survivalSL (version 0.98)

LIB_COXen: Library of the Super Learner for Elastic Net Cox Regression

Description

Fit an elastic net Cox regression for fixed values of the regularization parameters.

Usage

LIB_COXen(formula, data, penalty=NULL, alpha, lambda)

Value

formula

The formula object used for model construction.

model

The estimated model.

data

The data frame used for learning.

times

A vector of numeric values with the times of the predictions.

predictions

A matrix with the predictions of survivals of each subject (lines) for each observed time (columns).

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 whose columns correspond to the variables present 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.

alpha

The value of the regularization parameter alpha for penalizing the partial likelihood.

lambda

The value of the regularization parameter lambda for penalizing the partial likelihood.

Details

The elastic net Cox regression is obtained by using the glmnet package.

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")

# The estimation of the model from the first 200 lignes

formula<-Surv(times,failures) ~ age + hla + retransplant + ecd
model <- LIB_COXen(formula=formula, data=dataDIVAT2[1:200,], lambda=.1, alpha=.1)

# The 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))