Random Approximate Elastic Net (RAEN) Variable Selection Method

The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.


Reference manual

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0.2 by Han Sun, a year ago


Browse source code at https://github.com/cran/RAEN

Authors: Han Sun and Xiaofeng Wang

Documentation:   PDF Manual  

GPL (>= 2) license

Imports boot, foreach, doParallel, glmnet, fastcmprsk

Depends on lars

Suggests testthat, knitr, rmarkdown

See at CRAN