Skew as they are, your data certainly don't look normal. Try lognormal.
The chi-square test gives good results when all counts are 5 or more,
hence the warning.
At 12:25 AM 11/12/2010, Andrew Halford wrote:
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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