r2  R Documentation 
Calculate the R2, also known as the coefficient of determination, value for different model objects. Depending on the model, R2, pseudoR2, or marginal / adjusted R2 values are returned.
r2(model, ...) ## Default S3 method: r2(model, ci = NULL, verbose = TRUE, ...) ## S3 method for class 'merMod' r2(model, ci = NULL, tolerance = 1e05, ...)
model 
A statistical model. 
... 
Arguments passed down to the related r2methods. 
ci 
Confidence interval level, as scalar. If 
verbose 
Logical. Should details about R2 and CI methods be given
( 
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 (or NULL
if no R2 could be extracted). 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
If there is no r2()
method defined for the given model class,
r2()
tries to return a "generic" rquared value, calculated as following:
1sum((yy_hat)^2)/sum((yy_bar)^2))
r2_bayes()
, r2_coxsnell()
, r2_kullback()
,
r2_loo()
, r2_mcfadden()
, r2_nagelkerke()
,
r2_nakagawa()
, r2_tjur()
, r2_xu()
and
r2_zeroinflated()
.
# Pseudo rquared for GLM model < glm(vs ~ wt + mpg, data = mtcars, family = "binomial") r2(model) # rsquared including confidence intervals model < lm(mpg ~ wt + hp, data = mtcars) r2(model, ci = 0.95) if (require("lme4")) { model < lmer(Sepal.Length ~ Petal.Length + (1  Species), data = iris) r2(model) }
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