compare: Compare several survival learners on shared resampling folds

In survalis: Interpretable Survival Machine Learning Framework

Compare several survival learners on shared resampling folds

Description

Evaluates each learner in models on the same set of resampling splits, so the per-fold values are paired across models. Returns a leaderboard plus the full per-split table.

Usage

compare(
  formula,
  data,
  models,
  specs = list(),
  times = NULL,
  resampling = cv(),
  metrics = c("cindex", "ibs"),
  tune = FALSE,
  ncores = 1,
  ...
)

Arguments

formula	A survival formula.
data	A data frame.
models	Character vector of learner ids, or a list mixing ids and custom learners (see fit()). List names, when given, label the learners.
specs	Named list of engine-argument lists, keyed by learner label (specs[["ranger"]] <- list(num.trees = 500)).
times	Evaluation grid; NULL picks up to 50 event-time quantiles (5th to 95th percentile).
resampling	A resampling spec (default cv()); the split set is drawn once and reused for every model.
metrics	Character vector from listmetrics()$metric.
tune	Logical. When TRUE, each tunable model is tuned once on the full data (its default grid) and the winning configuration is then evaluated on the shared folds. This is not nested CV, so the tuned results are optimistic; a message is emitted. Default FALSE.
ncores	Cores for the split loop (default 1).
...	Unused.

Value

A survalis_compare object: leaderboard (model x metric, mean and se), per_split (long), times, resampling, metrics, models, tuned.

Examples

cmp <- compare(Surv(time, status) ~ age + karno + celltype, veteran,
               models = c("coxph", "ranger"), times = c(90, 180, 365),
               resampling = cv(3))
cmp
plot(cmp)

survalis documentation built on Nov. 14, 2026, 5:10 p.m.