Plot Longitudinal Benchmark Metrics — plot

Generates time-dependent performance curves comparing the SuperSurv ensemble against its base learners, or evaluates a single standalone learner.

Usage

plot_benchmark(
  object,
  newdata,
  time,
  event,
  eval_times,
  metrics = c("brier", "auc", "cindex"),
  verbose = FALSE
)

Arguments

object: A fitted SuperSurv object, a fitted standalone learner, or a benchmark returned by eval_benchmark(). A benchmark is plotted without recomputing predictions or metrics.
newdata: A data.frame of test covariates. Omit when object is a benchmark.
time: Numeric vector of observed follow-up times for the test set. Omit when object is a benchmark.
event: Numeric vector of event indicators for the test set. Omit when object is a benchmark.
eval_times: Numeric vector of times at which to evaluate predictions. Omit when object is a benchmark.
metrics: Character vector specifying which plots to return. Options: "brier", "auc", "cindex". Defaults to all three.
verbose: Logical; if TRUE, progress messages are shown. Defaults to FALSE.

Value

A combined patchwork object when patchwork is installed, a single ggplot when one metric is selected, or a named list of ggplots when multiple metrics are requested without patchwork.

Examples

if (requireNamespace("ranger", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:120, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  eval_times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = eval_times,
    event.library = c("surv.coxph", "surv.ranger"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  plot_benchmark(
    object = fit,
    newdata = X,
    time = dat$duration,
    event = dat$event,
    eval_times = eval_times,
    metrics = c("brier")
  )
}