Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes the K-fold cross validation estimates.
1 2 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | #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)]
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