effectFusion: Bayesian Effect Fusion for Categorical Predictors
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.
| Version: |
1.1.3 |
| Depends: |
R (≥ 3.3), mcclust |
| Imports: |
Matrix, MASS, bayesm, cluster, GreedyEPL, gridExtra, ggplot2, methods, utils, stats |
| Published: |
2021-10-13 |
| Author: |
Daniela Pauger [aut],
Magdalena Leitner [aut, cre],
Helga Wagner 
[aut],
Gertraud Malsiner-Walli
[aut],
Nicholas G. Polson [ctb],
James G. Scott [ctb],
Jesse Windle [ctb],
Bettina Grün
[ctb] |
| Maintainer: |
Magdalena Leitner <effectfusion.jku at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
effectFusion results |
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