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)
A list with the conditional and marginal R2 values.
A mixed effects model.
Logical, if TRUE
, returns the explained variance
at different levels (if there are multiple levels). This is essentially
similar to the variance reduction approach by Hox (2010), pp. 69-78.
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
r2_nakagawa()
returns a warning, stating that random effect variances
can't be computed (and thus, the conditional r-squared is NA
),
decrease the tolerance-level. See also check_singularity()
.
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).
Hox, J. J. (2010). Multilevel analysis: techniques and applications (2nd ed). New York: Routledge.
Johnson, P. C. D. (2014). Extension of Nakagawa and Schielzeth’s R2 GLMM to random slopes models. Methods in Ecology and Evolution, 5(9), 944–946. tools:::Rd_expr_doi("10.1111/2041-210X.12225")
Nakagawa, S., and 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. tools:::Rd_expr_doi("10.1111/j.2041-210x.2012.00261.x")
Nakagawa, S., Johnson, P. C. D., and 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. tools:::Rd_expr_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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