R help - Mar 2010 - logistic regression by group?

Hi,

Looking for a function in R that can help me calculate a parameter that
maximizes the likelihood over groups of observations.

The general formula is:

p = exp(xb) / sum(exp(xb))

So, according to the formulas I've seen published, to do this "by
group" is

product(p = exp(x_i * b_i) / sum(exp(x_i b_i)))

Where i represents a "group" and we iterate through each group.

Does anybody have any suggestions?

Thanks!


-N

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
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