[R] Interaction term not significant when using glm???
chaogai at xs4all.nl
Sat Mar 7 09:53:38 CET 2009
I think the interaction is not so strong anymore if you do what glm
does: use a logit transformation.
testdata <- as.data.frame(testdata)
testdata$Freq <- as.numeric(as.character(testdata$Freq))
testdata$spot <- as.numeric(as.character(testdata$spot))
T2$Prop <- T2$Freq.0/(T2$Freq.0+T2$Freq.1)
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 <-
> 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
> [[alternative HTML version deleted]]
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
More information about the R-help