Generelized Negative Binomial model in R

Steve Lianoglou <mailinglist.honeypot <at> gmail.com> writes:

Hi,

On Wed, Oct 12, 2011 at 11:23 AM, Akram Khaleghei Ghosheh balagh
<a.khaleghei <at> gmail.com> wrote:

Hello;

Does anybody knows that R have a function for Generelized Negative Binomial
model, something like "gnbreg" in "STATA" where dispersion parameter itself
is a function of covaraites ?

Take a look at the edgeR (and DESeq) package in bioconductor.

edgeR uses a GLMs w/ negative binomial to assess differential
expression of genomic regions using count data (aka next generation
sequencing data).

http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
http://www.bioconductor.org/packages/release/bioc/html/DESeq.html

You could code it fairly easily in mle2, e.g.

mle2(y~dnbinom(exp(logmu),exp(logk)),
       data=..., start=...,
       parameters=list(logmu~...,logk~...)

where the ... within parameters specify linear models for the log-mean
and log-overdispersion parameters.
  You do have to specify your own starting conditions, and it doesn't
do anything clever in terms of special-purpose optimization -- it just
uses the optimizers built into optim() [with a few other choices, e.g.
those from the optimx package]