This is a good point. You might consider a score test as well.

Moghimbeigi<author_page.cfm?id=81416603292&coll=GUIDE&dl=GUIDE&trk=0&CFID=107797713&CFTOKEN=68635639>,
et al. (2009) A score test for zero-inflation in multilevel count
data. Computational
Statistics & Data Analysis



On Fri, Oct 29, 2010 at 10:27 AM, ONKELINX, Thierry <
Thierry.ONKELINX@inbo.be> wrote:

> Hi Steve,
>
> In addition to the comments of Chris, I would like to add the a high
> number of zero's does not imply a zero-inflated distribution. Have a
> look at the example below.
>
> HTH,
>
> Thierry
>
>
> > set.seed(123)
> > # ordinay poisson with 91% zero
> > counts <- rpois(10000, lambda = 0.1)
> > mean(counts == 0)
> [1] 0.9105
> > table(counts)
> counts
>   0    1    2    3
> 9105  855   39    1
> > # ordinay poisson with 99% zero
> > counts <- rpois(10000, lambda = 0.01)
> > mean(counts == 0)
> [1] 0.9912
> > table(counts)
> counts
>   0    1
> 9912   88
> > # ordinay poisson without zero
> > counts <- rpois(10000, lambda = 10)
> > mean(counts == 0)
> [1] 0
> > table(counts)
> counts
>   1    2    3    4    5    6    7    8    9   10
>   4   21   86  203  389  606  887 1144 1277 1243
>  11   12   13   14   15   16   17   18   19   20
> 1147  904  707  553  367  218  108   75   36   10
>  21   22   24   27
>   9    4    1    1
> > # zero-inflated poisson with 50% zero's
> > # 20% zero's from the inflation
> > # 30% zero's from the poisson
> > # 50% non-zero from the poisson
> > zi <- rbinom(10000, prob = 0.2, size = 1)
> > counts <- rpois(10000, lambda = 1)
> > counts[zi == 1] <- 0
> > mean(counts == 0)
> [1] 0.4961
> > table(counts)
> counts
>   0    1    2    3    4    5    6    7
> 4961 2946 1463  472  129   25    3    1
>
> ------------------------------------------------------------------------
> ----
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek
> team Biometrie & Kwaliteitszorg
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> Research Institute for Nature and Forest
> team Biometrics & Quality Assurance
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> tel. + 32 54/436 185
> Thierry.Onkelinx@inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to
> say what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of
> data.
> ~ John Tukey
>
>
> > -----Oorspronkelijk bericht-----
> > Van: r-sig-mixed-models-bounces@r-project.org
> > [mailto:r-sig-mixed-models-bounces@r-project.org] Namens
> > Christopher Desjardins
> > Verzonden: vrijdag 29 oktober 2010 16:47
> > Aan: Steve Hong
> > CC: r-sig-mixed-models@r-project.org
> > Onderwerp: Re: [R-sig-ME] analysis of count data with many zero values
> >
> > 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
> > >
> > >        [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
> > > R-sig-mixed-models@r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >
> >
> >
> >
> > --
> > Christopher David Desjardins
> > Ph.D. student, Quantitative Methods in Education M.S.
> > student, Statistics University of Minnesota
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>



-- 
Christopher David Desjardins
Ph.D. student, Quantitative Methods in Education
M.S. student, Statistics
University of Minnesota

	[[alternative HTML version deleted]]


