`check_overdispersion.Rd`

`check_overdispersion()`

checks generalized linear (mixed) models
for overdispersion.

check_overdispersion(x, ...)

x | Fitted model of class |
---|---|

... | Currently not used. |

A list with results from the overdispersion test, like chi-squared statistics, p-value or dispersion ratio.

If the dispersion ratio is close to one, a poisson model fits well to the data. Dispersion ratios larger than one indicate overdispersion, thus a negative binomial model or similar might fit better to the data. A p-value < .05 indicates overdispersion.

For Poisson models, the overdispersion test is based on the code from Gelman and Hill (2007), page 115.

For `merMod`

- and `glmmTMB`

-objects, `check_overdispersion()`

is based on the code in the GLMM FAQ,
section *How can I deal with overdispersion in GLMMs?*. Note that
this function only returns an *approximate* estimate of an
overdispersion parameter, and is probably inaccurate for zero-inflated
mixed models (fitted with `glmmTMB`

).

Overdispersion can be fixed by either modelling the dispersion parameter, or by choosing a different distributional family (like Quasi-Poisson, or negative binomial, see Gelman and Hill (2007), pages 115-116).

Bolker B et al. (2017): GLMM FAQ.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.

library(glmmTMB) data(Salamanders) m <- glm(count ~ spp + mined, family = poisson, data = Salamanders) check_overdispersion(m)#> # Overdispersion test #> #> dispersion ratio = 2.946 #> Pearson's Chi-Squared = 1873.710 #> p-value = < 0.001 #>#>m <- glmmTMB( count ~ mined + spp + (1 | site), family = poisson, data = Salamanders ) check_overdispersion(m)#> # Overdispersion test #> #> dispersion ratio = 2.324 #> Pearson's Chi-Squared = 1475.875 #> p-value = < 0.001 #>#>