From: Kjetil Holuerson <kjetil_at_redcotel.bo>

Date: Thu 06 Oct 2005 - 13:38:42 EST

*>
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> So for quantitative variables (such as pH), one uses the mean pH in the

*> data set when making the predictions. Reasonable anmd easy.
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*>
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*> But for categorical variables (like Month), he implies we use a weighted
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*> average of the fitted coefficients for all the months, depending on the
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*> proportion of times those factor levels appear in the data.
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*>
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*> (I hope I explained that OK...)
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*>
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*> Is there an equivalent way in R or S-Plus of doing this? I have to do
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*> it for a number of sites and species, so an automated way would be
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*> useful. I have tried searching to no avail (but may not be searching
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*> on the correct terms), and tried hard-coding something myself
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*> as yet unsuccessfully: The poly terms and the use of the weighted
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*> averaging over the factor levels are proving a bit too much for my
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*> limited skills.
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*>
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*> Any assistance appreciated. (Any clarification of what I mean can be
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*> provided if I have not been clear.)
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*>
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*> Thanks, as always.
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*>
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*> P.
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*>
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*> > version
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*> _
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*> platform i386-pc-linux-gnu
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*> arch i386
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*> os linux-gnu
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*> system i386, linux-gnu
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*> status
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*> major 2
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*> minor 1.0
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*> year 2005
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*> month 04
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*> day 18
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*> language R
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*> >
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*>
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*>
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*>
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Date: Thu 06 Oct 2005 - 13:38:42 EST

check out the effects package on CRAN.

Kjetil

Peter Dunn wrote:

> Hi all

*>
**> I'm doing some things with a colleague comparing different
**> sorts of models. My colleague has fitted a number of glms in
**> Genstat (which I have never used), while the glm I have
**> been using is only available for R.
**>
**> He has a spreadsheet of fitted means from each of his models
**> obtained from using the Genstat "predict" function. For
**> example, suppose we fit the model of the type
**> glm.out <- glm( y ~ factor(F1) + factor(F2) + X1 + poly(X2,2) +
**> poly(X3,2), family=...)
**>
**> Then he produces a table like this (made up, but similar):
**>
**> F1(level1) 12.2
**> F1(level2) 14.2
**> F1(level3) 15.3
**> F2(level1) 10.3
**> F2(level2) 9.1
**> X1=0 10.2
**> X1=0.5 10.4
**> X1=1 10.4
**> X1=1.5 10.5
**> X1=2 10.9
**> X1=2.5 11.9
**> X1=3 11.8
**> X2=0 12.0
**> X2=0.5 12.2
**> X2=1 12.5
**> X2=1.5 12.9
**> X2=2 13.0
**> X2=2.5 13.1
**> X2=3 13.5
**>
**> Each of the numbers are a predicted mean. So when X1=0, on average
**> we predict an outcome of 10.2.
**>
**> To obtain these figures in Genstat, he uses the Genstat "predict"
**> function. When I asked for an explanation of how it was done (ie to
**> make the "predictions", what values of the other covariates were used) I
**> was told:
**>
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>> So, for a one-dimensional table of fitted means for any factor (or >> variate), all other variates are set to their average values; and the >> factor constants (including the first, at zero) are given a weighted >> average depending on their respective numbers of observations.

> So for quantitative variables (such as pH), one uses the mean pH in the

-- ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Thu Oct 06 22:57:12 2005

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