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.
|Author||Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]|
|Date of publication||2016-11-29 12:43:49|
|Maintainer||Daniela Pauger <firstname.lastname@example.org>|
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'