---
title: "Random Forest Survival Analysis with ggRandomForests"
author: "John Ehrlinger"
date: today
format:
  html:
    toc: true
    toc-depth: 3
    html-math-method: mathjax
    code-fold: false
bibliography: ggRandomForests.bib
vignette: >
  %\VignetteIndexEntry{Random Forest Survival Analysis with ggRandomForests}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
---

::: {.callout-warning}
## Work in progress
This vignette is under active development. Code examples and narrative may
change before the next release.
:::

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  fig.align = "center",
  fig.width = 7,
  fig.height = 5,
  message = FALSE,
  warning = FALSE,
  comment = "#>"
)
options(mc.cores = 1, rf.cores = 1)
```

# Introduction

Random forests [@Breiman:2001] are a non-parametric ensemble method that
requires no distributional assumptions on the relation between covariates and
the response. Random survival forests (RSF) [@Ishwaran:2007a; @Ishwaran:2008]
extend the method to right-censored, time-to-event data by growing trees with a
log-rank splitting rule and aggregating Kaplan--Meier estimates within terminal
nodes.

The **randomForestSRC** package [@Ishwaran:RFSRC:2014] provides a unified
implementation for survival, regression, and classification forests.
**ggRandomForests** extracts tidy data objects from `rfsrc` fits and renders
them with **ggplot2** [@Wickham:2009], making it straightforward to explore how
a forest is constructed, which variables matter, and how the response depends on
individual predictors.

This vignette demonstrates a complete random survival forest workflow on the
primary biliary cirrhosis (PBC) data set [@fleming:1991]:

1. **Data preparation and exploration** --- cleaning, EDA, Kaplan--Meier curves
2. **Growing the forest** --- fitting an RSF, checking convergence and OOB error
3. **Variable selection** --- VIMP and minimal depth via `max.subtree()`
4. **Dependence plots** --- variable dependence and partial dependence via
   `gg_variable()` and `gg_partial_rfsrc()`
5. **Variable interactions** --- conditioning plots and interactive 3-D partial
   dependence surfaces with **plotly**

```{r packages}
library(ggplot2)
library(dplyr)
library(tidyr)
library(randomForestSRC)
library(survival)

if (requireNamespace("ggRandomForests", quietly = TRUE)) {
  library(ggRandomForests)
} else if (requireNamespace("pkgload", quietly = TRUE)) {
  pkgload::load_all(export_all = FALSE, helpers = FALSE, attach_testthat = FALSE)
} else {
  stop("Install ggRandomForests (or pkgload for dev builds) to render this vignette.")
}

theme_set(theme_bw())

# Plotting constants
event_marks  <- c(1, 4)
event_labels <- c("Censored", "Death")
event_colors <- c("steelblue", "firebrick")
```

# Data: Primary Biliary Cirrhosis (PBC)

The PBC study consists of 424 patients referred to Mayo Clinic between 1974 and
1984, of whom 312 were randomized into a trial of D-penicillamine (DPCA) versus
placebo. The data is described in @fleming:1991 Chapter 0.2, with a proportional
hazards model developed in Chapter 4.4. We use the copy bundled with
**randomForestSRC**.

```{r data-load}
data("pbc", package = "randomForestSRC")
```

## Data cleaning

We convert `days` to `years`, recode `treatment` as a factor, and coerce
low-cardinality numeric columns (five or fewer unique values, including binary
0/1 indicators) to factors. We avoid converting binary columns to `logical`
because `randomForestSRC::partial.rfsrc()` does not handle logical predictors
correctly in survival forests.

```{r data-clean}
pbc <- pbc |>
  mutate(
    years     = days / 365.25,
    age       = age / 365.25,
    treatment = factor(
      ifelse(treatment == 1, "DPCA",
             ifelse(treatment == 2, "Placebo", NA)),
      levels = c("DPCA", "Placebo")
    )
  ) |>
  select(-days)

# Low-cardinality numerics (including binary 0/1) to factor.
# NOTE: do NOT convert to logical — partial.rfsrc() fails with logical
# predictors in survival forests (randomForestSRC <= 3.5.1).
# Exclude the response columns (status, years) from conversion.
resp_cols <- c("status", "years")
for (nm in setdiff(names(pbc), resp_cols)) {
  v <- pbc[[nm]]
  if (is.numeric(v) && !is.factor(v) && length(unique(v[!is.na(v)])) <= 5) {
    pbc[[nm]] <- factor(v)
  }
}
```

