Calculate the R2 value for different model objects. Depending on the model, R2, pseudoR2 or marginal / adjusted R2 values are returned.
r2(model, ...) # S3 method for merMod r2(model, tolerance = 1e05, ...)
model  A statistical model. 

...  Arguments passed down to the related r2methods. 
tolerance  Tolerance for singularity check of random effects, to decide
whether to compute random effect variances for the conditional rsquared
or not. Indicates up to which value the convergence result is accepted. When

Returns a list containing values related to the most appropriate R2 for the given model. See the list below:
Logistic models: Tjur's R2
General linear models: Nagelkerke's R2
Multinomial Logit: McFadden's R2
Models with zeroinflation: R2 for zeroinflated models
Mixed models: Nakagawa's R2
Bayesian models: R2 bayes
r2_bayes
, r2_coxsnell
, r2_kullback
,
r2_loo
, r2_mcfadden
, r2_nagelkerke
,
r2_nakagawa
, r2_tjur
, r2_xu
and
r2_zeroinflated
.
#> $R2_Tjur #> Tjur's R2 #> 0.4776926 #>if (require("lme4")) { model < lmer(Sepal.Length ~ Petal.Length + (1  Species), data = iris) r2(model) }#> # R2 for Mixed Models #> #> Conditional R2: 0.969 #> Marginal R2: 0.658