On Tue, 25 Jan 2005, Florian Menzel wrote:
Hello all, I found a weird result of the GLM function that seems to be a bug.
No, the problem is that you are using the Wald test when the mle is infinite, which is always going to be unreliable. It's even worse because you are using data that couldn't really have come from a Poisson distribution (because for a=1 you have mean 3 and variance 0).
If you used anova(model) to get a likelihood ratio test the p-value would be 4e-10.
-thomas
The code: a=c(rep(1,8),rep(2,8)) b=c(rep(0,8),rep(3,8)) cbind(a,b) model=glm(b~a, family=poisson) summary(model) generates a dataset with two groups. One group consists entirely of zeros, the other of 3�s (as happened in a dataset I�m analyzing right now). Since they are count data, one should apply a poisson distribution. A GLM with poisson distribution delivers a p value > 0.99, thus, completely fails to detect the difference between the two groups. Why not and what should I do to avoid this error? A quasipoisson distribution detects the difference but I�m not sure whether it�s appropriate to use it. Thanks a lot to everybody who answers! Florian
Version information: version 1.9.0 (2004-4-12) os mingw32 arch i386
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Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle
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