Hi! I have fitted a Negative Binomial model (glm.nb) and a Poisson model (glm family=poisson) to some count data. Both have the same explanatory variables & dataset
When I call sum(fitted(model.poisson)) for my GLM-Poisson model, I obtain exactly the same number of counts as my data. However, when I call sum(fitted(model.neg.binomial)) for my Negative Binomial model I clearly obtain many more count data (approx 27% more counts). Can anyone explain why such stark contrast between the two models exist? Why is the Negative Binomial massively over-estimating the values? Does it have to do with the dispersion parameter of the Negative Binomial model? Any thoughts or suggestions will be much appreciate it. Tomas -- View this message in context: http://r.789695.n4.nabble.com/over-estimation-Negative-Binomial-models-tp3912692p3912692.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