```{r variable-labels}
# Human-readable labels for plot axes
st_labs <- c(
  status       = "Death Event",
  treatment    = "Treatment",
  age          = "Age (years)",
  sex          = "Female",
  ascites      = "Ascites",
  hepato       = "Hepatomegaly",
  spiders      = "Spiders",
  edema        = "Edema (0, 0.5, 1)",
  bili         = "Serum Bilirubin (mg/dl)",
  chol         = "Serum Cholesterol (mg/dl)",
  albumin      = "Albumin (gm/dl)",
  copper       = "Urine Copper (ug/day)",
  alk.phos     = "Alkaline Phosphatase (U/liter)",
  ast          = "SGOT (U/ml)",
  trig         = "Triglycerides (mg/dl)",
  platelet     = "Platelets (per cubic ml/1000)",
  prothrombin  = "Prothrombin Time (sec)",
  stage        = "Histologic Stage",
  years        = "Follow-up Time (years)"
)
```

## Exploratory data analysis

Good practice before modeling: scan categorical variables as stacked histograms
over follow-up time, and continuous variables as scatter plots colored by event
status.

```{r eda-categorical, fig.height=4, fig.cap="EDA for categorical variables. Bar color indicates factor level; white = missing."}
cls <- sapply(pbc, class)
cnt_idx <- which(cls %in% c("numeric", "integer"))
fct_idx <- setdiff(seq_along(pbc), cnt_idx)
fct_idx <- union(fct_idx, which(names(pbc) == "years"))

dta_cat <- suppressWarnings(
  pbc[, fct_idx] |>
    pivot_longer(-years, names_to = "variable", values_to = "value",
                 values_transform = list(value = as.character))
)

ggplot(dta_cat, aes(x = years, fill = value)) +
  geom_histogram(color = "black", binwidth = 1) +
  labs(y = "", x = st_labs["years"]) +
  scale_fill_brewer(palette = "RdBu", na.value = "white") +
  facet_wrap(~variable, scales = "free_y", nrow = 2) +
  theme(legend.position = "none")
```

```{r eda-continuous, fig.height=4, fig.cap="EDA for continuous variables. Points colored by death event; rug marks indicate missing values."}
cnt_idx <- union(cnt_idx, which(names(pbc) == "status"))
dta_cont <- pbc[, cnt_idx] |>
  pivot_longer(c(-years, -status),
               names_to = "variable", values_to = "value")

ggplot(dta_cont |> filter(!is.na(value)),
       aes(x = years, y = value, color = factor(status), shape = factor(status))) +
  geom_point(alpha = 0.4) +
  geom_rug(data = dta_cont |> filter(is.na(value)), color = "grey50") +
  labs(y = "", x = st_labs["years"], color = "Death", shape = "Death") +
  scale_color_manual(values = event_colors) +
  scale_shape_manual(values = event_marks) +
  facet_wrap(~variable, scales = "free_y", ncol = 4) +
  theme(legend.position = c(0.8, 0.2))
```

Look for patterns of missingness (white bars, rug marks) and extreme values that
fall outside the biological range.

## Kaplan--Meier survival by treatment

We restrict to the 312 trial patients and construct KM curves with `gg_survival`.

```{r km-survival, fig.cap="KM survival estimates by treatment group with 95% confidence bands."}
pbc_trial <- pbc |> filter(!is.na(treatment))
pbc_test  <- pbc |> filter(is.na(treatment))

gg_km <- gg_survival(interval = "years", censor = "status",
                     by = "treatment", data = pbc_trial,
                     conf.int = 0.95)

plot(gg_km) +
  labs(y = "Survival Probability", x = "Time (years)",
       color = "Treatment", fill = "Treatment") +
  theme(legend.position = c(0.2, 0.2)) +
  coord_cartesian(ylim = c(0, 1.01))
```

The curves largely overlap, consistent with the original finding that DPCA
offered no clear survival benefit over placebo [@fleming:1991].

