[R-sig-ME] zero values generalized mixed model

[Thomas Parmentier]

Hi all,
I’ve got a question concerning a generalized mixed model where some levels only have zero values.
I will try to explain my setup: I am testing location preference of ant-associated beetles (15 species) in ant nests. Therefore I made six-chamber nests with 6 identical connected pots. Ant workers stored all their brood (standardized volumes) in one chamber. This brood chamber also contained most workers. My aim is to test whether some species are attracted or repulsed from those dense brood chambers. Later, I want to link this location preference with other traits of the myrmecophiles (e.g. degree of parasitism).
I used a generalized (binomial) mixed model with presence (score 1) / absence (score 0) in the brood chamber as dependent variable. A model without intercept and an offset of (logit(1/6) was used to directly test the deviation of the observed proportions from the expected proportion by random distribution (1/6). Species = 15 species of myrmecophiles, replicate = experiment was run 15 times.
Model:
modelbrood<-glmer(BROOD_CHAMBER~offset(datasetlocation$baseline) -1+SOORT+(1|REPLICATE)+(1|ID), binomial(link=logit), data=datasetlocation)
So far, everything runs fine. However two species were never observed in the brood chambers, even with more than 50 individuals tested in total. So  they show a clear aversion of the brood chambers. However, when tested, SE are extremely large for those species, and consequently P values of 0.8 are given. So apparently the model struggles with levels of a factor with only zero values. When I change one zero to one in both species, the SE are much more reliable.

So my question is there a way to handle zero values in generalized mixed models?

Thanks a lot,

Thomas
________________________________
Laboratory of Socioecology and Socioevolution
K.U.Leuven
Naamsestraat 59
B-3000 Leuven
Belgium

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