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?

# data generation
testdata <-
matrix(c(rep(0:1,times=4),rep(c("FLC","FLC","free","free"),times=2),
  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,
family="binomial")
anova(T0fit)

Kind regards
Joris

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