I think the interaction is not so strong anymore if you do what glm
does: use a logit transformation.
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)
testdata$Freq <- as.numeric(as.character(testdata$Freq))
testdata$spot <- as.numeric(as.character(testdata$spot))
T2 <-
reshape(testdata,v.names='Freq',timevar='spot',idvar=names(testdata)[c(2,3)],direction='wide')
T2$Prop <- T2$Freq.0/(T2$Freq.0+T2$Freq.1)
plot(log(T2$Prop/(1-T2$Prop)),x=interaction(T2$constr,T2$vernalized))
Kees
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?
>
> # 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
>
> [[alternative HTML version deleted]]
>
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