Re: [R] general question about dropping terms of glm model fits

From: Frank Harrell <f.harrell_at_vanderbilt.edu>
Date: Fri, 18 Mar 2011 15:37:18 -0700 (PDT)

It will distort statistical inference to drop any terms on the basis of P-values, AIC, etc..

If you drop terms, use the hierarchy principle.

High correlations between variables don't necessarily invalidate a test. Frank

Sacha Viquerat-2 wrote:
>
> hello dear list!
> as I am currently helping someone with their statistical analysis of a
> count survey, I stumbled upon a very basic question upon model
> optimization:
>
> when fitting a model like:
>
> model<-lmer(abundance~(x+y+z)^3,family=poisson,...)
>
> in which x,y,z are continuous abiotic parameters such as po4
> concentration, no2-concentration, which terms / interaction terms would
> you recommend removing FIRST?
>
> the ones of lowest significance (i.e. the ones with highest p-value) OR
>
> the ones with the most complex interaction structure (even though
> p-values may be low-ish)?
>
> another question just popped in my mind:
>
> let's say I've reduced my model to significant terms:
>
> y ~ temperature + po4 + po4:temperature
>
> and I know that correlation between po4 and temperature is high. would
> you say that this is reason enough to remove the interaction term?
>
> any opinion is a welcome opinion!
>
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>



Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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Received on Fri 18 Mar 2011 - 22:40:43 GMT

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