model

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

<p>The function displays for categorical covariates the selected model of an
object of class <code>fusion</code> as list.</p>

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

Helga Wagner

Gertraud Malsiner-Walli

Nicholas G. Polson

James G. Scott

Jesse Windle

Bettina Gr<c3><bc>n

model function

Selected model of a <code>fusion</code> object — model

Selected model of a <code>fusion</code> object

model: Selected model of a `fusion` object

Description

Usage

Arguments

Details

See Also

Examples