0.02143363, 0.13367920, 0.05892584, 0.54481330, 0.21380654~ +## $ id 3, 39, 43, 48, 50, 54, 64, 66, 78, 80, 83, 114, 131, 141, ~ +## $ trt d_penicill_main, d_penicill_main, d_penicill_main, placebo~ +## $ age 70.07255, 55.39220, 48.87064, 49.13621, 53.50856, 39.19781~ +## $ sex m, f, f, m, f, f, f, m, f, m, f, m, f, f, f, f, m, f, f, f~ +## $ ascites 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~ +## $ hepato 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1~ +## $ spiders 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~ +## $ edema 0.5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, ~ +## $ bili 1.4, 0.7, 1.1, 1.9, 1.1, 1.3, 2.1, 1.4, 6.3, 7.2, 1.3, 3.2~ +## $ chol 176, 282, 361, 259, 257, 288, 373, 427, 436, 247, 250, 259~ +## $ albumin 3.48, 3.00, 3.64, 3.70, 3.36, 3.40, 3.50, 3.70, 3.02, 3.72~ +## $ copper 210, 52, 36, 281, 43, 262, 52, 105, 75, 269, 48, 208, 74, ~ +## $ alk.phos 516.0, 9066.8, 5430.2, 10396.8, 1080.0, 5487.2, 1009.0, 19~ +## $ ast 96.10, 72.24, 67.08, 188.34, 106.95, 73.53, 150.35, 182.90~ +## $ trig 55, 111, 89, 178, 73, 125, 188, 171, 104, 91, 100, 78, 104~ +## $ platelet 151, 563, 203, 214, 128, 254, 178, 123, 236, 360, 81, 268,~ +## $ protime 12.0, 10.6, 10.6, 11.0, 10.6, 11.0, 11.0, 11.0, 10.6, 11.2~ +## $ stage 4, 4, 2, 3, 4, 4, 3, 3, 4, 4, 4, 3, 4, 2, 3, 4, 2, 3, 4, 3~ +## $ time 1012, 2297, 4556, 4427, 2598, 1434, 1487, 4191, 1690, 890,~ +## $ status 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0~ +## $ pred_aorsf 0.76027848, 0.25291419, 0.06284001, 0.59437152, 0.15286015~ +## $ pred_rfsrc 0.47891074, 0.16833427, 0.05141013, 0.46526027, 0.06438684~ +## $ pred_ranger 0.61304990, 0.13930022, 0.03715869, 0.48395613, 0.04959462~ }\if{html}{\out{
}} And finish by aggregating the predictions and computing performance in @@ -699,16 +699,16 @@ counts. ## Results by model: ## ## model times AUC lower upper -## 1: aorsf 1826 91.0 86.8 95.2 -## 2: rfsrc 1826 89.2 84.8 93.7 -## 3: ranger 1826 89.6 85.3 94.0 +## 1: aorsf 1826 90.9 86.7 95.1 +## 2: rfsrc 1826 90.0 85.8 94.3 +## 3: ranger 1826 90.1 86.0 94.3 ## ## Results of model comparisons: ## -## times model reference delta.AUC lower upper p -## 1: 1826 rfsrc aorsf -1.7 -3.4 -0.1 0.04 -## 2: 1826 ranger aorsf -1.3 -2.9 0.2 0.08 -## 3: 1826 ranger rfsrc 0.4 -0.8 1.6 0.52 +## times model reference delta.AUC lower upper p +## 1: 1826 rfsrc aorsf -0.9 -2.2 0.5 0.2 +## 2: 1826 ranger aorsf -0.8 -2.1 0.6 0.3 +## 3: 1826 ranger rfsrc 0.1 -0.8 1.0 0.8 ## ## NOTE: Values are multiplied by 100 and given in \%. @@ -722,19 +722,19 @@ counts. ## ## model times Brier lower upper IPA ## 1: Null model 1826.25 20.5 18.1 22.9 0.0 -## 2: aorsf 1826.25 10.9 8.7 13.1 46.9 -## 3: rfsrc 1826.25 12.0 9.9 14.2 41.3 -## 4: ranger 1826.25 12.0 9.9 14.1 41.5 +## 2: aorsf 1826.25 10.8 8.5 13.0 47.4 +## 3: rfsrc 1826.25 11.8 9.6 13.9 42.6 +## 4: ranger 1826.25 11.7 9.6 13.8 42.7 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p -## 1: 1826.25 aorsf Null model -9.6 -12.2 -7.0 9.364941e-13 -## 2: 1826.25 rfsrc Null model -8.5 -10.7 -6.2 2.074175e-13 -## 3: 1826.25 ranger Null model -8.5 -10.8 -6.2 3.712823e-13 -## 4: 1826.25 rfsrc aorsf 1.1 0.3 2.0 1.075856e-02 -## 5: 1826.25 ranger aorsf 1.1 0.3 1.9 4.825778e-03 -## 6: 1826.25 ranger rfsrc -0.1 -0.6 0.5 8.429772e-01 +## 1: 1826.25 aorsf Null model -9.7 -12.4 -7.0 2.820785e-12 +## 2: 1826.25 rfsrc Null model -8.7 -11.0 -6.4 5.857526e-14 +## 3: 1826.25 ranger Null model -8.7 -11.1 -6.4 1.380943e-13 +## 4: 1826.25 rfsrc aorsf 1.0 0.2 1.8 1.507974e-02 +## 5: 1826.25 ranger aorsf 1.0 0.3 1.7 8.236836e-03 +## 6: 1826.25 ranger rfsrc -0.0 -0.5 0.4 9.336601e-01 ## ## NOTE: Values are multiplied by 100 and given in \%. diff --git a/man/orsf_control_custom.Rd b/man/orsf_control_custom.Rd index 4aa57cf8..fe1e66dd 100644 --- a/man/orsf_control_custom.Rd +++ b/man/orsf_control_custom.Rd @@ -70,7 +70,7 @@ fit_rando ## Average leaves per tree: 20 ## Min observations in leaf: 5 ## Min events in leaf: 1 -## OOB stat value: 0.84 +## OOB stat value: 0.83 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## @@ -108,7 +108,12 @@ How well do our two customized ORSFs do? Let’s compute their indices of prediction accuracy based on out-of-bag predictions: \if{html}{\out{
}}\preformatted{library(riskRegression) -library(survival) +}\if{html}{\out{
}} + +\if{html}{\out{
}}\preformatted{## riskRegression version 2023.09.08 +}\if{html}{\out{
}} + +\if{html}{\out{
}}\preformatted{library(survival) risk_preds <- list(rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag) @@ -130,15 +135,15 @@ The PCA ORSF does quite well! (higher IPA is better) ## ## model times Brier lower upper IPA ## 1: Null model 1788 20.479 18.090 22.868 0.000 -## 2: rando 1788 11.604 9.535 13.673 43.339 -## 3: pca 1788 12.870 10.872 14.869 37.154 +## 2: rando 1788 11.809 9.727 13.890 42.338 +## 3: pca 1788 12.967 10.983 14.950 36.683 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p -## 1: 1788 rando Null model -8.875 -11.063 -6.688 1.852437e-15 -## 2: 1788 pca Null model -7.609 -9.351 -5.866 1.143284e-17 -## 3: 1788 pca rando 1.267 0.449 2.084 2.381056e-03 +## 1: 1788 rando Null model -8.670 -10.843 -6.498 5.218847e-15 +## 2: 1788 pca Null model -7.512 -9.183 -5.842 1.226512e-18 +## 3: 1788 pca rando 1.158 0.305 2.011 7.810716e-03 ## ## NOTE: Values are multiplied by 100 and given in \%. diff --git a/man/orsf_ice_oob.Rd b/man/orsf_ice_oob.Rd index d9396498..37edee35 100644 --- a/man/orsf_ice_oob.Rd +++ b/man/orsf_ice_oob.Rd @@ -132,7 +132,7 @@ fit ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 -## Average leaves per tree: 25 +## Average leaves per tree: 21 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 @@ -152,17 +152,17 @@ ice_oob }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred -## 1: 1 1 1788 1 0.9295584 -## 2: 1 2 1788 1 0.1422392 -## 3: 1 3 1788 1 0.7047846 -## 4: 1 4 1788 1 0.3845760 -## 5: 1 5 1788 1 0.1206201 +## 1: 1 1 1788 1 0.8976716 +## 2: 1 2 1788 1 0.1202763 +## 3: 1 3 1788 1 0.6842180 +## 4: 1 4 1788 1 0.3865812 +## 5: 1 5 1788 1 0.1184953 ## --- -## 6896: 25 272 1788 10 0.3878561 -## 6897: 25 273 1788 10 0.4854526 -## 6898: 25 274 1788 10 0.4389557 -## 6899: 25 275 1788 10 0.3639220 -## 6900: 25 276 1788 10 0.5461205 +## 6896: 25 272 1788 10 0.3421749 +## 6897: 25 273 1788 10 0.4296413 +## 6898: 25 274 1788 10 0.4496536 +## 6899: 25 275 1788 10 0.3186596 +## 6900: 25 276 1788 10 0.5490316 }\if{html}{\out{
