Nakagawa's R2 for mixed models
Compute the marginal and conditional r-squared value for mixed effects models with complex random effects structures.
r2_nakagawa(model, by_group = FALSE, tolerance = 1e-05)
model |
A mixed effects model. |
by_group |
Logical, if |
tolerance |
Tolerance for singularity check of random effects, to decide
whether to compute random effect variances for the conditional r-squared
or not. Indicates up to which value the convergence result is accepted. When
|
Marginal and conditional r-squared values for mixed models are calculated
based on Nakagawa et al. 2017. For more details on the computation
of the variances, see ?insight::get_variance.
The marginal r-squared considers only the variance of the fixed effects, while the
conditional r-squared takes both the fixed and random effects into account.
The random effect variances are actually the mean random effect variances,
thus the r-squared value is also appropriate for mixed models with random
slopes or nested random effects (see Johnson 2014).
A list with the conditional and marginal R2 values.
Hox, J. J. (2010). Multilevel analysis: techniques and applications (2nd ed). New York: Routledge.
Johnson, P. C. D. (2014). Extension of Nakagawa & Schielzeth’s R2 GLMM to random slopes models. Methods in Ecology and Evolution, 5(9), 944–946. doi: 10.1111/2041-210X.12225
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. doi: 10.1111/j.2041-210x.2012.00261.x
Nakagawa, S., Johnson, P. C. D., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of The Royal Society Interface, 14(134), 20170213. doi: 10.1098/rsif.2017.0213
if (require("lme4")) {
model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r2_nakagawa(model)
r2_nakagawa(model, by_group = TRUE)
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