```{r km-cumhaz, fig.cap="KM cumulative hazard estimates by treatment group."}
plot(gg_km, type = "cum_haz") +
  labs(y = "Cumulative Hazard", x = "Time (years)",
       color = "Treatment", fill = "Treatment") +
  theme(legend.position = c(0.2, 0.8)) +
  coord_cartesian(ylim = c(-0.02, 1.22))
```

We can also stratify on continuous variables. Here we reproduce the bilirubin
groupings from @fleming:1991 Figure 4.4.2:

```{r km-bili, fig.width=6, fig.cap="KM survival stratified by bilirubin groups."}
pbc_bili <- pbc_trial |>
  mutate(bili_grp = cut(bili, breaks = c(0, 0.8, 1.3, 3.4, 29)))

plot(gg_survival(interval = "years", censor = "status",
                 by = "bili_grp", data = pbc_bili),
     error = "none") +
  labs(y = "Survival Probability", x = "Time (years)", color = "Bilirubin")
```

Higher bilirubin strongly predicts worse survival --- an effect the random forest
will rediscover without any prior specification.

# Growing a Random Survival Forest

Several predictors in the PBC trial data contain missing values (cholesterol,
copper, triglycerides, and others). We handle these with a two-step approach:
first impute using `impute.rfsrc()`, which uses the random forest proximity
structure to fill in missing values, then fit the survival forest on the
complete imputed data. This keeps the fitted forest object free of imputation
state, which is required for `partial.rfsrc()` to work correctly.

```{r rfsrc-fit}
# Step 1: impute missing values via random forest proximity
pbc_imputed <- impute.rfsrc(Surv(years, status) ~ .,
                             data    = pbc_trial,
                             ntree   = 500,
                             nimpute = 2)

# Step 2: grow the survival forest on the complete imputed data
rfsrc_pbc <- rfsrc(Surv(years, status) ~ .,
                   data      = pbc_imputed,
                   nsplit    = 10,
                   tree.err  = TRUE,
                   importance = TRUE)
```

The forest grew `r rfsrc_pbc$ntree` trees, splitting on
`r rfsrc_pbc$mtry` randomly selected candidate variables at each node, and
stopping at a minimum terminal node size of `r rfsrc_pbc$nodesize`.

## OOB error convergence

```{r error-plot, fig.height=4, fig.cap="OOB prediction error vs. number of trees."}
plot(gg_error(rfsrc_pbc))
```

The error stabilizes well before 1000 trees, indicating the forest is large
enough for reliable predictions.

## OOB predicted survival

```{r rfsrc-predicted, fig.cap="OOB predicted survival curves. Blue = censored, red = death events."}
gg_rsf <- plot(gg_rfsrc(rfsrc_pbc), alpha = 0.2) +
  scale_color_manual(values = event_colors) +
  theme(legend.position = "none") +
  labs(y = "Survival Probability", x = "Time (years)") +
  coord_cartesian(ylim = c(-0.01, 1.01))
gg_rsf
```

Each curve represents one patient's OOB prediction, extended to the last
follow-up time. Red (death event) curves generally fall faster, confirming the
forest discriminates between risk groups.

```{r rfsrc-by-treatment, fig.cap="Median predicted survival by treatment group with 95% confidence bands."}
plot(gg_rfsrc(rfsrc_pbc, by = "treatment")) +
  theme(legend.position = c(0.2, 0.2)) +
  labs(y = "Survival Probability", x = "Time (years)") +
  coord_cartesian(ylim = c(-0.01, 1.01))
```

## Test set predictions

The non-trial patients (`pbc_test`) have substantial missing data.
`predict.rfsrc()` handles these transparently at prediction time via
`na.action = "na.impute"` — this is distinct from training-time imputation
and does not affect the fitted forest object.

```{r predict-test, fig.cap="Predicted survival for non-trial patients (test set)."}
rfsrc_pbc_test <- predict(rfsrc_pbc, newdata = pbc_test,
                          na.action = "na.impute",
                          importance = TRUE)

plot(gg_rfsrc(rfsrc_pbc_test), alpha = 0.2) +
  scale_color_manual(values = event_colors) +
  theme(legend.position = "none") +
  labs(y = "Survival Probability", x = "Time (years)") +
  coord_cartesian(ylim = c(-0.01, 1.01))
```

Because the training curves use OOB estimates, both plots represent
out-of-sample predictions and are directly comparable.

# Variable Selection

Random forest uses all available predictors. To understand which matter most, we
examine variable importance (VIMP) and minimal depth.

## Variable importance (VIMP)

VIMP measures the increase in prediction error when a variable is randomly
permuted. Large positive values mean the variable is essential for accuracy;
negative values suggest noise is more informative than the variable.

