Hi all,

When comparing identical models (only difference in predictor variable;
same d.f.) in lme4' using anova(model2,model), sometime I see nearly
identical AIC values like model2=1479.6 and model=1479.5 and a very low chi
sq. value like 0.1062, yet an extremely low p-value of <0.0001.  How would
you reconcile this?  Should we be more concerned with looking for
differences in AIC values of >3 when determing a better fit model, rather
than looking at a p-value?

Secondly, I read on the glmm.wikidot.com/faq page that when testing for the
significance of random effects, p values are conservative and are roughly
half what is returned when performing LRTs.  Do you find that what Pinheiro
and Bates (2000) states is sufficient to justify reporting the significance
of random effects when reported p values are between 0.05 and 0.10?  And is
it enough to convince you that is the case, especially when examining the
raw data with this in mind?

Thank you,
Jacob

	[[alternative HTML version deleted]]

