[R-sig-ME] Reconciling Near Identical AIC Values and Highly Significant P Value

Emmanuel Curis emmanuel.curis at parisdescartes.fr
Sun Apr 13 21:06:10 CEST 2014


I may completely misunderstand your problem, but if you replace a
predictor variable by another one with the same number of parameters
(let's say, for instance, « length » by « surface » in a linear
regression), then

 1) comparing AIC and likelihoods value is the same since the number
    of parameters does not change ;
    hence same AIC <=> predictors give equally good fits

 2) chi-square tests is meaningless, since models are not nested.
    Here, I guess you have the very low p because the function uses a
    0-degrees of freedom [same number of parameters...]  khi-square,
    that is the constant 0, and any value other than 0 as a null
    probability, hence p = 0 < whatever you want...

>From 2), it results than comparing AIC is, between your two options,
the only one valid when models are not nested.

For the second point, I don't know.

Hope this helps,

On Sun, Apr 13, 2014 at 02:52:20PM -0400, AvianResearchDivision wrote:
« 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]]
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                                Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

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