% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/screener.R
\name{screen.var}
\alias{screen.var}
\title{High Variance Screening Algorithm (Unsupervised)}
\usage{
screen.var(
  time,
  event,
  X,
  obsWeights = NULL,
  keep_fraction = 0.5,
  minscreen = 2,
  ...
)
}
\arguments{
\item{time}{Numeric vector of observed follow-up times (Ignored internally).}

\item{event}{Numeric vector of event indicators (Ignored internally).}

\item{X}{Training covariate data.frame or matrix.}

\item{obsWeights}{Numeric vector of observation weights (Ignored internally).}

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

\item{minscreen}{Integer. Minimum number of covariates to return. Defaults to 2.}

\item{...}{Additional ignored arguments.}
}
\value{
A logical vector of the same length as the number of columns in \code{X},
indicating which variables passed the screening algorithm (\code{TRUE} to keep,
\code{FALSE} to drop).
}
\description{
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.
}
\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
)
}
