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

r2_nakagawa(model, by_group = FALSE)

## 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. |

## 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 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).

## References

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

## Examples

#> # Explained Variance by Level
#>
#> Level | R2
#> ----------------
#> Level 1 | 0.569
#> Species | -0.853
#>