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High Variance Screening Algorithm (Unsupervised)

Source: R/screener.R
screen.var.Rd

An unsupervised screening algorithm that filters out low-variance features. This is particularly useful for high-dimensional genomic or transcriptomic data where many features remain relatively constant across all observations.

Usage

screen.var(
  time,
  event,
  X,
  obsWeights = NULL,
  keep_fraction = 0.5,
  minscreen = 2,
  ...
)

Arguments

time

Numeric vector of observed follow-up times (Ignored internally).

event

Numeric vector of event indicators (Ignored internally).

X

Training covariate data.frame or matrix.

obsWeights

Numeric vector of observation weights (Ignored internally).

keep_fraction

Numeric value between 0 and 1. The fraction of highest-variance features to retain. Defaults to 0.5 (keeps the top 50%).

minscreen

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

...

Additional ignored arguments.

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

data("metabric", package = "SuperSurv")
dat <- metabric[1:40, ]
x_cols <- grep("^x", names(dat))[1:6]
X <- dat[, x_cols, drop = FALSE]

screen.var(
  time = dat$duration,
  event = dat$event,
  X = X,
  keep_fraction = 0.5,
  minscreen = 2
)
#>    x0    x1    x2    x3    x4    x5 
#>  TRUE  TRUE  TRUE FALSE FALSE FALSE