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.
So to take overdispersion into account, I tried to add an 
observation-level random effect and here again variance was almost zero 
and therefore nothing (but just one more df) change when using the 
dispersion_glmer function. Moreover, each time I added the observation 
level-random effect AIC was higher ...

Any thoughts to explain why adding the observation-level random effect 
did not improve overdispersion ?

I can send a bit of R code and dataset if needed.

Thanks for your help,
Thomas
-- 
Thomas Merkling, PhD
Fyssen post-doctoral researcher (Pryke lab)
Website <https://sites.google.com/site/merklingthomas/>

Evolution, Ecology and Genetics
Research School of Biology
The Australian National University
Canberra ACT 0200
Tel: +61 (0)2 612 530 29

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