r2 function

Compute the model's R2

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, ...)

## Default S3 method:
r2(model, ci = NULL, verbose = TRUE, ...)

## S3 method for class 'mlm'
r2(model, multivariate = TRUE, ...)

## S3 method for class 'merMod'
r2(model, ci = NULL, tolerance = 1e-05, ...)

Arguments

• model: A statistical model.
• ...: Arguments passed down to the related r2-methods.
• ci: Confidence interval level, as scalar. If NULL (default), no confidence intervals for R2 are calculated.
• verbose: Logical. Should details about R2 and CI methods be given (TRUE) or not (FALSE)?
• multivariate: Logical. Should multiple R2 values be reported as separated by response (FALSE) or should a single R2 be reported as combined across responses computed by r2_mlm (TRUE).
• 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().

Returns

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

Note

If there is no r2()-method defined for the given model class, r2() tries to return a "generic" r-quared value, calculated as following: 1-sum((y-y_hat)^2)/sum((y-y_bar)^2)

Examples

# 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)

See Also

r2_bayes(), r2_coxsnell(), r2_kullback(), r2_loo(), r2_mcfadden(), r2_nagelkerke(), r2_nakagawa(), r2_tjur(), r2_xu(), r2_zeroinflated(), and r2_mlm().