On Mon, 23 Feb 2004 13:45:31 +0100
Christoph Scherber <Christoph.Scherber@uni-jena.de> wrote:
> Dear all,
>
> I have several partially correlated explanatory variables that I want to
>
> analyse using analysis of covariance.
>
> The problem now is that order in model specification matters. I would
> therefore like to have an automated routine which permutates the
> positions of the explanatory variables, and then compares all resulting
> model versions using AIC.
>
> What I´d like to do is something like:
>
> explanatories_c(explanatory.1,explanatory.2,explanatory.3....explanator
> y.n) for (i in 1:n) explanatory[i]_sample((explanatories[i])
> model[i]_aov(response~explanatory[i])
> AIC(model[i+1],model[i])
>
> this whole procedure would then be replicated until the full nr of
> explanatory variables (say, 10) is tested in all possible positions
> (e.g. 10!)
>
> I would really appreciate any suggestions on this.
>
> Best regards
> Christoph.
There are faster systematic ways to go about this, but any of these
procedures are likely to destroy the meaning of P-values, confidence
limits, and result in biased regression models with overly optimistic R^2
and betas. If you are doing ANCOVA in order to test for a treatment
effect you are really in trouble. Pre-specify the model.
---
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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