Craig Lyon <craig.lyon <at> rogers.com> writes:
>
> Hi,
>
> I am trying to run a generalized linear regression using a negative binomial
> error distribution. However, I want to use an overdispersion parameter that
> varies (dependent on the length of a stretch of road) so glm.nb will not do.
>
> >From what I've read I should be able to do this using GLM by specifying my
> own quasi family and describing the variance function using varfun, validmu,
> dev.resids and initiate. However I cannot find any detailed discussion of
> this method. Any help would be appreciated.
>
> ps I've done this in SAS so I know it should be possible!
>
I think you'd be better off with the bbmle package, e.g.
something like
library(bbmle)
mle2(response~dnbinom(mu=exp(logmu),size=exp(logk)),data=mydata,
parameters=list(logmu~fac1+fac2,logk=roadlength),
start=list(logmu=1,logk=0))
Slightly less efficient than setting up your own quasi family
but probably much faster in terms of analyst time ...
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