Compute the model's R2

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, ...)

Arguments

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 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.

Value

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

See also

r2_bayes, r2_coxsnell, r2_kullback, r2_loo, r2_mcfadden, r2_nagelkerke, r2_nakagawa, r2_tjur, r2_xu and r2_zeroinflated.

Examples

model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
r2(model)
#> $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