How to test for overdispersion in binomial GLMs?
You can test specifically for overdispersion in binomial GLMs with the DHARMa R package (disclaimer: I’m the developer), which compares the dispersion in the data with the dispersion of simulated data from the fitted model.
How to check for overdispersion of GLMMs, lme4?
Because the marginal variance among sites in your data is less than would be expected from a binomial variable; the variance can’t be negative, so it’s estimated as zero. The GLMM FAQ discusses this. How do I check for overdispersion in this model?
Can you test for overdispersion in a logistic regression?
However, note that the raw 0/1 response in a logistic regression cannot have overdispersion, so this test (as any other dispersion test) will never be positive. Overdispersion tests on a 0/1 response only make sense if you group the residuals. See the comments specific to binomial responses in the DHARMa vignette here.
How to check for over dispersion in a model?
Over-dispersion is a problem if the conditional variance (residual variance) is larger than the conditional mean. One way to check for and deal with over-dispersion is to run a quasi-poisson model, which fits an extra dispersion parameter to account for that extra variance. Now let’s fit a quasi-Poisson model to the same data.
How to model over dispersion in count data?
An alternative approach to modeling over-dispersion in count data is to startfrom a Poisson regression model and add a multiplicativerandom eecto represent unobserved heterogeneity. This leads to the negative binomialregression model.
How does the dispersion parameter affect negative binomial regression?
The coefficients have an additive effect in the l n ( y) scale and the IRR have a multiplicative effect in the y scale. The dispersion parameter in negative binomial regression does not affect the expected counts, but it does affect the estimated variance of the expected counts.