survivalSL (version 0.98)

LIB_COXaic: Library of the Super Learner for a Cox Model with Selected Covariates

Description

Fit a Cox regression for a selection of covariate.

Usage

LIB_COXaic(formula, data, penalty=NULL)

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 predictoes 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 with a length equals to the number of predictors. It allows the integration of covariates into the final model, i.e. with no selection: the value 0 to force the covariate in the model, 1 otherwise. If NULL, all covariates undergo the selection process.

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 lines

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

# 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))