Enet.wls: Elastic net based on the weighted data

In AdapEnetClass: A Class of Adaptive Elastic Net Methods for Censored Data

Description

This function provides the estimates of the initial estimators.

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.

Details

This function provides the estimates of the initial estimators that are used to calculate weights for observations under adaptive and weighted elastic net approaches. Here simple elastic net method is applied to the data weighted by the Kaplan-Meier weights (Khan and Shaw, 2015). The elastic net estimates are obtained using glmnet.

Value

beta	coefficient estimates of the covariates
fit	an object that gives df (The number of nonzero coefficients for each value of lambda), Dev (deviance) abd Lambda (The actual sequence of lambda values used)

Author(s)

Hasinur Rahaman Khan and Ewart Shaw

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.

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)

#Enet.wls: Elastic net on the weighted data
enet<-Enet.wls(dat$x, dat$y, dat$delta)
enet

AdapEnetClass documentation built on May 2, 2019, 7:55 a.m.