plot.fusion

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

maximum number of plots on a single page, default argument to 4

maxPlots

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

This function provides plots of posterior means and 95%-HPD intervals for the
regression effects. Plots are based on the refitted MCMC samples of the
selected model in an object of class <code>fusion</code> or on model averaged results if no
final model selection was performed.

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

plot.fusion: Plot an object of class `fusion`

Description

Usage

Arguments

Details

See Also

Examples