Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models

By Paul C. Johnson. Methods in Ecology and Evolution, 2014

913 citations recorded by the source corpus.

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.

Subfields: Ecology, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1316670ab26067e99050d79b0b68b0786e81b911

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Nature and Landscape Conservation

In Statistics and Probability

Citations in the corpus

rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models (2017, methodology): “(iv) The package allows repeatability and marginal R2 estimation from random-slope models, using the approach introduced by Johnson (2014).”
piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics (2015, methodology): “…sem.model.fits , for example, generates R 2 and pseudo-R 2 and AIC values for component models based on methods in (Nakagawa & Schielzeth 2012; Johnson 2014). predict.sem is a wrapper for the generic predict function and additionally implements standard errors on predictions from models…”
Quantifying variable importance in a multimodel inference framework (2016, background): “…variety of statistical models including fixed-effects and mixed-effects linear models with different error structures (Nakagawa & Schielzeth 2013; Johnson 2014), Iweighted and Iglobal can be applied to many common ecological analyses such as determining drivers of phylogenetic and species…”
An R2 statistic for fixed effects in the generalized linear mixed model (2017, background): “R2NSJ, an R 2 statistic for marginal and conditional explained variance in the GLMM Nakagawa and Schielzeth’s marginal and conditional R2 statistics (with the extension proposed by Johnson [20]) for the LMM are written as R2NSJ(m) = σ̂ 2f σ̂ 2f + σ̂ 2ε + σ̄ 2l and R2NSJ(c) = σ̂ 2f + σ̄ 2l σ̂ 2f + σ̂”