We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. We also discuss some special considerations for binomial GLMMs with binary or proportion data. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data.
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Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. And if you do decide to try Pot Of Gold cannabis, be sure to take it slow at first and see how you react before taking too much.The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest.
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