I have a vector of count data that is strongly over dispersed and zero inflated.
The vector looks like this:
i.vec=c(0,63,1,4,1,44,2,2,1,0,1,0,0,0,0,1,0,0,3,0,0,2,0,0,0,0,0,2,0,0,0,0,
0,0,0,0,0,0,0,0,6,1,11,1,1,0,0,0,2)
m=mean(i.vec)
# 3.040816
sig=sd(i.vec)
# 10.86078
I would like to fit a distribution to this, which I strongly suspect will be a zero inflated poisson (ZIP). But I need to perform a significance test to demonstrate that a ZIP distribution fits the data.
If I had a normal distribution, I could do a chi square goodness of fit test using the function goodfit() in the package vcd, but I don't know of any tests that I can perform for zero inflated data.