Dear Peter, See the effects package, described in <http://www.jstatsoft.org/counter.php?id=75&url=v08/i15/effect-displays-revi sed.pdf>.
I hope this helps, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] 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. > > > 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 > > > > > > -- > Dr Peter Dunn | Senior Lecturer in Statistics Faculty of > Sciences, University of Southern Queensland > Web: http://www.sci.usq.edu.au/staff/dunn > Email: dunn <at> usq.edu.au > CRICOS: QLD 00244B | NSW 02225M | VIC 02387D | WA 02521C > > ______________________________________________ > 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 ______________________________________________ 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