I've been creating some models in R using
rxGlm(). I'm experienced in building GLMs but my memory of some of the underlying theory is a little rusty.
I'm interested in comparing model fits for nested models using chi-square tests, F tests, etc.
I'm able to compare nested glm model objects using
anova (model1, model2, test = "Chisq")
etc. From reading around the subject a little, it seems that chi-square is only valid for certain GLMs - those where the scale parameter is fixed (Poisson & binomial), whereas the F test should be used where the scale parameter is estimated (eg normal, gamma). Is this correct?
I have a particular interest in creating GLMs using the Tweedie family of distributions. Is this a case where F would be preferable to chi-square?