<b>glmulti</b>: An<i>R</i>Package for Easy Automated Model Selection with (Generalized) Linear Models 论文

摘要

We introduce <b>glmulti</b>, an <b>R</b> package for automated model selection and multi-model inference with <code>glm</code> and related functions. From a list of explanatory variables, the provided function <code>glmulti</code> 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 <b>R</b> functions like <code>glm</code>. The <i>n</i> best models and their support (e.g., (Q)AIC, (Q)AICc, or BIC) are returned, allowing model selection and multi-model inference through standard <b>R</b> 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.