highmlr_screen: Pre-screen features when p is very large
In highMLR: Machine Learning Feature Selection for High Dimensional Survival Data

highmlr_screen

R Documentation

Pre-screen features when p is very large

Description

Lightweight filter before the main pipeline (e.g. to drop features with low variance or low marginal association).

Usage

highmlr_screen(
  data,
  time,
  status,
  features = NULL,
  filter = c("variance", "univariate_p", "none"),
  keep = 1000L
)

Arguments

`data`, `time`, `status`, `features`	As in [highmlr()].
`filter`	One of '"variance"', '"univariate_p"', '"none"'.
`keep`	Integer, how many features to retain (default 1000).

Value

Character vector of retained feature names.

Examples

## Not run: 
data(srdata)
keep <- highmlr_screen(srdata, "OS", "event",
                       filter = "variance", keep = 500)
fit <- highmlr(srdata, "OS", "event", features = keep, method = "coxnet")

## End(Not run)

highMLR documentation built on May 23, 2026, 5:07 p.m.