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

## Usage

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).