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Negative binomial regression for count data

Hi,
I do not know the article. Notice that an excess of zeroes can lead to 
(spurious) overdispersion in data, therefore you should decide whether 
assuming a zip ( zero excess coming from a "mixture") or a negBin (zero 
execess due to overdispersion) model. Of course some likelihood based 
criteria (eg AIC) could be of help for you

However it is possible (at least in principle) to account for both extra 
zeros  and overdispersion as well. S. Jackman has written code to fit 
zip or zinb regression models http://pscl.stanford.edu/zeroinfl.r You 
should modify the code if you want to assume different "linear 
predictors" in the logit (zero vs non/zero) and count part

Hope this helps,
vito
Seyed Reza Jafarzadeh wrote: