Generalized Random Survival Forest Learner

Fits an honest generalized random survival forest using grf::survival_forest() and returns conditional survival curves.

Usage

surv.grf(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  num.trees = 1000L,
  mtry = NULL,
  min.node.size = 15L,
  honesty = TRUE,
  prediction.type = c("Kaplan-Meier", "Nelson-Aalen"),
  seed = 1L,
  ...
)

Arguments

time: Observed follow-up time.
event: Observed event indicator.
X: Training covariate data frame.
newdata: Covariate data frame used for prediction.
new.times: Times at which survival probabilities are requested.
obsWeights: Optional non-negative observation weights.
id: Currently ignored.
num.trees: Number of trees.
mtry: Number of candidate variables considered at each split.
min.node.size: Minimum terminal-node size.
honesty: Whether to use honest sample splitting.
prediction.type: Either "Kaplan-Meier" or "Nelson-Aalen".
seed: Integer random seed passed to grf.
...: Additional arguments passed to grf::survival_forest().

Value

A list with numeric matrix pred and fitted object fit.

Examples

if (requireNamespace("grf", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  X <- dat[, grep("^x", names(dat))[1:3], drop = FALSE]
  fit <- surv.grf(
    dat$duration, dat$event, X, X[1:4, , drop = FALSE],
    c(50, 100), num.trees = 50, seed = 1
  )
  dim(fit$pred)
}
#> [1] 4 2