I have a concern with a GLMM I am running and I would be very grateful if you could help me out.
I am modelling the factors that cause a frog species to make either a type 1 or type 2 calls. I am using a GLMM logistic regression. The data from this were generated from recordings of individuals in frog choruses of various sizes. For each male in the dataset, I randomly chose 100 of his calls, and then determined if they were type 1 or 2 (type 1 call =0, type 2 call =1). So each frog is represented by the same number of calls (100 calls), and some frogs are represented in several choruses of different sizes (total n= 12400). The response variable is whether each call in the dataset is type 1 or 2, and my fixed effects are: the size of the chorus a frog is calling in (2,3,4,5,6), the body condition of the frog (residuals from an LM of mass on body length), and standardized body length (SVL) (body length and body condition score are not correlated so no VIF issues). I included frog ID and the chorus ID as random intercepts.
The model looks fine, and the coefficients seem sensible; they are about what I expect. The only thing that worries me is that, when I calculate the 95%CI for the coefficients, the coefficient for body condition has a huge range (-9.7 to 6.3) (see screenshot). This seems crazy. Even when exponentiated, it seems quite crazy (0 to 492). Is this reasonable? This variable was involved in a significant interaction with chorus size; does this explain a wide CI? Or does this suggest my approach is flawed? Instead of having each male equally represented in the dataset by 100 calls in each chorus he is in, should I instead collapse that down to a proportion (e.g. proportion of type 2 calls out of the 100 randomly selected calls for each male) and model this as a poisson regression or something? Is the way I’m doing my logistic regression a reasonable approach? I have run model checks and everything and they all seem to point to logistic regression being suitable for my data, at least as I have set it up currently.
Thanks for any help you can provide!
Values I get after standardizing condition:
2.5 % 97.5 %
.sig01 2.0948132676 3.1943483
.sig02 0.0000000000 2.0980214
(Intercept) -3.1595281536 -1.2902779
chorus_size 0.8936643930 1.0418465
cond_resid -0.8872467384 0.5746653
svl -0.0865697646 1.2413117
chorus_size:cond_resid -0.0005998784 0.1383067