From: John Maindonald <john.maindonald_at_anu.edu.au>

Date: Fri 07 Oct 2005 - 20:44:14 EST

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.html Received on Fri Oct 07 21:35:06 2005

Date: Fri 07 Oct 2005 - 20:44:14 EST

As an alternative to the effects package, try predict() with
type="terms"

JM

On 7 Oct 2005, at 8:00 PM, Peter Dunn wrote:

> From: r-help-bounces@stat.math.ethz.ch

*> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Peter Dunn
**> Sent: Wednesday, October 05, 2005 9:06 PM
**> To: R-help mailing list
**> Subject: [R] R/S-Plus equivalent to Genstat "predict":
**> predictions over "averages" of covariates
**>
**> 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:
**>
**>
**>> 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 data set when making the predictions. Reasonable anmd easy.
**>
**> But for categorical variables (like Month), he implies we use
**> a weighted average of the fitted coefficients for all the
**> months, depending on the proportion of times those factor
**> levels appear in the data.
**>
**> (I hope I explained that OK...)
**>
**> Is there an equivalent way in R or S-Plus of doing this? I
**> have to do it for a number of sites and species, so an
**> automated way would be useful. I have tried searching to no
**> avail (but may not be searching on the correct terms), and
**> tried hard-coding something myself as yet unsuccessfully:
**> The poly terms and the use of the weighted averaging over
**> the factor levels are proving a bit too much for my limited skills.
**>
**> Any assistance appreciated. (Any clarification of what I
**> mean can be provided if I have not been clear.)
**>
**> Thanks, as always.
**>
**> P.
*

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.html Received on Fri Oct 07 21:35:06 2005

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