effectFusion (version 1.1.3)
Bayesian Effect Fusion for Categorical Predictors
Description
Variable selection and Bayesian effect fusion for categorical predictors in linear and
logistic 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 by taking model averages. Posterior inference is accomplished by an MCMC
sampling scheme which makes use of a data augmentation strategy (Polson, Scott & Windle
(2013) ) based on latent Polya-Gamma random variables
in the case of logistic regression. The code for data augmentation is taken from Polson et al. (2013)
, who own the copyright.