D_Tomas <tomasmeca <at> hotmail.com> writes: > 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? >
Nothing springs to mind immediately. Can you post a reproducible example? The trivial example below works: > z <- rpois(1000,5) > pm <- glm(z~1,family=poisson) > sum(fitted(pm)) [1] 4975 > sum(z) [1] 4975 > nbm <- MASS::glm.nb(z~1) Warning messages: 1: In theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace > : iteration limit reached 2: In theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace > : iteration limit reached > sum(fitted(nbm)) [1] 4975 ______________________________________________ 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.
