Bayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel

In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.

Gonzalo Garcia-Donato , Anabel Forte
2018-05-21

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-021.zip

CRAN packages used

BayesVarSel, faraway, BayesFactor, BMS, mombf, BAS, BMA

CRAN Task Views implied by cited packages

Bayesian, Econometrics, SocialSciences, Survival

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Garcia-Donato & Forte, "Bayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel", The R Journal, 2018

BibTeX citation

@article{RJ-2018-021,
  author = {Garcia-Donato, Gonzalo and Forte, Anabel},
  title = {Bayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel},
  journal = {The R Journal},
  year = {2018},
  note = {https://doi.org/10.32614/RJ-2018-021},
  doi = {10.32614/RJ-2018-021},
  volume = {10},
  issue = {1},
  issn = {2073-4859},
  pages = {155-174}
}