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

Jiangshan Lai, Yi Zou, Shuang Zhang, Xiaoguang Zhang, Lingfeng Mao

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)

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.
Original languageChinese (Simplified)
Pages (from-to)1302-1307
Number of pages6
JournalJournal of Plant Ecology
Volume15
Issue number6
DOIs
Publication statusPublished - 2022

Cite this