[R] Interaction term not significant when using glm???
tlumley at u.washington.edu
Sat Mar 7 11:57:17 CET 2009
On Fri, 6 Mar 2009, joris meys wrote:
> Dear all,
> I have a dataset where the interaction is more than obvious, but I was asked
> to give a p-value, so I ran a logistic regression using glm. Very funny, in
> the outcome the interaction term is NOT significant, although that's
> completely counterintuitive. There are 3 variables : spot (binary response),
> constr (gene construct) and vernalized (growth conditions). Only for the FLC
> construct after vernalization, the chance on spots should be lower. So in
> the model one would suspect the interaction term to be significant.
> Yet, only the two main terms are significant here. Can it be my data is too
> sparse to use these models? Am I using the wrong method?
The point estimate for the interaction term is large: 1.79, or an odds ratio of nearly 6.
The data are very strongly overdispersed (variance is 45 times larger than it should be), so they don't fit a binomial model well. If you used a quasibinomial model you would get no statistical significance for any of the terms.
I would say the problem is partly combination of the overdispersion and the sample size. It doesn't help that the situation appears to be a difference between the FLC:yes cell and the other three cells, a difference that is spread out over the three parameters.
> # data generation
> testdata <-
> rep(c("no","yes"),each =4),3,42,1,44,27,20,3,42),ncol=4)
> colnames(testdata) <-c("spot","constr","vernalized","Freq")
> testdata <- as.data.frame(testdata)
> # model
> T0fit <- glm(spot~constr*vernalized, weights=Freq, data=testdata,
> Kind regards
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Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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