Random Survival Forest Screening Algorithm

This screening algorithm uses the randomForestSRC package to select covariates based on their Variable Importance (VIMP). It grows a fast forest and retains features with a VIMP greater than zero.

Usage

screen.rfsrc(
  time,
  event,
  X,
  obsWeights = NULL,
  minscreen = 2,
  ntree = 100,
  ...
)

Arguments

time

Numeric vector of observed follow-up times.

event

Numeric vector of event indicators (1 = event, 0 = censored).

X

Training covariate data.frame or matrix.

obsWeights

Numeric vector of observation weights.

minscreen

Integer. Minimum number of covariates to return. Defaults to 2.

ntree

Integer. Number of trees to grow. Defaults to 100 for fast screening.

...

Additional arguments passed to rfsrc.

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

if (requireNamespace("randomForestSRC", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]

  screen.rfsrc(
    time = dat$duration,
    event = dat$event,
    X = X,
    minscreen = 2,
    ntree = 10
  )
}
#>    x0    x1    x2    x3    x4 
#>  TRUE  TRUE FALSE FALSE FALSE