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Mixed model correlation structure for unbalanced, longitudinal data

Andy Flies 
Hello Alain,

Thank you for responding to my question.

My full data set has 96 observations from 72 individuals. The subset of 
data I referred to in my initial question was a subset that included 
observations on adult females that have no missing values in any of the 
covariates. I am primarily interested in the following question: Does 
social rank affect my response variables? Social rank data are available 
for all observations, whereas some of the covariates are missing values, 
so the subset sample size increases if I drop covariates from the 
analysis. Some of the covariates, such as age and sample collection 
date, are correlated for the repeated measures, so potential 
collinearity issues might justify dropping one of the variables from the 
analysis anyway. Additionally, simple graphs of the response variables 
vs. the covariates do not suggest two-way relationships in most cases. 
Based on the techniques outlined in your data exploration methods paper, 
I agree that it would be better to keep only 1 or 2 covariates in the 
analysis and use the collapsed mean of repeated measures, rather than 
using a mixed model approach.

I initially planned to include the sample collection date as a covariate 
in the subset models to assess whether or not the sample collection date 
is important in any of the data subsets. However, there is no biological 
reason to expect the effect of sample collection date would be different 
in any of the data subsets (i.e. males, females, juveniles, adults). I 
think it would be more appropriate to assess the effect of sample 
collection date in the full data set and then not include it as a 
covariate in the subset analysis.

Thanks you,

Andy