```{r vimp-plot, fig.width=6, fig.cap="Variable importance ranking. Blue = positive VIMP, red = negative."}
plot(gg_vimp(rfsrc_pbc), lbls = st_labs) +
  theme(legend.position = c(0.8, 0.2)) +
  labs(fill = "VIMP > 0")
```

Bilirubin ranks highest, followed by copper, prothrombin time, albumin, and age
--- closely matching the variables selected in the @fleming:1991 proportional
hazards model.

## Minimal depth

Minimal depth [@Ishwaran:2010] ranks variables by how close to the root node
they first split, on average. Variables that partition large portions of the
population early are considered most important.

```{r varsel}
md_pbc <- max.subtree(rfsrc_pbc)
```

The `max.subtree()` function computes minimal depth for each variable. The
threshold is `r round(md_pbc$threshold, 2)`, selecting
`r length(md_pbc$topvars)` variables: `r paste(md_pbc$topvars, collapse = ", ")`.

Both selection methods agree on the key predictors: `bili`, `albumin`, `copper`,
`prothrombin`, and `age`. We add `edema` (selected by the @fleming:1991 model)
for the remainder of the analysis.

```{r select-vars}
xvar     <- c("bili", "albumin", "copper", "prothrombin", "age")
xvar_cat <- "edema"
xvar_all <- c(xvar, xvar_cat)
```

# Variable Dependence

## Variable dependence plots

Variable dependence shows each patient's predicted survival at a given time
horizon plotted against a predictor of interest. Points are colored by event
status; a loess smooth indicates the trend.

```{r vardep-bili, fig.height=4, fig.cap="Variable dependence of survival at 1 and 3 years on bilirubin."}
gg_v <- gg_variable(rfsrc_pbc, time = c(1, 3),
                    time.labels = c("1 Year", "3 Years"))

plot(gg_v, xvar = "bili", alpha = 0.4) +
  labs(y = "Survival", x = st_labs["bili"]) +
  theme(legend.position = "none") +
  scale_color_manual(values = event_colors, labels = event_labels) +
  scale_shape_manual(values = event_marks, labels = event_labels) +
  coord_cartesian(ylim = c(-0.01, 1.01))
```

Survival drops sharply with increasing bilirubin, and the 3-year curve drops
further than the 1-year curve, suggesting a non-proportional hazards effect.

```{r vardep-panel, fig.height=5, fig.cap="Variable dependence at 1 and 3 years for continuous predictors."}
plot(gg_v, xvar = xvar[-1], panel = TRUE, alpha = 0.4) +
  labs(y = "Survival") +
  theme(legend.position = "none") +
  scale_color_manual(values = event_colors, labels = event_labels) +
  scale_shape_manual(values = event_marks, labels = event_labels) +
  coord_cartesian(ylim = c(-0.05, 1.05))
```

The plots confirm survival increases with albumin and decreases with copper,
prothrombin time, and age. The divergence between time curves for `copper`
further supports a non-proportional hazards mechanism.

```{r vardep-categorical, fig.height=4, fig.cap="Variable dependence on edema (categorical)."}
plot(gg_v, xvar = xvar_cat, alpha = 0.4) +
  labs(y = "Survival") +
  theme(legend.position = "none") +
  scale_color_manual(values = event_colors, labels = event_labels) +
  scale_shape_manual(values = event_marks, labels = event_labels) +
  coord_cartesian(ylim = c(-0.01, 1.02))
```

Patients with edema = 1 (edema despite diuretics) have markedly lower predicted
survival.

## Partial dependence

::: {.callout-warning}
**Known issue (draft):** `randomForestSRC::partial.rfsrc()` currently fails for
survival forests in randomForestSRC ≥ 3.3.  The partial dependence and
interactive surface sections below will show an error until this upstream bug
is resolved.  All other sections of this vignette are fully functional.
:::

Partial dependence integrates out the effects of other covariates, giving a
risk-adjusted view of how each predictor influences the response
[@Friedman:2000]. We use `gg_partial_rfsrc()` which calls
`randomForestSRC::partial.rfsrc()` directly and returns a `gg_partial_rfsrc`
object.  For survival forests, the result includes a `time` column so
`plot(pd)` produces one curve per predictor value over time, faceted by
variable name.

