R/S-Plus equivalent to Genstat "predict"

From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at 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.