Extension of the glmm.hp package to zero-inflated generalized linear mixed models and multiple regression

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 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 however, brings new improvements. It has integrated results obtained from the glmmTMB package, enabling it to handle Zero-Inflated Generalized Linear Mixed Models effectively. Furthermore, it has introduced the new functionalities of commonality analysis and hierarchical partitioning for multiple linear regression models, considering both unadjusted R2 and adjusted R2. This paper will serve as a demonstration of these new functionalities, making them more accessible to users.

Correction to “Variations in root architecture traits and their association with organ mass fraction of common annual ephemeral species in the desert of northern Xinjiang”

“…In paragraph 3 of the “Materials and Methods” section, the text “The glmm.hp() function in the glmm.hp package was used for variance decomposition, and the proportion between variance components represents the proportional contribution of each scale change.” was incorrect. This should have read as follows: “The glmm.hp() function in the glmm.hp package was used for variance decomposition, and the proportion between variance components represents the proportional contribution of each scale change (Lai et al., 2022, 2023). ”…”