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

glmm.hp:一个计算广义线性混合模型中单个预测变量效应的R包

作　　者：Jiangshan Lai Yi Zou Shuang Zhang Xiaoguang Zhang Lingfeng Mao 

机构地区：[1]College of Biology and the Environment,Nanjing Forestry University,Nanjing 210037,China [2]Research Center of Quantitative Ecology,Nanjing Forestry University,Nanjing 210037,China [3]Department of Health and Environmental Sciences,Xi’an JiaotongLiverpool University,Suzhou 215123,China [4]State Key Laboratory of Urban and Regional Ecology,Research Center for EcoEnvironmental Sciences,Chinese Academy of Sciences,Beijing 100085,China [5]State Key Laboratory of Freshwater Ecology and Biotechnology,Institute of Hydrobiology,Chinese Academy of Sciences,Wuhan 430072,China [6]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Journal of Plant Ecology》2022年第6期1302-1307,共6页植物生态学报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(32271551);the Metasequoia funding of Nanjing Forestry University.Conflict of interest statement.The authors declare that they have no conflict of interest.

摘　　要：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^(2)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 R2^(2)s that can be used to evaluate the relative importance of predictors,which sums up to the overall marginal R2^(2).Overall,we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.广义线性混合效应模型(GLMMs)是当代生态学研究中广泛应用的数据分析模型。然而,确定GLMMs中共线性的预测变量(固定效应)对响应变量的相对重要性是个挑战。基于适用于多元分析的‘平均共享方差’的算法,我们开发一个新的R包glmm.hp来分解GLMMs中由固定效应解释的边际(marginal)R2^(2)。我们论述了该软件包的工作原理并通过模拟数据集演示了该软件包的使用。glmm.hp包的输出结果为每个预测变量将获得一个独自的(individual)边际R2^(2),且它们的总和刚好等于模型总的边际R2^(2)。总之,我们相信glmm.hp包将有助于解释GLMMs的输出结果。

关 键 词：coefficient of determination commonality analysis fixed effect GLMM hierarchical partitioning marginal R2^(2) relative importance variance partitioning 

分 类 号：Q14[生物学—生态学]