[R-sig-ME] stepwise model selection (of fixed effects only) using AIC?

[Steve Taylor]

Obrigado, Diego.  Yes I have studied a little bit of information theory, tho my recollections are hazy.

> you can not compare combinations of fixed effects of class "mer" with REML = TRUE.
Curious then, that that's the default value, and that the default anova() does precisely that by comparing two models differing only in the fixed effects included.

I'm aware of the objections, such as the danger of spurious relations.  But I cannot see why they prevent step(glmer()) when step(glm()) has been a standard feature in R for many years.  The real reason seems to be the fact that methods in package:stats don't work with S4 objects.

With my sample size, I think the difference between AIC and AICc is negligible.

cheers,
    Steve

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Diego Pujoni
Sent: Tuesday, 8 January 2013 2:04a
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] stepwise model selection (of fixed effects only) using AIC?

Hi Steve, have you heard about Information-Theoretic Approach? It uses the
value of AIC (or AICc) to choose the best hypothesis among many a priori
hypothesis. In Anderson (2008) "Model Based Inference in the Life Sciences"
we see recomendations against stepwise (or all possible models) because
this can lead easily to spurious relations. The author recommend to create
several a priori hypothesis (models), using knowledge about the system and
then use the AICc to look for the best of them. Another thing that you have
to pay attention is the fact that you can not compare combinations of fixed
effects of class "mer" with REML = TRUE.

A hug

-- 
                                               Diego PJ

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