Dear Dorris,

You might also want to consider using multimodel inference techniques as
outlined by Burnham and Anderson.  In my opinion, this technique is superior
to the step-wise model selection approach.  This will also provide an answer
to your question about p-values and interpretation.

Cheers!

Andrew










On Mon, Jul 19, 2010 at 5:32 PM, Christoph Scherber <
Christoph.Scherber@agr.uni-goettingen.de> wrote:

> Dear Doris,
>
> I assume you performed a likelihood ratio test on the two models?
>
> The model containing X1 has the lower AIC, so it is actually pointless to
> perform a likelihood ratio test on that model and a model containing both
> X1 and X2.
>
> Rather, to assess the significanced of X1, you should compare the model to
> a null model (containing the intercept only).
>
> The algorithm is like this:
>
> - Write down your minimal adequate model
> - Write down the corresponding null model
> - Perform likelihood ratio tests, starting with the null model and adding
> terms one at a time until you arrive at the minimal adequate model.
>
> Example:
>
> - minimal adequate model contains X1+X2+X3
> - Null model is ~1
> - Now do LRT´s with:
>
> ~1
> ~1+X1
> ~1+X1+X2
> ~1+X1+X2+X3
>
> And be sure to use;
> (1) QAIC in case of overdispersion
> (2) AICc in case of small sample sizes
>
> All the best
> Christoph
>
>
>
>
>
>
>
>
>
> > Dear lmer users,
> >
> > I compute several models with lmer (poisson distribution, correction for
> > overdispersion).
> >
> > I run several models with the same random effect and different structures
> > for
> > fixed effect (a full model and different models with only some of the
> > factors of
> > the full model).
> >
> > I select the model with the minimal AIC. In fact, two models are within 2
> > AIC
> > units:
> > - a model (with lowest AIC) with one factor, X1.
> > - another model (at 0.8 of the first) with two factors, X1 and X2.
> >
> > Looking at the p-values, X1 is significant in both models while X2 is not
> > significant, despite the low AIC associated to the model. How to
> interpret
> > the
> > apparent contradiction between the non-significant p-value and the low
> > AIC?
> >
> >
> >>From an old message on the list, I can see that p-values  are not good.
> > But the bayesian approach mcmcsample does not run either with lmer.
> >
> > How to get the p-values associated with the fixed effects?
> > Thank you for your help
> > Doris Gomez
> >
> >
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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>

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