You can compute the conditional probability that your variable equals k given
that it is non-zero. For example, if X has poisson distribution with parameter
lambda then
P(X=k/X!=0) = P(X=k)/(1-P(X=0)) = (exp(-lambda)/(1-exp(-lambda))*lambda^k/k!
Now you can find lambda for which the sum of squares of your errors is minimal
and then use CHi-aquared test using these expected frequencies.
Similarly for negative binomial distribution.
--- On Tue, 23/2/10, pinusan anh...@msu.edu wrote:
From: pinusan anh...@msu.edu
Subject: [R] Goodness of fit test for count data
To: r-help@r-project.org
Received: Tuesday, 23 February, 2010, 6:11 AM
Dear all,
I am trying to test goodness of fit. I assume that a data
follow Poisson or
Negative binomial distribution. I can test the goodness of
fit in case of no
truncated data. However, I could not find any good function
or packages when
a data is truncated.
For example, a frequency table for the number of visiting
emergency room in
one hundred one observations past one year is as follow:
N freq
1 30
2 35
3 26
4 8
5 0
6 2
7 0
I expect the frequency table to satisfy a Poisson
distribution or Negative
binomial distribution. However, the distribution is
different from the usual
Poisson or Negative binomial distribution because one
value, zero, is
excluded. I expect that the distribution is zero truncated
distribution.
In case of SAS, I used NLMIXED procedure to calculate the
expected
probability when y=1 … y=n under the assumption that a
data follows Poisson
or Negative binomial distribution. And then I run
Chi-square test. If you
need the SAS code, I will send E-mail.
I want to run this test in R.
Could you suggest any idea that can I perform this test in
R.
Have a nice day.
--
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