Re: st: group size needed for mixed models (binary response)

[Susan Lingle <susan.lingle@uleth.ca>]

Dear Jeph

Your response helps tremendously - I have been struggling with this for 
**quite** some time. I wanted to make the change to a mixed model, 
because it was in response to reviewers' comments (and it can be wise to 
heed such advice). But I am not familiar with mixed models, and in some 
ways it made sense but not in other ways. The initial plan when I 
captured fawns was to use data for the first sibling that was captured 
(most fawns have twins, but we did not always capture the twins, so the 
number of siblings is not a factor that I will include). That approach 
would be consistent with your "instinct" to select one sibling based on 
a certain criterion.

I am pretty sure survival of twins is correlated and can easily check 
that (in JMP, I'm not that familiar with Stata yet). Plus twins live in 
the same area and their area has a large influence on their survival. So 
another alternative is to include area as the random factor and not the 
mother's identity (mean of 36 fawns per 4 sub-areas within the study 
area). Previously I had planned to include area as a fixed factor.

This evening, I was considering comparing AIC (or BIC) values to select 
a model with or without the mother's identity included as a random 
factor. Would that be reasonable? Again, I am getting into procedures I 
have not used, and would prefer to stay on solid ground in this 
publication. I have not used AIC or BIC previously, but a quick check in 
Stata suggested that the model without the random factor of mother's 
identity has a slightly lower AIC & BIC and a slightly higher LL than 
the model that included the random factor. I could use the "unmixed" 
model and mention in passing that inclusion of the mother's identity did 
not improve the model.

I wanted to test a specific hypothesis (WT are more likely to die during 
summer; MD during winter) so did not plan to undertake a model selection 
procedure. Anyway, I think your response gives me enough food for 
thought now - checking the intraclass correlation is a sensible place to 
start before making the next decision.

Susan


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