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Compute the marginal and conditional r-squared value for mixed effects models with complex random effects structures.

Usage

r2_nakagawa(
  model,
  by_group = FALSE,
  tolerance = 1e-05,
  ci = NULL,
  iterations = 100,
  ...
)

Arguments

model

A mixed effects model.

by_group

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

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().

ci

Confidence resp. credible interval level. For icc() and r2(), confidence intervals are based on bootstrapped samples from the ICC resp. R2 value. See iterations.

iterations

Number of bootstrap-replicates when computing confidence intervals for the ICC or R2.

...

Arguments passed down to brms::posterior_predict().

Value

A list with the conditional and marginal R2 values.

Details

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 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).

  • Conditional R2: takes both the fixed and random effects into account.

  • Marginal R2: considers only the variance of the fixed effects.

The contribution of random effects can be deduced by subtracting the marginal R2 from the conditional R2 or by computing the icc().

References

  • 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. 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. 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. doi:10.1098/rsif.2017.0213

Examples

if (require("lme4")) {
  model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
  r2_nakagawa(model)
  r2_nakagawa(model, by_group = TRUE)
}
#> # Explained Variance by Level
#> 
#> Level   |     R2
#> ----------------
#> Level 1 |  0.569
#> Species | -0.853
#>