In a GLMM, one compares the conditional model including covariates with the
unconditional model to see whether the conditional model fits the data
better.

(1) For my unconditional model, a different random effects term fits better
(independent random effects) than for my conditional model (correlated
random effects). Is this very uncommon, and how can this be explained? Can
I compare these models (although they have different random effect terms)
with anova(m0,m1) to see whether my conditional model is better? If not,
what solution would you recommend for model comparison?

(2) Using pvals.fnc(m1) I get the error that this option isn't available
for correlated random effects (it works well for independent ones). I read
up on the whole p-value discussion for several days now, but found no
information as to the way to go when obliged to provide p-values having
models with correlated random effects.
"Error in pvals.fnc(m1) : MCMC sampling is not implemented in recent
versions of lme4 for models with random correlation parameters"

Thanks
E.

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