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glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models

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  • Vincent Calcagno
  • Claire de Mazancourt
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    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.

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    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 34 ()
    Issue (Month): i12 ()
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    Handle: RePEc:jss:jstsof:34:i12

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    Web page: http://www.jstatsoft.org/

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      by stevepalley in Steve Palley on 2012-08-20 05:21:04
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    Cited by:
    1. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.

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