Zero-inflated Negative Binomial Error

Hello,

I am having a problem with the zero-inflated negative binomial (package
pscl).  I have 6 sites with plant populations, and I am trying to model the
number of seeds produced as a function of their size and their site.  There
are a lot of zero's because many of my plants get eaten before flowering,
thereby producing 0 seeds, and that varies by site.  Because of that and
because the variance exceeds the mean, I'm pretty sure the zero-inflated
negative binomial is the appropriate model to use.

Anyways, the code I have used for the zero-inflated binomial is:
fit.fec=zeroinfl(seeds~site/size-1 |
site-1,na.action=na.omit,dist="negbin",link="log",EM=TRUE)

This code works fine on the data I have.  The problem I'm having is when I
bootstrap this equation, after about 250 iterations, I get the following
error message: "Error: NA/NaN/Inf in foreign function call (arg 1)."
The bootstrap code is:

n.boot=1000
for(b.samp in 1:n.boot){

sample.boot=c(sample(1:104,replace=T),104+sample(1:70,replace=T),174+sample(1:71,replace=T),
 245+sample(1:87,replace=T),332+sample(1:55,replace=T),387+sample(1:125,replace=T))
size.boot=size[sample.boot]
seeds.boot=seeds[sample.boot]
fit.fec.boot=zeroinfl(seeds.boot~site/size.boot-1 |
site-1,na.action=na.omit,dist="negbin",link="log",EM=TRUE)
infl.slopes.boot=fit.fec.boot$coef$zero[1:6]
r.intercepts.boot=fit.fec.boot$coef$count[1:6]
r.slopes.boot=fit.fec.boot$coef$count[7:12]

...
}

In the above code, size and seeds are both vectors ordered by the site
vector, and the sample sizes for each site are found in the sample.boot
line.
I have looked at size.boot, seeds.boot, and site, and none of them have
missing values.  I'm really puzzled why it takes 250+ iterations for this
problem to crop up.  When I used a subset of this data, it took fewer
iterations for this problem to occur - so maybe it has to do with sample
size?  However, the number of individuals per site remains constant through
each iteration - only the values of size and seeds are changing.
Therefore, it must be some problematic combination of values that's being
chosen, though I can't for the life of me figure it out (for example, it
doesn't seem like there are an outrageous number of 0's when it crashes).
Also, I tried running the problematic size.boot and seeds.boot vectors with
just a negative binomial model (glm.nb), and I get the same error.  Can
anyone provide some insight into what is going on?

Thanks!

Best,
Melissa

-- 
Ph.D. Candidate
Department of Biology
University of Virginia
P.O. Box 400328
Charlottesville, VA 22904-4328

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