Dear List,

I am wondering if many people have used the new 'blme' package, which
extends 'lmer' functions to include weak priors for estimating variance
parameters in multilevel models?
If so, can anybody help me work out how to summarise and report model fit
using this method?

I'm using the package to help estimate a variety of models where the
variance components are close to zero, and where the number of groups is
small (22). Some of the models have varying slopes. Everything seems to be
working fine, but I'm not sure how to summarise the model fit. The method
gives an AIC, BIC, LogLikelihood, Deviance and (deviance?) Penalty. I am
particularly interested in how I go about interpreting the last two. Should
I just stick to using LR tests to compare models, and report the log
likelihoods, or can I somehow use the deviance to report improvements in
model fit?

Any help would be really great, as I can't seem to find anything from other
users on the 'blme' package.

Cheers,

Heidi

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