```{r partial-dep, error=TRUE, fig.height=5, fig.cap="Partial dependence of survival at approximately 1 and 3 years on continuous predictors."}
# partial.rfsrc() requires times that match the model's time.interest grid;
# gg_partial_rfsrc() snaps the requested values to the nearest observed times.
ti   <- rfsrc_pbc$time.interest
t1yr <- ti[which.min(abs(ti - 1))]
t3yr <- ti[which.min(abs(ti - 3))]

pd <- gg_partial_rfsrc(rfsrc_pbc, xvar.names = xvar,
                       partial.time = c(t1yr, t3yr))

# Quick S3 plot — survival forests produce time-series curves per predictor value
plot(pd)
```

For a publication-ready layout with custom colour scale, access `pd$continuous`
directly:

```{r partial-dep-custom, error=TRUE, fig.height=5, fig.cap="Partial dependence (custom styling)."}
ggplot(pd$continuous, aes(x = x, y = yhat,
                          color = factor(round(time, 2)),
                          group = factor(time))) +
  geom_line(linewidth = 1) +
  facet_wrap(~name, scales = "free_x") +
  labs(y = "Predicted Survival", x = "", color = "Year") +
  scale_color_manual(values = setNames(c("steelblue", "firebrick"),
                                       as.character(round(c(t1yr, t3yr), 2)))) +
  theme_bw()
```

The partial dependence curves at approximately 1 and 3 years confirm the
variable dependence findings and support the log-transforms used in the
@fleming:1991 model for `bili`, `albumin`, and `prothrombin`. The divergence
between time curves for `bili` and `copper` supports non-proportional hazards.

## Conditional dependence

To investigate interactions, we can condition variable dependence on group
membership in another variable. Here we examine the dependence of survival on
bilirubin, stratified by edema group.

```{r coplot-edema, fig.width=7, fig.height=4, fig.cap="Variable dependence on bilirubin, conditional on edema group."}
gg_v1 <- gg_variable(rfsrc_pbc, time = 1)
gg_v1$edema <- paste("edema =", gg_v1$edema)

plot(gg_v1, xvar = "bili", alpha = 0.5) +
  labs(y = "Survival at 1 Year", x = st_labs["bili"]) +
  theme(legend.position = "none") +
  scale_color_manual(values = event_colors, labels = event_labels) +
  scale_shape_manual(values = event_marks, labels = event_labels) +
  facet_grid(~edema) +
  coord_cartesian(ylim = c(-0.01, 1.01))
```

The decreasing trend with bilirubin holds across all edema groups, but survival
is uniformly lower in the edema = 1 panel, confirming an additive effect.

We can also condition on a continuous variable by binning into quantile groups:

```{r coplot-albumin, fig.width=7, fig.height=5, fig.cap="Variable dependence on bilirubin, conditional on albumin groups."}
albumin_cts <- quantile_pts(gg_v1$albumin, groups = 6, intervals = TRUE)
gg_v1$albumin_grp <- cut(gg_v1$albumin, breaks = albumin_cts)
levels(gg_v1$albumin_grp) <- paste("albumin =",
                                    levels(gg_v1$albumin_grp))

plot(gg_v1, xvar = "bili", alpha = 0.5) +
  labs(y = "Survival at 1 Year", x = st_labs["bili"]) +
  theme(legend.position = "none") +
  scale_color_manual(values = event_colors, labels = event_labels) +
  scale_shape_manual(values = event_marks, labels = event_labels) +
  facet_wrap(~albumin_grp) +
  coord_cartesian(ylim = c(-0.01, 1.01))
```

The effect of bilirubin attenuates at higher albumin levels, suggesting an
interaction between these two liver function markers.

# Interactive Partial Dependence Surfaces

For a richer view of the interaction between bilirubin and albumin, we construct
a partial dependence surface. We compute partial dependence on a grid of 25
albumin values, each evaluated at 25 bilirubin points.

```{r surface-data, error=TRUE, cache=FALSE}
# Create grid of albumin values
alb_grid <- quantile_pts(pbc_trial$albumin, groups = 25)

# For each albumin value, compute partial dependence on bili at ~1 year
surface_list <- lapply(alb_grid, function(alb_val) {
  newx <- pbc_trial[, rfsrc_pbc$xvar.names, drop = FALSE]
  newx$albumin <- alb_val
  pd_alb <- gg_partial_rfsrc(rfsrc_pbc, xvar.names = "bili",
                              newx = newx, partial.time = t1yr)
  df <- pd_alb$continuous
  df$albumin <- alb_val
  df
})

surface_df <- bind_rows(surface_list)
```

```{r plotly-surface, error=TRUE, fig.cap="Interactive partial dependence surface: survival as a function of bilirubin and albumin."}
if (!exists("surface_df")) {
  message("surface_df not available — skipping plotly surface (see surface-data chunk error above).")
} else if (requireNamespace("plotly", quietly = TRUE)) {
  # Reshape for surface
  library(plotly)

  surface_wide <- surface_df |>
    select(bili = x, albumin, survival = yhat) |>
    arrange(albumin, bili)

