Compute indices of model performance for mixed models.

## Usage

```
# S3 method for merMod
model_performance(
model,
metrics = "all",
estimator = "REML",
verbose = TRUE,
...
)
```

## Arguments

- model
A mixed effects model.

- metrics
Can be

`"all"`

,`"common"`

or a character vector of metrics to be computed (some of`c("AIC", "AICc", "BIC", "R2", "ICC", "RMSE", "SIGMA", "LOGLOSS", "SCORE")`

).`"common"`

will compute AIC, BIC, R2, ICC and RMSE.- estimator
Only for linear models. Corresponds to the different estimators for the standard deviation of the errors. If

`estimator = "ML"`

(default), the scaling is done by n (the biased ML estimator), which is then equivalent to using`AIC(logLik())`

. Setting it to`"REML"`

will give the same results as`AIC(logLik(..., REML = TRUE))`

.- verbose
Toggle warnings and messages.

- ...
Arguments passed to or from other methods.

## Details

### Intraclass Correlation Coefficient (ICC)

This method returns the *adjusted ICC* only, as this is typically of
interest when judging the variance attributed to the random effects part of
the model (see also `icc()`

).

### REML versus ML estimator

The default behaviour of `model_performance()`

when computing AIC or BIC of
linear mixed model from package **lme4** is the same as for `AIC()`

or
`BIC()`

(i.e. `estimator = "REML"`

). However, for model comparison using
`compare_performance()`

sets `estimator = "ML"`

by default, because
*comparing* information criteria based on REML fits is usually not valid
(unless all models have the same fixed effects). Thus, make sure to set
the correct estimator-value when looking at fit-indices or comparing model
fits.

### Other performance indices

Furthermore, see 'Details' in `model_performance.lm()`

for more details
on returned indices.

## Examples

```
model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
model_performance(model)
#> # Indices of model performance
#>
#> AIC | AICc | BIC | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma
#> --------------------------------------------------------------------------
#> 77.320 | 77.595 | 89.362 | 0.972 | 0.096 | 0.969 | 0.279 | 0.283
```