Corey, Thanks for the quick reply.
I cant give any sample code as I don't know how to code this in R. That's why I tried to pass along some pseudo code. I'm looking for the best "beta" that maximize likelihood over all the groups. So, while your suggestion is close, it isn't quite what I need. I've seen the formula written as: L = product( exp(xb) / sum(exp(xb)) ) Where sum(exp(xb)) represents the sum of all the items in the group. Does that make sense? -N On 3/4/10 4:04 AM, Corey Sparks wrote: > Hi, first, you should always provide some repeatable code for us to have a > look at, that shows what you have tried so far. > That being said, you can use the subset= option in glm to subdivide your > data and run separate models like that, e.g. > > fit.1<-glm(y~x1+x2, data=yourdat, family=binomial, subset=group==1) > fit.2<-glm(y~x1+x2, data=yourdat, family=binomial, subset=group==2) > > where group is your grouping variable. > Which should give you that kind of stratified model. > Hope this helps, > Corey > > ----- > Corey Sparks, PhD > Assistant Professor > Department of Demography and Organization Studies > University of Texas at San Antonio > 501 West Durango Blvd > Monterey Building 2.270C > San Antonio, TX 78207 > 210-458-3166 > corey.sparks 'at' utsa.edu > https://rowdyspace.utsa.edu/users/ozd504/www/index.htm > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
