accounting for overdispersion in binary GLMM in lme4
On 14-05-13 08:54 PM, Thomas Merkling wrote:
Hi all, I have a large dataset (n= 893) of chick sex (0 or 1) and I am looking at which variables can influence the probability of producing a male versus a female. I had to put random effects for year (as I have data for different years), fatherID and motherID (as I have many chicks for a same individual and mother and father were not always with the same partner). Surprisingly, variance was zero (or almost depending on the models) for all 3 random effects, but as I have seen that it can happen, so I kept going. A model looked like that: mod = glmer (CodeSex ~ X *Y + Z + (1|Year) + (1|FatherID) + (1|MotherID), family="binomial", data = sexratio) Using the dispersion_glmer function from the GLMM wiki website I estimated overdispersion. The ratio was around 1.4, which is not very high (but p-value highly significant, due to the large dataset?). I saw a lot of model alternatives when using count data, but the only one I found for binary data seem to be adding an observation-level random effect.
Overdispersion is unidentifiable for binary data (unless there are multiple observations with identical sets of predictors, in which case the data can be aggregated into binomial data with N>1), so all of this is more or less irrelevant/unnecessary ... Ben Bolker