supplement.Rmd

---
title: "Supplement"
output:
  bookdown::html_document2:
    toc: false
  bookdown::word_document2:
    keep_md: true
---

```{r knitr_setup, include = FALSE}
```

```{r knitr_setup_word, eval = !knitr::is_html_output(), include = FALSE}
knitr::opts_chunk$set(dpi = 1200, dev = c("png", "pdf"))
```

```{r supplement_setup, echo = FALSE, message = FALSE}
library("ameld")
library("data.table")
library("targets")
library("mlr3")
library("mlr3misc")
library("mlr3proba")
library("mlr3viz")
library("viridis")
library("ggplot2")
```

# Benchmark results of machine learning algorithms

```{r benchmark-plot, fig.cap = "Benchmark results of machine learning algorithms.", echo = FALSE, fig.width = 18, fig.height = 12}
tar_load(bmrk_results)
r <- bmrk_results$clone()
id <- grep("^scale", x = r$learners$learner_id, invert = TRUE, value = TRUE)
r$filter(task_id = "zlog_eldd", learner_id = id)
autoplot(r) +
    geom_boxplot(aes(fill = learner_id)) +
    geom_jitter(position = position_jitter(0.2)) +
    scale_fill_viridis(discrete = TRUE) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

```{r benchmark-table, echo = FALSE}
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"))
```

# AUROC trend

```{r roc-plot, echo = FALSE, fig.width = 18, fig.height = 12}
tar_load(timeROC_MELD)
tar_load(timeROC_MELDNa)
tar_load(timeROC_MELDPlus7)
tar_load(timeROC_RCV)

m <- list(
    AMELD = timeROC_RCV,
    MELD = timeROC_MELD,
    "MELD-Na" = timeROC_MELDNa,
    "MELD-Plus7" = timeROC_MELDPlus7
)
plot_surv_roc_trend(m)
```

# Variable importance

```{r vimpbs, fig.width = 8, fig.height = 10, fig.cap = "Variable importance by frequency of bootstrap selections.", echo = FALSE, message = FALSE}
tar_load(bootrcv)
plot(bootrcv, what = "selected", cex = 0.5)
```

```{r vimprf, fig.width = 8, fig.height = 10, fig.cap = "Variable importance by logrank in random forest.", echo = FALSE, message = FALSE}
tar_load(rngr)
plot_dots(
    sort(rngr$variable.importance),
    main = "Variable importance (random forest)",
    xlab = rngr$splitrule
)
```