uniCoxCV: Function to cross-validate a high dimensional Cox survival...

In uniCox: Univarate shrinkage prediction in the Cox model

Description

Function to cross-validate a high dimensional Cox survival model using Univariate Shrinkage

Usage

uniCoxCV(fit,x,y,status,nfolds=5,folds=NULL)

Arguments

fit	object returned by call to uniCox
x	Feature matrix, n obs by p variables
y	Vector of n survival times
status	Vector of n censoring indicators (1= died or event occurred, 0=survived, or event was censored)
nfolds	Number of cross-valdiation folds
folds	Optional list of sample numbers defining folds

Details

This function does cross-validation for a prediction model for survival data with high-dimensional covariates, using the Unvariate Shringae method.

Value

A list with components

devcvm	Average drop in CV deviance for each lambda value
ncallcvm=ncallcvm	Average number of features with non-zero wts in the CV, for each lambda value
se.devcvm	Standard error of average drop in CV deviance for each lambda value
devcv	Drop in CV deviance for each lambda value
ncallcv	Number of features with non-zero wts in the CV, for each lambda value
folds	Indices for CV folds
call	Call to this function

Source

Tibshirani, R. Univariate shrinkage in the Cox model for high dimensional data (2009). http://www-stat.stanford.edu/~tibs/ftp/cus.pdf To appear SAGMB.

Examples

library(survival)
# generate some data
x=matrix(rnorm(200*1000),ncol=1000)
y=abs(rnorm(200))
x[y>median(y),1:50]=x[y>median(y),1:50]+3
status=sample(c(0,1),size=200,replace=TRUE)

# fit uniCox model
a=uniCox(x,y,status)

# do cross-validation to examine choice of lambda
aa=uniCoxCV(a,x,y,status)

uniCox documentation built on May 29, 2017, 2:19 p.m.