Date: Thu, 15 May 1997 06:03:37 GMT
Reply-To: Gerard Smits <gerards@DELTANET.COM>
Sender: "SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>
From: Gerard Smits <gerards@DELTANET.COM>
Organization: Delta Internet Services, Inc.
Subject: Re: Test of Poisson Distribution
I recently tried genmod on a distribution I though might be Poisson. It
was the number
of days in ICU (hospital). Clearly, the data do not meet the independence
I thought I'd check it out and compare it to a rank sum test. I used
resampling with a
monte carlo loop of 1000. I found, after feeding a 1 day difference (this
was a test of power)
that the genmod was not as powerful as the rank sum.
More inportantly, I reran the simulation with no difference between the two
resampled groups. Using an alpha of .05, I got a Type 1 error rate of 5%
rank sum, but a 35% error rate with the genmod poisson regression. I later
true poisson variates and got the error rate I expected.
So, in short, the poisson model does not appear to be robust with respect
of the distrbution assumptions.
Charlie Hofacker <firstname.lastname@example.org> wrote in article
> Dear SAS wizards and assorted hangers on,
> I have a set of count data which represents the incidences of
> certain behaviors across a large sample. A frequency distribution
> reveals something that looks a lot like a Poisson with zero
> being the most common frequency, followed by 1 and so forth. Also
> the mean and the variance are pretty similar.
> I would like to test the hypothesis that the incidence of this
> behavior is Poisson, preferably by maximum likelihood. I originally
> thought I could use GENMOD to do this with an intercept only model,
> but it didn't seem to fit the bill. I vaguely remember a macro
> to do this reported in a SUGI Proceedings from the early 1980's.
> Any ideas would be appreciated....