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, verbose = TRUE, ...)
# S3 method for 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`

)?- 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()`

.

## 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" 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)
#> # 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
```