summary.fusion

an object of class <code>fusion</code>

object

further arguments passed to or from other methods (not used)

Returns basic information about the model and the priors, MCMC details and posterior
means from the refit of the selected model or model averaged results as well as 95%-HPD intervals for the regression effects.

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) <doi:10.1080/01621459.2013.829001>) 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)
<doi:10.1080/01621459.2013.829001>, who own the copyright.

Magdalena Leitner

effectFusion

Bayesian Effect Fusion for Categorical Predictors

Daniela Pauger

summary.fusion: Summary of object of class `fusion`

Description

Usage

Arguments

Details

See Also

Examples