Hi All,

I have a dataset consisting of abundance counts of a fish and I want to test
if my data are poisson in distribution or normal.

My first question is whether it is more appropriate to model my data
according to a poisson distribution (if my test says it conforms) or use
transformed data to normalise the data distribution?

I have been using the vcd package

gf<-goodfit(Y,type= "poisson",method= "MinChisq")

but i get the following error message

Warning message:
In optimize(chi2, range(count)) : NA/Inf replaced by maximum positive value


I then binned my count data to see if that might help

   V1 V2
1   5 34
2  10 30
3  15 10
4  20  8
5  25  7
6  30  0
7  35  3
8  40  2
9  45  3
10 50  1
11 55  0
12 60  1

but still received an error message

 Goodness-of-fit test for poisson distribution

            X^2 df P(> X^2)
Pearson 2573372 33        0
Warning message:
In summary.goodfit(gf) : Chi-squared approximation may be incorrect

Am I getting caught out because of zero counts or frequencies in my data?

Andy






-- 
Andrew Halford Ph.D
Associate Research Scientist
Marine Laboratory
University of Guam
Ph: +1 671 734 2948

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