Hi Listers

I am running some linear mixed models in lme4 (v. *-39, R 2.12.2 on 32-bit
pc windows xp), and taking a model averaging approach for estimating the
fixed effects coefficients. I was wondering whether the random effects
estimates of variance for these models (where only the fixed effects
structure changes) could also be averaged in the same way as the fixed
effects coefficients (i.e. weighted average based on the Aikake weights), or
whether this isn't appropriate at all? If it is not appropriate, are there
any recommendations/best practice for how to present the variance for the
random effects when presenting the average model? - i.e. should they be
presented for all models that are used for model averaging of the fixed
effects, or just for the global model or top model? I'm currently just
looking at random intercept models with only one grouping factor as I have
repeated measures for individuals so I'm including individual as a random
effect on the intercept with no interaction/random slope terms. But I would
also be interested in whether any recommendations would also apply where the
model averaging is applied to models with more complex random effects
structures such as nested random effects or random slopes.

If the random effects of these models can be averaged, is there a package
that can do that? (I'm aware of the MuMIn package which can handle model
averaging of fixed effects in lmer objects).

Thanks in advance for any help/advice
Nick

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