Calculates the logarithmic, quadratic/Brier and spherical based on a fitted mixed model
for categorical data.

```
scoring_rules(object, newdata, newdata2 = NULL, max_count = 2000,
return_newdata = FALSE)
```

## Arguments

- object
an object inheriting from class `"MixMod"`

.

- newdata
a data.frame based on which to estimate the random effect and calculate
predictions. It should contain the response variable.

- newdata2
a data.frame based on which to estimate the random effect and calculate
predictions. It should contain the response variable.

- max_count
numeric scalar denoting the maximum count up to which to calculate
probabilities; this is relevant for count response data.

- return_newdata
logical; if `TRUE`

the values of the scoring rules are
ruturned as extra columns of the `newdata`

or `newdata2`

data.frame.

## Value

A data.frame with (extra) columns the values of the logarithmic, quadratic and spherical
scoring rules calculated based on the fitted model and the observed responses in
`newdata`

or `newdata2`

.

## References

Carvalho, A. (2016). An overview of applications of proper scoring rules.
*Decision Analysis* **13**, 223--242. doi:10.1287/deca.2016.0337

## Examples

```
# \donttest{
NA
#> [1] NA
NA
#> [1] NA
NA
#> [1] NA
# }
```