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.
Usage
r2(model, ...)
# S3 method for default
r2(model, ci = NULL, ci_method = "analytical", verbose = TRUE, ...)
# S3 method for merMod
r2(model, tolerance = 1e-05, ...)
Value
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
Arguments
model
A statistical model.
...
Arguments passed down to the related r2-methods.
ci
Confidence Interval (CI) level. Default is NULL. Confidence
intervals for R2 can be calculated based on different methods, see
ci_method.
ci_method
Method for constructing the R2 confidence interval.
Options are "analytical" for sampling-theory-based frequentist
intervals and "bootstrap" for bootstrap intervals. Analytical intervals
are not available for all models. For Bayesian models, r2_bayes() is used.
verbose
Logical. Should details about R2 and CI methods be given (TRUE) or not (FALSE)?
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().