Two alternatives to the zero inflated Poisson (ZIP) model are
mentioned
in Jung, Jhun, and Lee (Biometrics, vol 61, no 2, June 2005,
p626):
"Although the ZIP model is more general than the standard Poisson,
count
data with many zeros are often more dispersed than the ZIP model.
In
this case, the use of a zero-inflated negative binomial (ZINB)
distribution or a zero-inflated generalized Poisson distribution is
a
good alternative."
best,
Richard
--
Richard E. Remington
Statistician
KERN Statistical Services, Inc.
PO Box 1046
Boise, ID 83701
Tel: 208.426.0113
KernStat.com
Andrew Robinson wrote:
Hi Richard,
I'm not sure that I can imagine how data can have too many zeros
fit well with zero-inflated Poisson models. Won't the excess
accommodated by increasing the the inflation?
In any case, if you want a model that separates the zeros from
occurrences before fitting a Poisson model to account for
abundance then it might be safest to do that split manually.
Another angle to try is to treat it as a special case of a
mixture regression. I think that some of Jim Lindsey's code will
such models. Google can help you find his wbsite.
An MS student of mine explored these models for regeneration
I'd be happy to send you a pdf of his thesis if it would help.
Cheers,
Andrew
On Mon, Jun 27, 2005 at 03:35:30PM -0400, Richard Chandler
Hello,
This is an (hopefully) improved question of one I posted several
ago. Does anyone know of a function for fitting "two-part"
These models are designed to handle count data with so many
that they can't be fit well with zero-inflated Poisson models or
'typical' GLMs. My understanding is that they work by first
binomial model to separate the zeros from the occurrences
integers) before fitting a Poisson model to account for variation
abundance.
I have tried help.search("two-part") and many other similar
Thanks,
Richard
--
Richard Chandler, M.S. Candidate
Department of Natural Resources Conservation
UMass Amherst
(413)545-1237