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

Description Usage Arguments Details Value Author(s) References See Also Examples

This function computes the K-fold cross validation estimates.

1 2	cv.AWEnet(X, Y, delta, weight, lambda2, maxit, K = 10, fraction = seq(from = 0, to = 1, length = 100), plot.it = F, se = TRUE, AEnet = T, all.folds = NULL)

`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).
`lambda2`	regularization parameter for the L2 norm of the coefficients. This is typically assumed to take values in a relatively small grid.
`maxit`	an optional bound for the number of steps to be taken. Default is 10.
`K`	number of folds.
`fraction`	abscissa values at which CV curve should be computed. This is the fraction of the saturated \|beta\|. The default value is seq(from = 0, to = 1, length =100).
`plot.it`	if T then plot will be showed. Default is T.
`se`	include standard error bands.
`AEnet`	if T then the results are based on adaptive elastic net otherwise based on weighted elastic net.
`all.folds`	null.

This function computes the K-fold cross validation, cross validation error, cross validation mean squared error.

An "index" object is returned with a CV curve. The index includes

`lambda2`	as AEnetCC.aft
`cv`	the CV curve at each value of index
`cv.mse`	the mean square error of the CV curve
`cv.error`	the standard error of the CV curve

Hasinur Rahaman Khan and Ewart Shaw

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.AWEnetCC

#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.AWEnet: Cross validation of Adaptive elastic net
wt<-l$enet
## Not run: cv1 <-cv.AWEnet(dat$x, dat$y, dat$delta, weight=wt, lambda2=0.001, maxit=10,
plot.it = T, AEnet=T)
## End(Not run)
## Not run: cv1$index[which.min(cv1$cv)]

#cv.AWEnet: Cross validation of weighted elastic net
## Not run: l<-mrbj(cbind(dat$y, dat$delta) ~ dat$x, mcsize=100, trace=FALSE, gehanonly=TRUE)
## Not run: wt<-l$gehansd
## Not run: cv2 <-cv.AWEnet(dat$x, dat$y, dat$delta, weight=wt, lambda2=0.001, 
maxit=10, plot.it = T, AEnet=F)
## End(Not run)
## Not run: cv2$index[which.min(cv2$cv)]