Compute the model's R2Source:
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, ...)
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 (
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
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
If there is no
r2()-method defined for the given model class,
r2() tries to return a "generic" r-quared value, calculated as following:
# Pseudo r-quared for GLM model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial") r2(model) #> # R2 for Logistic Regression #> Tjur's R2: 0.478 # r-squared including confidence intervals model <- lm(mpg ~ wt + hp, data = mtcars) r2(model, ci = 0.95) #> R2: 0.827 [0.654, 0.906] #> adj. R2: 0.815 [0.632, 0.899] model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) r2(model) #> # R2 for Mixed Models #> #> Conditional R2: 0.969 #> Marginal R2: 0.658