uniCoxCV {uniCox} | R Documentation |
Function to cross-validate a high dimensional Cox survival model using Univariate Shrinkage
uniCoxCV(fit,x,y,status,nfolds=5,folds=NULL)
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 |
This function does cross-validation for a prediction model for survival data with high-dimensional covariates, using the Unvariate Shringae method.
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 |
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.
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)