Hi Steve,

The MCMCglmm package has several different models that you could fit to
zero-inflated count data. You can fit zero-inflated Poisson models, hurdle
models, zero-alterated and zero-truncated models. I don't believe you can
fit zero-inflated negative binomials with that package but I could be wrong.
Also I believe that ZINB models work well when you have zero-inflated and
non-zero overdispersed data. You could also roll your own using rjags or
r2winbugs, etc.

There are lots of publications out there examining zero-inflation especially
using MCMC based approaches. (Do a quick Google Scholar search for
zero-inflated multilevel models). In addition, Jarrod Hadfield's CourseNotes
(they come w/ MCMCglmm) are also quite informative and provide some examples
of how you might fit such a model. In my experience with count data that are
highly zero-inflated (86% of all data were zeroes), the ZIP model worked
well but converged very slowly and required about 60,000 MCMC iterations. If
you'd like to see the code I can share it as well. Also I believe this topic
has come up several times and I would encourage to search through the
archives of R-Sig-Mixed-Models.

HTH,
Chris




On Fri, Oct 29, 2010 at 9:32 AM, Steve Hong <emptican@gmail.com> wrote:

> Dear list,
>
> This is the first time I have this type of data.  I have count data
> collected repeatedly from the same plot with multiple years (14 yrs) and
> have found that proportion of 'zero' values are very high (average of
> proportion is about 92 %, min: 53 %, max: 100 %).  Only one year has 53% of
> zeros in the data and the rest of years have at least greater than 86% zero
> values in the data set.
>
> The objective of the study is to develop predictive models and validate
> them, for example, using cross validation.
>
> Variables collected are: year, insect count, longitude, latitude, soil
> properties (x1...x4).
>
> Since data have too many zero observations, I am thinking about using zero
> inflated model to fit the data.  However, I am very new to this method.
>
> My questions are:
> 1. Is it possible to use zero inflated model to fit data with about 90%
> zeros?  I am wondering if zero proportion is too high to make any inference
> using statistical methods.
> 2. If I can use zero inflated models, can I use either Poisson distribution
> or negative binomial distribution?  Or both?
> 3. Do you have any good reference (paper and/or website) for good and
> 'easy'
> tutorial for me to study?
>
> I am wondering if I provided enough information or submitted it to correct
> mailing list.  Please let me know if you have any comments and suggestions.
> I would greatly appreciate that.
>
> Thank you very much in advance!!!
>
> Steve
>
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-- 
Christopher David Desjardins
Ph.D. student, Quantitative Methods in Education
M.S. student, Statistics
University of Minnesota

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