Extension of the<i>glmm.hp</i>package to zero-inflated generalized linear mixed models and multiple regression
Jiangshan Lai, Weijie Zhu, Dongfang Cui, Lingfeng Mao
Abstract
Abstract glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has been rapidly gained recognition and popularity among ecologists. However, the previous glmm.hp package was limited to work GLMMs derived exclusively from the lme4 and nlme packages. The latest glmm.hp package has extended its functions. It has integrated results obtained from the glmmTMB package, thus enabling it to handle zero-inflated generalized linear mixed models (ZIGLMMs) effectively. Furthermore, it has introduced the new functionalities of commonality analysis and hierarchical partitioning for multiple linear regression models by considering both unadjusted R2 and adjusted R2. This paper will serve as a demonstration for the applications of these new functionalities, making them more accessible to users.