}} Much more detailed examples are given in the diff --git a/man/orsf_pd_oob.Rd b/man/orsf_pd_oob.Rd index 58e11315..4e690b26 100644 --- a/man/orsf_pd_oob.Rd +++ b/man/orsf_pd_oob.Rd @@ -160,12 +160,12 @@ You can compute partial dependence and ICE three ways with \code{aorsf}: pd_train }\if{html}{\out{}} -\if{html}{\out{
}}\preformatted{## pred_horizon bili mean lwr medn upr -## 1: 1826.25 1 0.2188047 0.01435497 0.09604722 0.8243506 -## 2: 1826.25 2 0.2540831 0.03086042 0.13766124 0.8442959 -## 3: 1826.25 3 0.2982917 0.05324065 0.19470910 0.8578131 -## 4: 1826.25 4 0.3536969 0.09755193 0.27774884 0.8699063 -## 5: 1826.25 5 0.3955249 0.14622431 0.29945708 0.8775099 +\if{html}{\out{
}}\preformatted{## pred_horizon bili mean lwr medn upr +## 1: 1826.25 1 0.2046395 0.02119497 0.1038427 0.7755589 +## 2: 1826.25 2 0.2372342 0.03380476 0.1307957 0.8017817 +## 3: 1826.25 3 0.2785774 0.05468892 0.1828047 0.8173042 +## 4: 1826.25 4 0.3286266 0.09236600 0.2433536 0.8357596 +## 5: 1826.25 5 0.3641739 0.12598059 0.2811453 0.8390030 }\if{html}{\out{
}} \item using out-of-bag predictions for the training data @@ -175,11 +175,11 @@ pd_train }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{## pred_horizon bili mean lwr medn upr -## 1: 1826.25 1 0.2182691 0.01218789 0.1008030 0.8304537 -## 2: 1826.25 2 0.2542021 0.02447359 0.1453580 0.8484741 -## 3: 1826.25 3 0.2980946 0.04854875 0.1997769 0.8640601 -## 4: 1826.25 4 0.3552203 0.10116417 0.2691853 0.8642393 -## 5: 1826.25 5 0.3959143 0.14768055 0.3264149 0.8737186 +## 1: 1826.25 1 0.2051876 0.02157962 0.1117025 0.7757180 +## 2: 1826.25 2 0.2373600 0.03558132 0.1413032 0.7984893 +## 3: 1826.25 3 0.2778296 0.05380047 0.1809783 0.8069165 +## 4: 1826.25 4 0.3281679 0.09414283 0.2378438 0.8244269 +## 5: 1826.25 5 0.3632203 0.12856484 0.2805170 0.8253445 }\if{html}{\out{
}} \item using predictions for a new set of data @@ -191,11 +191,11 @@ pd_test }\if{html}{\out{}} \if{html}{\out{
}}\preformatted{## pred_horizon bili mean lwr medn upr -## 1: 1826.25 1 0.2643662 0.01758300 0.2098936 0.8410357 -## 2: 1826.25 2 0.2990578 0.04063388 0.2516202 0.8553218 -## 3: 1826.25 3 0.3432503 0.06843859 0.3056799 0.8670726 -## 4: 1826.25 4 0.3968111 0.11801725 0.3593064 0.8725208 -## 5: 1826.25 5 0.4388962 0.16038177 0.4094224 0.8809027 +## 1: 1826.25 1 0.2439104 0.02172942 0.1779830 0.8041872 +## 2: 1826.25 2 0.2756943 0.03962448 0.2154036 0.8222641 +## 3: 1826.25 3 0.3183049 0.06352379 0.2685873 0.8375648 +## 4: 1826.25 4 0.3688559 0.10419502 0.3253473 0.8540203 +## 5: 1826.25 5 0.4044198 0.13842629 0.3598379 0.8570826 }\if{html}{\out{
}} \item in-bag partial dependence indicates relationships that the model has learned during training. This is helpful if your goal is to interpret diff --git a/man/orsf_vi.Rd b/man/orsf_vi.Rd index a6376b7f..69f26aa5 100644 --- a/man/orsf_vi.Rd +++ b/man/orsf_vi.Rd @@ -135,7 +135,7 @@ fit ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 -## Average leaves per tree: 25 +## Average leaves per tree: 21 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 @@ -156,12 +156,12 @@ the ‘raw’ variable importance values can be accessed from the fit object \if{html}{\out{
