[R] R/S-Plus equivalent to Genstat "predict": predictions over "averages" of covariates

[Kjetil Holuerson]

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:
> 
>> 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.
> 
>  > version
>           _
> platform i386-pc-linux-gnu
> arch     i386
> os       linux-gnu
> system   i386, linux-gnu
> status
> major    2
> minor    1.0
> year     2005
> month    04
> day      18
> language R
>  >
> 
> 
> 



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