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, ...)
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, seeci_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 isNA
), decrease the tolerance-level. See alsocheck_singularity()
.
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
Note
If there is no r2()
-method defined for the given model class,
r2()
tries to return a "generic r2 value, calculated as following:
1-sum((y-y_hat)^2)/sum((y-y_bar)^2))
Examples
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
r2(model)
#> # R2 for Logistic Regression
#> Tjur's R2: 0.478
if (require("lme4")) {
model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r2(model)
}
#> # R2 for Mixed Models
#>
#> Conditional R2: 0.969
#> Marginal R2: 0.658