}}\preformatted{attr(fit, 'importance_values') }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## ascites_1 edema_1 bili copper albumin age -## 0.44989185 0.43936093 0.29908016 0.22471022 0.20573664 0.19373368 -## edema_0.5 protime chol stage spiders_1 ast -## 0.19096711 0.17582704 0.17527675 0.17057992 0.16721527 0.16061635 -## sex_f hepato_1 trig alk.phos platelet trt_placebo -## 0.14513788 0.14241390 0.12695468 0.12228332 0.10395510 0.09001406 +\if{html}{\out{
}}\preformatted{## edema_1 ascites_1 bili copper albumin age +## 0.53189300 0.49950642 0.39598881 0.30443254 0.26028060 0.24758399 +## protime stage chol edema_0.5 spiders_1 ast +## 0.22874192 0.20974576 0.20353982 0.18401760 0.18090452 0.17457962 +## hepato_1 sex_f trig alk.phos platelet trt_placebo +## 0.16402406 0.14803440 0.13009809 0.11627907 0.07853659 0.06939410 }\if{html}{\out{
}} these are ‘raw’ because values for factors have not been aggregated into @@ -181,11 +181,11 @@ To get aggregated values across all levels of each factor, }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{## ascites bili edema copper albumin age protime -## 0.44989185 0.29908016 0.29150746 0.22471022 0.20573664 0.19373368 0.17582704 -## chol stage spiders ast sex hepato trig -## 0.17527675 0.17057992 0.16721527 0.16061635 0.14513788 0.14241390 0.12695468 +## 0.49950642 0.39598881 0.32482431 0.30443254 0.26028060 0.24758399 0.22874192 +## stage chol spiders ast hepato sex trig +## 0.20974576 0.20353982 0.18090452 0.17457962 0.16402406 0.14803440 0.13009809 ## alk.phos platelet trt -## 0.12228332 0.10395510 0.09001406 +## 0.11627907 0.07853659 0.06939410 }\if{html}{\out{
}} \item use \code{orsf_vi()} with group_factors set to \code{TRUE} (the default) @@ -193,11 +193,11 @@ To get aggregated values across all levels of each factor, }\if{html}{\out{}} \if{html}{\out{
}}\preformatted{## ascites bili edema copper albumin age protime -## 0.44989185 0.29908016 0.29150746 0.22471022 0.20573664 0.19373368 0.17582704 -## chol stage spiders ast sex hepato trig -## 0.17527675 0.17057992 0.16721527 0.16061635 0.14513788 0.14241390 0.12695468 +## 0.49950642 0.39598881 0.32482431 0.30443254 0.26028060 0.24758399 0.22874192 +## stage chol spiders ast hepato sex trig +## 0.20974576 0.20353982 0.18090452 0.17457962 0.16402406 0.14803440 0.13009809 ## alk.phos platelet trt -## 0.12228332 0.10395510 0.09001406 +## 0.11627907 0.07853659 0.06939410 }\if{html}{\out{
}} } @@ -220,25 +220,25 @@ You can fit an ORSF without VI, then add VI later orsf_vi_negate(fit_no_vi) }\if{html}{\out{}} -\if{html}{\out{
}}\preformatted{## bili copper sex stage protime age -## 0.117833946 0.046771025 0.038096005 0.026596235 0.023892153 0.022568331 -## albumin ascites chol ast edema hepato -## 0.020502226 0.015764542 0.013505575 0.011507061 0.007444267 0.007318432 -## trt spiders alk.phos trig platelet -## 0.006135388 0.005416366 0.003385460 0.003359579 0.001225734 +\if{html}{\out{
