tuneCOXridge: Tune Ridge Cox Regression

In survivalSL: Super Learner for Survival Prediction from Censored Data

Tune Ridge Cox Regression

Description

This function finds the optimal lambda parameter for a ridge Cox regression.

Usage

tuneCOXridge(formula, data, penalty=NULL,
 cv=10, parallel=FALSE, lambda=NULL, seed=NULL)

Arguments

formula	A formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
data	A data frame for training the model with the same covariates as in the formula.
penalty	A numerical vector that allows the covariates not to be penalized. We give the value 0 if we do not want the covariate to be penalized otherwise 1. If NULL, all covariates are penalized.
cv	The value of the number of folds. The default value is 10.
parallel	If TRUE, use parallel foreach to fit each fold. The default is FALSE.
lambda	The values of the regularization parameter lambda optimized over.
seed	A random seed to ensure reproducibility during the cv process. If NULL, a seed is randomly assigned.

Details

The function runs the cv.glmnet function of the glmnet package.

Value

optimal	The value of lambda that gives the minimum cross-validated deviance.
results	The data frame with the cross-validated deviance for each lambda value.

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data("dataDIVAT2")

formula<-Surv(times,failures) ~ age + hla + retransplant + ecd

tune.model <- tuneCOXridge(formula=formula, data=dataDIVAT2,
  cv=5, lambda=seq(0, 10, by=.1))

tune.model$optimal$lambda # the estimated lambda value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_COXridge(formula, data=dataDIVAT2,
  lambda=tune.model$optimal$lambda)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

survivalSL documentation built on July 29, 2026, 5:08 p.m.