cv.coxpath {glmpath}R Documentation

Computes cross-validated log-partial-likelihoods for coxpath

Description

This function computes cross-validated log-partial-likelihoods for coxpath.

Usage

  cv.coxpath(data, method = c("breslow", "efron"), nfold = 5,
             fraction = seq(from=0, to=1, length=100),
             mode = c("norm","lambda"), plot.it = TRUE, se = TRUE, ...)

Arguments

data a list consisting of x: a matrix of features, time: the survival time, and status: censor status with 1 if died and 0 if censored.
method approximation method for tied survival times. Approximations derived by Breslow (1974) and Efron (1977) are available. Default is breslow.
nfold number of folds to be used in cross-validation. Default is nfold=5.
fraction fraction of the L1 norm or log(λ) with respect to their maximum values at which CV values are computed. Default is seq(0,1,length=100).
mode If mode=norm, cross-validation is done at certain values of L1 norm. If mode=lambda, cross-validation is done at certain values of log(λ). Default is norm.
plot.it If TRUE, the CV curve is plotted.
se If TRUE, the standard errors are plotted.
... other options for coxpath

Author(s)

Mee Young Park and Trevor Hastie

References

Mee Young Park and Trevor Hastie (2006) L1 Regularization Path Algorithm for Generalized Linear Models - available at the authors' websites, http://www.stanford.edu/~mypark or http://stat.stanford.edu/~hastie/pub.htm.

See Also

coxpath, plot.coxpath, predict.coxpath

Examples

data(lung.data)
attach(lung.data)
cv <- cv.coxpath(lung.data)
detach(lung.data)

[Package glmpath version 0.91 Index]