r2 {performance} | R Documentation |
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 'merMod' r2(model, ci = NULL, tolerance = 1e-05, ...)
model |
A statistical model. |
... |
Arguments passed down to the related r2-methods. |
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 r-squared
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 zero-inflation: R2 for zero-inflated 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" r-quared value, calculated as following:
1-sum((y-y_hat)^2)/sum((y-y_bar)^2))
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) if (require("lme4")) { model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) r2(model) }