Run LASSO Regression with Cross-Validation

Convenience wrapper for LASSO regression (L1 regularization) using glmnet with cross-validation. Equivalent to run_glmnet with alpha = 1.

Usage

run_lasso(X, y, family = gaussian(), standardize = TRUE, nfolds = 10)

Arguments

X: Design matrix of predictors (n × p).
y: Response vector or survival object.
family: Response family. Can be gaussian(), binomial(), poisson(), Gamma(), or "cox" for Cox regression.
standardize: Logical. Should variables be standardized? Default TRUE.
nfolds: Number of cross-validation folds. Default 10.

Value

A list containing the same components as run_glmnet.

Details

LASSO (Least Absolute Shrinkage and Selection Operator) performs both regularization and variable selection by setting some coefficients to exactly zero. This function provides a convenient interface specifically for LASSO regression.

Examples

if (FALSE) { # \dontrun{
# Simulate sparse data
set.seed(123)
n <- 100; p <- 50
X <- matrix(rnorm(n * p), n, p)
y <- X[,1:3] %*% c(1, -1, 0.5) + rnorm(n)

# Run LASSO
lasso_result <- run_lasso(X, y)
print(lasso_result$selected)
} # }