}}\preformatted{## bili copper sex protime stage albumin +## 0.118355612 0.048917049 0.037068840 0.027044335 0.023867241 0.021214168 +## age ascites chol ast hepato edema +## 0.020517824 0.014993236 0.014726515 0.011441749 0.007711157 0.007218808 +## spiders trig alk.phos trt platelet +## 0.006372905 0.003230269 0.002823511 0.002469395 0.001550349 }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{orsf_vi_permute(fit_no_vi) }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## bili copper age protime albumin -## 0.0557854459 0.0230058852 0.0142318894 0.0139189306 0.0138242166 -## ascites stage chol ast edema -## 0.0122576604 0.0122514140 0.0062628391 0.0060073065 0.0057933534 +\if{html}{\out{
}}\preformatted{## bili copper protime albumin ascites +## 0.0546201463 0.0248826626 0.0154623867 0.0135573758 0.0134222183 +## age stage chol edema ast +## 0.0119054385 0.0113940805 0.0074991392 0.0052943907 0.0051219919 ## hepato spiders sex trig alk.phos -## 0.0052890246 0.0038620727 0.0031610738 0.0014580912 0.0009063636 +## 0.0050381864 0.0046277553 0.0039401072 0.0024125340 0.0009602607 ## platelet trt -## 0.0001124081 -0.0017971380 +## 0.0004343594 -0.0018248238 }\if{html}{\out{
}} } @@ -254,14 +254,14 @@ fit an ORSF and compute vi at the same time orsf_vi_permute(fit_permute_vi) }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## bili copper age ascites protime -## 0.0537706105 0.0232845222 0.0135823364 0.0127916446 0.0125320108 -## albumin stage ast edema hepato -## 0.0115100144 0.0109035858 0.0063943212 0.0062769135 0.0048230621 -## chol spiders sex trig alk.phos -## 0.0042752565 0.0030699653 0.0025422803 0.0022410492 0.0010977282 +\if{html}{\out{
}}\preformatted{## bili copper age ascites albumin +## 5.352210e-02 2.610549e-02 1.286639e-02 1.251888e-02 1.205836e-02 +## protime stage ast chol edema +## 1.084665e-02 1.057182e-02 8.228770e-03 6.002428e-03 5.834663e-03 +## spiders hepato sex trig alk.phos +## 4.760070e-03 3.437825e-03 3.388559e-03 2.274705e-03 2.226034e-03 ## platelet trt -## 0.0010972387 -0.0005947093 +## 1.424997e-03 -4.992912e-06 }\if{html}{\out{
}} You can still get negation VI from this fit, but it needs to be computed @@ -269,12 +269,12 @@ You can still get negation VI from this fit, but it needs to be computed \if{html}{\out{
}}\preformatted{orsf_vi_negate(fit_permute_vi) }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## bili copper sex age protime stage -## 0.120854614 0.046515980 0.036380485 0.022668834 0.021816803 0.021111101 -## albumin ascites ast chol edema spiders -## 0.018969867 0.014101778 0.013042103 0.011220170 0.008009693 0.006193354 -## trt hepato trig alk.phos platelet -## 0.005184060 0.005113622 0.003389060 0.003156121 0.002242597 +\if{html}{\out{
}}\preformatted{## bili copper sex stage protime albumin +## 0.124726411 0.052319578 0.038681018 0.027479231 0.022737472 0.022214271 +## age ast ascites chol spiders edema +## 0.020375826 0.013404081 0.013208974 0.011856865 0.008990815 0.007340934 +## hepato trt trig alk.phos platelet +## 0.006432032 0.004392664 0.004237792 0.003229450 0.002576929 }\if{html}{\out{
