over-estimation Negative Binomial models

Ben Bolker · 2011-10-18T12:12:40Z

D_Tomas 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).

Ben Bolker

Tue, Oct 18, 2011 5:12 AM

D_Tomas <tomasmeca <at> hotmail.com> writes:

Nothing springs to mind immediately.  Can you post a reproducible
example?

  The trivial example below works:

[1] 4975

[1] 4975

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

[1] 4975

over-estimation Negative Binomial models

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