AdapEnetClass (version 1.2)

cv.AWEnetCC: Computes K-fold cross validated error curve for AEnetCC and WEnetCC

Description

This function computes the K-fold cross validation estimates.

Usage

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

Arguments

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.

Value

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).

Details

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

References

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.

See Also

cv.AWEnet

Examples

#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)