Hello all, 

I'm monitoring groups of individuals all composed of one male only and several females. I want to test if var1 is correlated with var2 (both measured at the individual level) within the population, and if the correlation varies between sexes. Groups (and so individuals) have been studied only once.


I was thinking of fitting:
glmer(var2~var1*sex+(var1*sex+0 |group))
to account for possible heterogeneity between groups. However, this should not be estimable for males no? (actually it does run and returns some variance estimates for var1:male, so I don't get what is happening).


If true, then 
glmer(var2~var1*sex+(var1+0 |group))
could be a more suitable model, but I wonder how to interpret the random effect on the main effect of var1, when the interaction var1:sex will be present as fixed effect only. I got from Doug Bates' paper: 
http://lme4.r-forge.r-project.org/slides/2009-07-21-Seewiesen/6Interactions-4a4.pdf
(see p. 6 top-left) that this should make sense somehow, but as he does not explain the interpretation of this model and that I'm too dumb to do it myself, any hint would be appreciated. 

Thanks again to all for the time devoted to supporting this list.

Jehol.

Any suggestion on better ways to address my question with another model would be welcomed too. I particularly thought about modelling it along these lines:
glmer(value~typeofvariable*sex+(sex|group)
where typeofvariable would code for value being either  var1 or var2. This would allow having a random effect of groups on BOTH var1 and var2 (which is not feasible with the models above), and estimating the correlation between random intercept and slope (can it be formally tested?). In any case, this model still leaves me with the problem of fitting/interpreting the random slope for males. 
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