Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Vincent Calcagno, Claire de Mazancourt
Title: glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models
Abstract: We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and, optionally, their pairwise interactions. Restrictions can be specified for candidate models, by excluding specific terms, enforcing marginality, or controlling model complexity. Models are fitted with standard R functions like glm. The n best models and their support (e.g., (Q)AIC, (Q)AICc, or BIC) are returned, allowing model selection and multi-model inference through standard R functions. The package is optimized for large candidate sets by avoiding memory limitation, facilitating parallelization and providing, in addition to exhaustive screening, a compiled genetic algorithm method. This article briefly presents the statistical framework and introduces the package, with applications to simulated and real data.

Page views:: 63447. Submitted: 2009-06-19. Published: 2010-05-31.
Paper: glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models     Download PDF (Downloads: 119905)
glmulti_0.6-2.tar.gz: R source package Download (Downloads: 1049; 57KB) Java classes for glmulti Download (Downloads: 877; 15KB)
v34i12.R: R example code from the paper Download (Downloads: 1601; 14KB)

DOI: 10.18637/jss.v034.i12

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.