From: Ben Bolker <bolker_at_ufl.edu>

Date: Thu 21 Jul 2005 - 08:48:31 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Jul 21 09:06:06 2005

Date: Thu 21 Jul 2005 - 08:48:31 EST

Thomas Isenbarger <isen <at> plantpath.wisc.edu> writes:

*>
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> I haven't been an R lister for a bit, but I hope to enlist someone's

*> help here. I think this is a simple question, so I hope the answer
**> is not much trouble. Can you please respond directly to this email
**> address in addition to the list (if responding to the list is
**> warranted)?
**>
**> I have a histogram and I want to see if the data fit a Poisson
**> distribution. How do I do this? It is preferable if it could be
**> done without having to install any or many packages.
**>
**> I use R Version 1.12 (1622) on OS X
**>
**> Thank-you very much,
**> Tom Isenbarger
**>
**> --
**> Tom Isenbarger PhD
**> isen <at> plantpath.wisc.edu
**> 608.265.0850
**>
**> [[alternative HTML version deleted]]
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by "histogram" do you mean that you have counts for each non-negative integer? or are the data more binned than that? (I'll assume the former since it's slightly easier to deal with.) Say you have vectors "number" and "count". The mean of the sample is meanval <- sum(number*count). The _expected_ number of counts in each bin if the distribution is Poisson is expval <- sum(count)*dpois(number,meanval). The chi-square statistic is csq <- sum((expval-count)^2/expval), with df <- length(count)-2 degrees of freedom (for the mean and the total number of observations). pchisq(csq,df=df,lower.tail=FALSE) should give you the chi-squared probability.

A couple of minor issues: (1) beware, this is shooting from the hip -- haven't tested at all; (2) you may have to deal with lumping categories (rule of thumb is that expected number of counts in a bin should not be < 5).

Other ways to tackle this: use fitdistr() from MASS with different candidate distributions (Poisson, neg. bin.) and do a likelihood ratio test or compare AICs; compare variance and mean of distribution (much cruder).

See

http://www.zoo.ufl.edu/bolker/emd/probs/ED-4.1.html
for a worked example of a similar problem.

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Jul 21 09:06:06 2005

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