“glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models” (2022), by Jiangshan Lai et al. Mathematics research paper. Original abstract below, unchanged. No free PDF was found.

glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models

Jiangshan Lai, Yi Zou, Shuang Zhang et al. · November 15, 2022 · Mathematics

Abstract

Generalized linear mixed models (GLMMs) have been widely used in contemporary ecology studies. However, determination of the relative importance of collinear predictors (i.e. fixed effects) to response variables is one of the challenges in GLMMs. Here, we developed a novel R package, glmm.hp, to decompose marginal R2 explained by fixed effects in GLMMs. The algorithm of glmm.hp is based on the recently proposed approach ‘average shared variance’ i.e. used for multivariate analysis. We explained the principle and demonstrated the use of this package by simulated dataset. The output of glmm.hp shows individual marginal R2s that can be used to evaluate the relative importance of predictors, which sums up to the overall marginal R2. Overall, we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.

Source page DOI: 10.1093/jpe/rtac096