lasso_pro: Train Lasso Cox Proportional Hazards Model

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train Lasso Cox Proportional Hazards Model

Description

Fits a Cox proportional hazards model regularized by the Lasso (L1) penalty. Uses cross-validation to select the optimal lambda.

Usage

lasso_pro(X, y_surv, tune = FALSE)

Arguments

X	A data frame of predictors.
y_surv	A Surv object containing time and status.
tune	Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically).

Value

An object of class survival_glmnet and pro_model.

Examples

  library(survival)
  # Create dummy data
  set.seed(123)
  df <- data.frame(time = rexp(50), status = sample(0:1, 50, replace=TRUE),
                   var1 = rnorm(50), var2 = rnorm(50))
  y <- Surv(df$time, df$status)
  x <- df[, c("var1", "var2")]

  model <- lasso_pro(x, y)
  print(class(model))

E2E documentation built on March 22, 2026, 1:07 a.m.