GLMNET (Lasso) Screening

GLMNET (Lasso) Screening

Usage

screen.glmnet(
  time,
  event,
  X,
  obsWeights = NULL,
  alpha = 1,
  minscreen = 2,
  nfolds = 10,
  nlambda = 100,
  ...
)

Arguments

time

Observed follow-up time.

event

Observed event indicator.

X

Training covariate data.frame.

obsWeights

Observation weights.

alpha

Penalty exponent (1 = lasso).

minscreen

Minimum number of covariates to return. Defaults to 2.

nfolds

Number of CV folds.

nlambda

Number of penalty parameters.

...

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

if (requireNamespace("glmnet", 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.glmnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    alpha = 1,
    minscreen = 2,
    nfolds = 3,
    nlambda = 20
  )
}
#>    x0    x1    x2    x3    x4 
#>  TRUE  TRUE FALSE FALSE FALSE