Hi,

I think I understand that if you are comparing models using AICc, you
should use ML to compare models with the same random effects but different
fixed effects.  Therefore ML should be used during model selection.
 However, when you present the effect sizes of your final model, you should
use REML, because it provides better estimates of beta (please correct me
if I'm wrong here!).  However, I am now interested in proceeding to model
averaging, and I'm unclear whether I should be using ML or REML for this
stage of the analysis - ML will provide better estimates of AIC weights (I
assume?) but REML will provide better estimates of beta.  So does anyone
know which I should be using?

Thanks for your help

Paul York

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