Compute mean square error of linear models.

## Arguments

- model
A model.

- ...
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 mean square error is the mean of the sum of squared residuals, i.e. it measures the average of the squares of the errors. Less technically speaking, the mean square error can be considered as the variance of the residuals, i.e. the variation in the outcome the model doesn't explain. Lower values (closer to zero) indicate better fit.