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)) 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.

Getting started

Package details

AuthorDaniela Pauger [aut], Magdalena Leitner [aut, cre], Helga Wagner [aut] (<https://orcid.org/0000-0002-7003-9512>), Gertraud Malsiner-Walli [aut] (<https://orcid.org/0000-0002-1213-4749>), Nicholas G. Polson [ctb], James G. Scott [ctb], Jesse Windle [ctb], Bettina Grün [ctb] (<https://orcid.org/0000-0001-7265-4773>)
MaintainerMagdalena Leitner <[email protected]>
LicenseGPL-3
Version1.1.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("effectFusion")

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effectFusion documentation built on Jan. 20, 2019, 1:04 a.m.