Compute root mean squared error for (mixed effects) models, including Bayesian regression models.

## Usage

```
performance_rmse(
model,
normalized = FALSE,
ci = NULL,
iterations = 100,
ci_method = NULL,
verbose = TRUE,
...
)
rmse(
model,
normalized = FALSE,
ci = NULL,
iterations = 100,
ci_method = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
A model.

- normalized
Logical, use

`TRUE`

if normalized rmse should be returned.- ci
Confidence resp. credible interval level. For

`icc()`

,`r2()`

, and`rmse()`

, confidence intervals are based on bootstrapped samples from the ICC, R2 or RMSE value. See`iterations`

.- iterations
Number of bootstrap-replicates when computing confidence intervals for the ICC, R2, RMSE etc.

- ci_method
Character string, indicating the bootstrap-method. Should be

`NULL`

(default), in which case`lme4::bootMer()`

is used for bootstrapped confidence intervals. However, if bootstrapped intervals cannot be calculated this way, try`ci_method = "boot"`

, which falls back to`boot::boot()`

. This may successfully return bootstrapped confidence intervals, but bootstrapped samples may not be appropriate for the multilevel structure of the model. There is also an option`ci_method = "analytical"`

, which tries to calculate analytical confidence assuming a chi-squared distribution. However, these intervals are rather inaccurate and often too narrow. It is recommended to calculate bootstrapped confidence intervals for mixed models.- verbose
Toggle warnings and messages.

- ...
Arguments passed down to

`lme4::bootMer()`

or`boot::boot()`

for bootstrapped ICC, R2, RMSE etc.; for`variance_decomposition()`

, arguments are passed down to`brms::posterior_predict()`

.

## Details

The RMSE is the square root of the variance of the residuals and indicates the absolute fit of the model to the data (difference between observed data to model's predicted values). It can be interpreted as the standard deviation of the unexplained variance, and is in the same units as the response variable. Lower values indicate better model fit.

The normalized RMSE is the proportion of the RMSE related to the range of the response variable. Hence, lower values indicate less residual variance.