}} } } diff --git a/man/predict.orsf_fit.Rd b/man/predict.orsf_fit.Rd index 1aa15166..657be9dd 100644 --- a/man/predict.orsf_fit.Rd +++ b/man/predict.orsf_fit.Rd @@ -119,12 +119,12 @@ predict(fit, pred_horizon = c(500, 1000, 1500)) }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## [,1] [,2] [,3] -## [1,] 0.49884105 0.77681319 0.91901860 -## [2,] 0.04475471 0.09161544 0.17682278 -## [3,] 0.12850458 0.27603519 0.41455070 -## [4,] 0.01279086 0.02980402 0.06458151 -## [5,] 0.01277317 0.02249769 0.04875677 +\if{html}{\out{
}}\preformatted{## [,1] [,2] [,3] +## [1,] 0.459077419 0.73067673 0.89246351 +## [2,] 0.032194868 0.08028381 0.15592011 +## [3,] 0.115945485 0.24099853 0.38094684 +## [4,] 0.008378033 0.02964250 0.06977315 +## [5,] 0.009798295 0.01793586 0.04454374 }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{# predicted survival, i.e., 1 - risk @@ -135,11 +135,11 @@ predict(fit, }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{## [,1] [,2] [,3] -## [1,] 0.5011589 0.2231868 0.0809814 -## [2,] 0.9552453 0.9083846 0.8231772 -## [3,] 0.8714954 0.7239648 0.5854493 -## [4,] 0.9872091 0.9701960 0.9354185 -## [5,] 0.9872268 0.9775023 0.9512432 +## [1,] 0.5409226 0.2693233 0.1075365 +## [2,] 0.9678051 0.9197162 0.8440799 +## [3,] 0.8840545 0.7590015 0.6190532 +## [4,] 0.9916220 0.9703575 0.9302269 +## [5,] 0.9902017 0.9820641 0.9554563 }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{# predicted cumulative hazard function @@ -151,11 +151,11 @@ predict(fit, }\if{html}{\out{
}} \if{html}{\out{
}}\preformatted{## [,1] [,2] [,3] -## [1,] 0.70860748 1.40641948 1.79893071 -## [2,] 0.04954335 0.11460828 0.24130253 -## [3,] 0.16616222 0.43287394 0.71524591 -## [4,] 0.01443848 0.03640393 0.08366798 -## [5,] 0.01435412 0.02680792 0.06203327 +## [1,] 0.63532189 1.27109029 1.74481341 +## [2,] 0.03415809 0.09124550 0.20017014 +## [3,] 0.14715014 0.34375274 0.62976148 +## [4,] 0.00857621 0.03195771 0.08744159 +## [5,] 0.01043219 0.01888677 0.05177019 }\if{html}{\out{
}} Predict mortality, defined as the number of events in the forest’s @@ -168,12 +168,12 @@ prediction horizon pred_type = 'mort') }\if{html}{\out{
}} -\if{html}{\out{
}}\preformatted{## [,1] -## [1,] 81.23490 -## [2,] 27.69730 -## [3,] 41.52408 -## [4,] 15.79522 -## [5,] 10.65239 +\if{html}{\out{
}}\preformatted{## [,1] +## [1,] 78.646185 +## [2,] 20.872849 +## [3,] 37.341745 +## [4,] 13.616617 +## [5,] 8.798328 }\if{html}{\out{
}} } diff --git a/src/AAA_check_cpp_version.cpp b/src/AAA_check_cpp_version.cpp index d5d30887..0a48901a 100644 --- a/src/AAA_check_cpp_version.cpp +++ b/src/AAA_check_cpp_version.cpp @@ -1,5 +1,3 @@ -#ifndef WIN_R_BUILD #if __cplusplus < 201402L #error Error: aorsf requires a C++14 compiler, e.g., gcc >= 5 or Clang >= 3.4. You probably have to update your C++ compiler. #endif -#endif diff --git a/src/Makevars.win b/src/Makevars.win index 72930746..3a7f8ac9 100644 --- a/src/Makevars.win +++ b/src/Makevars.win @@ -1,3 +1,2 @@ -PKG_CPPFLAGS = -DWIN_R_BUILD PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)