cv.AWEnetCC: Computes K-fold cross validated error curve for AEnetCC and...
In AdapEnetClass: A Class of Adaptive Elastic Net Methods for Censored Data

Description Usage Arguments Details Value References See Also Examples

This function computes the K-fold cross validation estimates.

1	cv.AWEnetCC(X, Y, delta, weight, kFold = 10, C, s, lambda2, AEnetCC = T)

`X`	covariate matrix under study, particularly for AFT modelling. The order of matrix covariate is typically n by p.
`Y`	typically the logarithmic of the survival time under AFT models. Otherwise survival time.
`delta`	status. it includes value 1 for uncensored and value 0 for censored subject.
`weight`	vector of observation weights. Weight is based on initial estimator that is obtained from elastic net on the weighted data (see Enet.wls function) or from Gehan estimator (see mrbj function).
`kFold`	number of folds.
`C`	this is a positive value that accounts for the penalties of violations of constraints. C is typically allowed to take values in a grid such as (0, 0.5, 1, 1.5, ..., 10).
`s`	this is the optimal equivalent specification for lasso in terms of fraction of the L1 norm. This is obtained from the AEnet.aft function

`lambda2`	regularization parameter for the L2 norm of the coefficients. This is typically assumed to take values in a relatively small grid.
`AEnetCC`	If T then the results are based on adaptive elastic net with censoring constraints otherwise based on the weighted elastic net with censoring constraints.

The function gives the K-fold cross validation, cross validation error, cross validation mean squared error.

`beta`	shows coefficient estimates of the covariates.
`betavar`	variance of the coefficient estimates.
`cvscore`	a CV score based on the CV error. This is basically the sum of squared residuals of uncensored data multiplied by the Kaplan-Meier weights (Khan and Shaw, 2015).

Khan and Shaw (2015) imputeYn: Imputing the last largest censored observation/observations under weighted least squares. R package version 1.3, https://cran.r-project.org/package=imputeYn.

Khan and Shaw (2015). Variable Selection for Survival Data with a Class of Adaptive Elastic Net Techniques. Statistics and Computing (published online; DOI: 10.1007/s11222-015-9555-8). Also available in http://arxiv.org/abs/1312.2079.

cv.AWEnet

#For full data typically used for AFT models (using imputeYn (2015) package)
dat<-data(n=100, p=10, r=0, b1=c(rep(5,5),rep(0,5)), sig=1, Cper=0)

#This needs to run for generating weights of the observations
l<-mrbj(cbind(dat$y, dat$delta) ~ dat$x, mcsize=100, trace=FALSE, gehanonly=FALSE)

#cv.AWEnetCC: Cross validation of Adaptive elastic net with censoring constraints
wt<-l$enet
cv1cc<-cv.AWEnetCC(dat$x, dat$y, dat$delta, weight=wt, kFold = 10, C=1.2, s=0.88,
 lambda2=0.001, AEnetCC=TRUE)

#cv.AWEnetCC: Cross validation of weighted elastic net with censoring constraints
## Not run: l<-mrbj(cbind(dat$y, dat$delta) ~ dat$x, mcsize=100, trace=FALSE, gehanonly=TRUE)
## Not run: wt<-l$gehansd
## Not run: cv1cc<-cv.AWEnetCC(dat$x, dat$y, dat$delta, weight=wt, kFold = 10, C=1.2, s=0.88,
 lambda2=0.001, AEnetCC=F)
## End(Not run)