As Ben pointed out, this is not reproducible. My guess is that the
offending sample is degenerate in some way, e.g., there are no
observations in one of the six sites or no non-zero responses.
hth,
Z
On Mon, 26 Dec 2011, Melissa Aikens wrote:
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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