`check_zeroinflation()`

checks whether count models are
over- or underfitting zeros in the outcome.

`check_zeroinflation(x, tolerance = 0.05)`

## Arguments

x |
Fitted model of class `merMod` , `glmmTMB` , `glm` ,
or `glm.nb` (package MASS). |

tolerance |
The tolerance for the ratio of observed and predicted
zeros to considered as over- or underfitting zeros. A ratio
between 1 +/- `tolerance` is considered as OK, while a ratio
beyond or below this threshold would indicate over- or underfitting. |

## Value

A list with information about the amount of predicted and observed
zeros in the outcome, as well as the ratio between these two values.

## Details

If the amount of observed zeros is larger than the amount of
predicted zeros, the model is underfitting zeros, which indicates a
zero-inflation in the data. In such cases, it is recommended to use
negative binomial or zero-inflated models.

## Examples

```
if (require("glmmTMB")) {
data(Salamanders)
m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
check_zeroinflation(m)
}
#> # Check for zero-inflation
#>
#> Observed zeros: 387
#> Predicted zeros: 298
#> Ratio: 0.77
#>
#> Model is underfitting zeros (probable zero-inflation).
```