Hi,

Thank you for your response.  How then would you proceed with testing two
models that are not nested within each other?  I suppose I could compare
AIC values, but is it correct to use the AIC values obtained using my
initial method of anova(model2,model)?

Thank you,
Jacob


On Sun, Apr 13, 2014 at 3:06 PM, Emmanuel Curis <
emmanuel.curis@parisdescartes.fr> wrote:

> Hi,
>
> 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@parisdescartes.fr
>
> Page WWW: http://emmanuel.curis.online.fr/index.html
>

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