Calculate the R2 value for different model objects. Depending on the model, R2, pseudo-R2 or marginal / adjusted R2 values are returned.
r2(model, ...) # S3 method for merMod r2(model, tolerance = 1e-05, ...)
model | A statistical model. |
---|---|
... | Arguments passed down to the related r2-methods. |
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
|
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 zero-inflation: R2 for zero-inflated 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