I have some data which I am trying to fit with a negative binomial distribution. I have found the glm.nb function from MASS.
I have reason to believe that the mean parameter mu depends on certain factors, and that the shape parameter theta depends on others. If, say, the factors are P and Q, it might be that mu ~ P:Q and theta ~ P (where mu ~ P:Q means that mu is a function of the pair (P,Q)) in which case I could call glm.nb several times, one for each level of P (though this would be somewhat cumbersome). >From looking at the data, I have reason to believe that mu ~ P and theta ~ Q. How can I go about fitting this? I expect I will have to write my own likelihood-maximization routine, and I am happy to do this (though if there are packages that do it for me, so much the better). In fact, my real model is a mixture model, and for this I am sure I will have to write my own likelihood-maximization routine. What I want to learn from this list is if there are helpful commands that would do all the tedious work of working out how many variables there are etc. I'm hoping for some sort of call like gm( count ~ mydist(mu=p:q, theta=q) ) where all I need to write the likelihood-maximization routine and the function gm does the rest, providing the answer in a suitable format for doing chi-squared tests etc. Damon Wischik. ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
