Calculate the R2, also known as the coefficient of determination, value for different model objects. Depending on the model, R2, pseudo-R2, or marginal / adjusted R2 values are returned.
r2(model, ...)# S3 method for default
r2(model, ci = NULL, verbose = TRUE, ...)
# S3 method for merMod
r2(model, ci = NULL, tolerance = 1e-05, ...)
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 zero-inflation: R2 for zero-inflated models
Mixed models: Nakagawa's R2
Bayesian models: R2 bayes
A statistical model.
Arguments passed down to the related r2-methods.
Confidence interval level, as scalar. If NULL (default), no
confidence intervals for R2 are calculated.
Logical. Should details about R2 and CI methods be given
(TRUE) or not (FALSE)?
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().
r2_bayes(), r2_coxsnell(), r2_kullback(),
r2_loo(), r2_mcfadden(), r2_nagelkerke(),
r2_nakagawa(), r2_tjur(), r2_xu() and
r2_zeroinflated().
# Pseudo r-quared for GLM
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
r2(model)
# r-squared including confidence intervals
model <- lm(mpg ~ wt + hp, data = mtcars)
r2(model, ci = 0.95)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r2(model)
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