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.

Install the latest version of this package by entering the following in R:

`install.packages("effectFusion")`

Author | Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut] |

Date of publication | 2016-11-29 12:43:49 |

Maintainer | Daniela Pauger <daniela.pauger@jku.at> |

License | GPL-3 |

Version | 1.0 |

**dic:** DIC

**effectFusion:** Bayesian effect fusion for categorical predictors

**model:** Selected model of a 'fusion' object

**plot.fusion:** Plot an object of class 'fusion'

**print.fusion:** Print object of class 'fusion'

**sim1:** Simulated data set 1

**sim2:** Simulated data set 2

**summary.fusion:** Summary of object of class 'fusion'

NAMESPACE

data

data/sim1.RData

data/sim2.RData

R

R/model_refit.R
R/model.R
R/sim1.R
R/create_modelvars.R
R/get_S_ordinal.R
R/plot.R
R/get_fusion.R
R/create_model.R
R/dic.R
R/create_selmat.R
R/getA_diag.R
R/get_gamma.R
R/sampler_ss.R
R/create_prior_par.R
R/hpd_mcmc.R
R/create_row_names.R
R/fusion_vector.R
R/corr_rest.R
R/print.R
R/coding.R
R/select_model.R
R/mcmc_linreg.R
R/get_reparm_mats.R
R/hpd_ggplot.R
R/incl_prob.R
R/effect_fusion.R
R/summary.R
R/sampler_mix.R
R/sim2.R
R/get_S_nominal.R
R/getA.R
MD5

DESCRIPTION

man

man/sim2.Rd
man/plot.fusion.Rd
man/model.Rd
man/effectFusion.Rd
man/summary.fusion.Rd
man/print.fusion.Rd
man/dic.Rd
man/sim1.Rd
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