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.
摘要：
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
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。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，且它们的总和刚好等于模型总的边际R2。总之，我们相信glmm.hp包将有助于解释GLMMs 的输出结果。
关键词:
决定系数,
共性分析,
固定效应,
广义线性混合效应模型,
层次分割,
边际R2,
相对重要性,
方差分解
