Supplement
Last updated: 2022-06-15
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Knit directory:
ampel-leipzig-meld/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 19d9fed | Sebastian Gibb | 2022-06-15 | feat: further work on the manuscript |
| html | d3e9462 | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| html | b20484a | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| Rmd | 01f934b | Sebastian Gibb | 2022-06-06 | feat: add first discussion draft |
| html | 983ec69 | Sebastian Gibb | 2022-03-17 | chore: rebuild site |
| html | 373e7d8 | Sebastian Gibb | 2021-10-20 | chore: rebuild site |
| html | df8964f | Sebastian Gibb | 2021-10-15 | chore: rebuild site |
| html | c66640c | Sebastian Gibb | 2021-09-14 | feat: first nnet tests |
| html | afa48d9 | Sebastian Gibb | 2021-08-07 | chore: rebuild site |
| Rmd | 3957af7 | Sebastian Gibb | 2021-08-02 | refactor: move common yaml headers into _site.yml |
| html | 3aab3e1 | Sebastian Gibb | 2021-08-01 | chore: rebuild site |
| Rmd | 91bfbd2 | Sebastian Gibb | 2021-08-01 | feat: add first article draft with pandoc filters |
library("ameld")
library("data.table")
library("targets")
library("mlr3")
library("mlr3misc")
library("mlr3proba")
library("mlr3viz")
library("viridis")
library("ggplot2")1 Benchmark results of machine learning algorithms
Figure 1.1: Benchmark results of machine learning algorithms.
agg <- bmrk_results$aggregate(msr("surv.cindex", id = "harrell"))
agg[, `:=`
(nr = NULL, resample_result = NULL, resampling_id = NULL, iters = NULL)
]
agg <- subset(agg, task_id == "zlog_eldd" & !grepl("^scale", learner_id))
agg[, task_id := NULL]
setorder(agg, -harrell)
knitr::kable(agg, col.names = c("Learner ID", "Harrell C"))| Learner ID | Harrell C |
|---|---|
| ridge.tuned | 0.9161824 |
| elasticnet.tuned | 0.9134508 |
| ranger.tuned | 0.9123586 |
| penlasso.tuned | 0.9117655 |
| penridge.tuned | 0.9109179 |
| lasso.tuned | 0.9104912 |
| surv.rfsrc.tuned | 0.9066976 |
| cox | 0.8677244 |
| svmvanbelle1.tuned | 0.8314557 |
| surv.xgboost.tuned | 0.8267293 |
| coxtime.tuned | 0.8249875 |
| deepsurv.tuned | 0.8212755 |
| svmregression.tuned | 0.8015032 |
2 AUROC trend
tar_load(timeROC_MELD)
tar_load(timeROC_MELDNa)
tar_load(timeROC_MELDPlus7)
tar_load(timeROC_RCV)
m <- list(
MELD = timeROC_MELD,
"MELD-Na" = timeROC_MELDNa,
"MELD-Plus7" = timeROC_MELDPlus7,
RCV = timeROC_RCV
)
plot_surv_roc_trend(m)
3 Variable importance
Figure 3.1: Variable importance by frequency of bootstrap selections.
Figure 3.2: Variable importance by logrank in random forest.
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-unknown-linux-gnu (64-bit)
Matrix products: default
BLAS/LAPACK: /gnu/store/ras6dprsw3wm3swk23jjp8ww5dwxj333-openblas-0.3.18/lib/libopenblasp-r0.3.18.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.6 viridis_0.6.2 viridisLite_0.4.0 mlr3viz_0.5.9
[5] mlr3proba_0.4.11 mlr3misc_0.10.0 mlr3_0.13.3 targets_0.12.1
[9] data.table_1.14.2 ameld_0.0.23 survival_3.3-1 glmnet_4.1-4
[13] Matrix_1.4-1
loaded via a namespace (and not attached):
[1] fs_1.5.2 bbotk_0.5.3 rprojroot_2.0.3
[4] numDeriv_2016.8-1.1 mlr3pipelines_0.4.1 tools_4.2.0
[7] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 colorspace_2.0-3 withr_2.5.0
[13] tidyselect_1.1.2 gridExtra_2.3 processx_3.5.3
[16] compiler_4.2.0 git2r_0.30.1 cli_3.3.0
[19] ooplah_0.2.0 lgr_0.4.3 timeROC_0.4
[22] labeling_0.4.2 bookdown_0.26 sass_0.4.1
[25] scales_1.2.0 checkmate_2.1.0 mvtnorm_1.1-3
[28] pec_2022.05.04 callr_3.7.0 palmerpenguins_0.1.0
[31] mlr3tuning_0.13.1 stringr_1.4.0 digest_0.6.29
[34] mlr3extralearners_0.5.37 rmarkdown_2.14 param6_0.2.4
[37] paradox_0.9.0 set6_0.2.4 pkgconfig_2.0.3
[40] htmltools_0.5.2 parallelly_1.31.1 fastmap_1.1.0
[43] highr_0.9 rlang_1.0.2 shape_1.4.6
[46] jquerylib_0.1.4 generics_0.1.2 farver_2.1.0
[49] jsonlite_1.8.0 dplyr_1.0.9 magrittr_2.0.3
[52] Rcpp_1.0.8.3 munsell_0.5.0 fansi_1.0.3
[55] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[58] yaml_2.3.5 grid_4.2.0 parallel_4.2.0
[61] dictionar6_0.1.3 listenv_0.8.0 promises_1.2.0.1
[64] crayon_1.5.1 lattice_0.20-45 splines_4.2.0
[67] timereg_2.0.2 knitr_1.39 ps_1.7.0
[70] pillar_1.7.0 igraph_1.3.1 uuid_1.1-0
[73] base64url_1.4 future.apply_1.9.0 codetools_0.2-18
[76] glue_1.6.2 evaluate_0.15 vctrs_0.4.1
[79] httpuv_1.6.5 foreach_1.5.2 distr6_1.6.9
[82] gtable_0.3.0 purrr_0.3.4 future_1.26.1
[85] xfun_0.31 prodlim_2019.11.13 later_1.3.0
[88] tibble_3.1.7 iterators_1.0.14 lava_1.6.10
[91] workflowr_1.7.0 globals_0.15.0 ellipsis_0.3.2