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.
|Depends:||R (≥ 3.3.1)|
|Imports:||Matrix, MASS, bayesm, cluster, ggplot2, utils, stats|
|Author:||Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]|
|Maintainer:||Daniela Pauger <daniela.pauger at jku.at>|
|CRAN checks:||effectFusion results|
|Windows binaries:||r-devel: effectFusion_1.0.zip, r-release: effectFusion_1.0.zip, r-oldrel: effectFusion_1.0.zip|
|OS X El Capitan binaries:||r-release: effectFusion_1.0.tgz|
|OS X Mavericks binaries:||r-oldrel: effectFusion_1.0.tgz|
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