effectFusion: Bayesian Effect Fusion for Categorical Predictors

Variable selection and Bayesian effect fusion for categorical predictors in linear regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.

AuthorDaniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]
Date of publication2016-11-29 12:43:49
MaintainerDaniela Pauger <daniela.pauger@jku.at>
LicenseGPL-3
Version1.0

View on CRAN

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.