  # Create matrix form
  bili_vals <- sort(unique(surface_wide$bili))
  alb_vals  <- sort(unique(surface_wide$albumin))
  z_matrix  <- matrix(surface_wide$survival,
                       nrow = length(alb_vals),
                       ncol = length(bili_vals),
                       byrow = TRUE)

  plot_ly(x = bili_vals, y = alb_vals, z = z_matrix) |>
    add_surface(colorscale = "Viridis", showscale = TRUE) |>
    layout(
      scene = list(
        xaxis = list(title = "Bilirubin"),
        yaxis = list(title = "Albumin"),
        zaxis = list(title = "Survival")
      )
    )
} else {
  message("Install the plotly package for interactive 3D surfaces.")
  # Fallback: contour plot with ggplot2
  ggplot(surface_df, aes(x = x, y = albumin, fill = yhat)) +
    geom_tile() +
    scale_fill_viridis_c(name = "Survival") +
    labs(x = "Bilirubin", y = "Albumin") +
    theme_bw()
}
```

The surface shows that survival is highest when bilirubin is low and albumin is
high (upper-left corner), and drops steeply as bilirubin increases or albumin
decreases. The non-planar shape of the surface --- particularly the steep
gradient at low albumin and high bilirubin --- confirms the interaction detected
in the conditional plots.

## Brier Score and CRPS

The Brier score measures prediction accuracy at each point on the event-time
grid by comparing predicted survival probabilities to observed outcomes, with
inverse-probability-of-censoring weighting (IPCW) to account for
right-censoring [@graf:1999]. `gg_brier()` wraps
`randomForestSRC::get.brier.survival()` and returns a tidy data frame ready
for plotting.

```{r brier-overall, fig.width=7, fig.height=3.5, fig.cap="Time-resolved Brier score for the PBC survival forest."}
gg_bs <- gg_brier(rfsrc_pbc)
plot(gg_bs)
```

The score starts near zero (all subjects alive, predictions trivially correct),
peaks around the median event time where uncertainty is greatest, then
decreases as the remaining at-risk pool shrinks.

The 15--85% per-subject envelope shows how spread the individual Brier
contributions are at each time. A narrow ribbon indicates the forest assigns
similar predicted risks across subjects; a wide ribbon points to
heterogeneous risk predictions.

```{r brier-envelope, fig.width=7, fig.height=3.5, fig.cap="Brier score with 15--85% per-subject envelope."}
plot(gg_bs, envelope = TRUE)
```

The running CRPS (continuous ranked probability score) integrates the Brier
score over time, normalised by elapsed time. It provides a single
time-averaged accuracy measure that penalises poorly calibrated predictions
throughout the follow-up period.

```{r brier-crps, fig.width=7, fig.height=3.5, fig.cap="Running CRPS for the PBC survival forest."}
plot(gg_bs, type = "crps")
```

The integrated CRPS --- a scalar summary of overall calibration --- is stored
as an attribute and can be retrieved with:

```{r brier-crps-scalar}
attr(gg_bs, "crps_integrated")
```

# Conclusion

This vignette demonstrated a complete random survival forest analysis using
**randomForestSRC** and **ggRandomForests**:

- **Kaplan--Meier curves** via `gg_survival()` confirmed no treatment effect in
  the PBC trial, consistent with @fleming:1991.
- **OOB error** via `gg_error()` showed the forest stabilized quickly.
- **VIMP** via `gg_vimp()` and **minimal depth** via `max.subtree()` both
  identified bilirubin, albumin, copper, prothrombin, and age as key
  predictors --- matching the proportional hazards model.
- **Variable dependence** via `gg_variable()` revealed non-proportional hazards
  effects for bilirubin and copper.
- **Partial dependence** via `gg_partial_rfsrc()` provided risk-adjusted
  confirmation and supported the log-transforms used in parametric models.
- **Conditional plots** and **interactive surfaces** exposed the bilirubin--albumin
  interaction.
- **Brier score and CRPS** via `gg_brier()` quantified time-resolved prediction
  accuracy and overall calibration of the survival forest.

The **ggRandomForests** design separates data extraction from plotting: every
`gg_*()` function returns a tidy data frame that can be plotted with the
package's `plot()` methods or fed directly into custom `ggplot2` workflows.

# References
