spikeslab: Prediction and Variable Selection Using Spike and Slab Regression

Authors

• Hemant Ishwaran, Cleveland Clinic
• Udaya B. Kogalur, Cleveland Clinic
• J. Sunil Rao, University of Miami

Date of this Version

12-2010

Document Type

Article

Citation

The R Journal (December 2010) 2(2)

Comments

Copyright 2010, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

Weighted generalized ridge regression offers unique advantages in correlated high dimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we de scribe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.