This screening algorithm uses cv.glmnet to select covariates.
Unlike LASSO (alpha = 1), which drops correlated features, Elastic Net
(alpha = 0.5 by default) shrinks correlated groups of features together,
making it ideal for selecting entire biological pathways.
Usage
screen.elasticnet(
time,
event,
X,
obsWeights = NULL,
alpha = 0.5,
minscreen = 2,
nfolds = 10,
nlambda = 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.
- alpha
Numeric penalty exponent for
glmnet. Defaults to 0.5 (Elastic Net).- minscreen
Integer. Minimum number of covariates to return. Defaults to 2.
- nfolds
Integer. Number of folds for cross-validation. Defaults to 10.
- nlambda
Integer. Number of penalty parameters to search over. Defaults to 100.
- ...
Additional arguments passed to
screen.glmnet.
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.elasticnet(
time = dat$duration,
event = dat$event,
X = X,
alpha = 0.5,
minscreen = 2,
nfolds = 3,
nlambda = 20
)
}
#> x0 x1 x2 x3 x4
#> TRUE TRUE FALSE FALSE FALSE