Extension of Nakagawa & Schielzeth's R(2)(GLMM) to random slopes models.
Extension of Nakagawa & Schielzeth's R(2)(GLMM) to random slopes models.
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DOI：
10.1111/2041-210x.12225
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发表时间：
2014-09
影响因子：
6.6
通讯作者：
Johnson PC
中科院分区：
文献类型：
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作者：
Johnson PC
Nakagawa & Schielzeth将广泛使用的拟合优度统计量R2扩展到广义线性混合模型（GLP-generalized linear mixed models）。然而，他们的R2 GLMM方法仅限于具有最简单随机效应结构的模型，称为随机截距模型。它不适用于另一种常见的随机效应结构，随机斜率模型。我表明，R2 GLMM可以扩展到随机斜率模型，使用一个简单的公式，是直接在统计软件中实现。这种扩展大大拓宽了R2 GLMM的潜在应用。
Nakagawa & Schielzeth extended the widely used goodness-of-fit statistic R2 to apply to generalized linear mixed models (GLMMs). However, their R2GLMM method is restricted to models with the simplest random effects structure, known as random intercepts models. It is not applicable to another common random effects structure, random slopes models. I show that R2GLMM can be extended to random slopes models using a simple formula that is straightforward to implement in statistical software. This extension substantially widens the potential application of R2GLMM.