type III effect from glm()
Hi Simon,
I want to know if yrs (a continuous variable) has a significant unique effect in the model, so I fit a simplified model with the main effect ommitted...
[A different approach...] This is not really a sensible question until you have established that there is no significant interaction between "yrs" and "district." If this interaction is significant then, ipso facto, the effect of "yrs" is not unique but depends on "district." So establish that first. There is a good section on marginality in MASS (Venables & Ripley) and, as Mark has mentioned, in Prof Fox's texts. From what I can remember, some of these tests are reparametrized behind the scenes to enforce the marginality constraint. Regards, Mark.
Simon Pickett-4 wrote:
Hi all, This could be naivety/stupidity on my part rather than a problem with model output, but here goes.... I have fitted a fairly simple model m1<-glm(count~siteall+yrs+yrs:district,family=quasipoisson,weights=weight,data=m[x[[i]],]) I want to know if yrs (a continuous variable) has a significant unique effect in the model, so I fit a simplified model with the main effect ommitted... m2<-glm(count~siteall+yrs:district,family=quasipoisson,weights=weight,data=m[x[[i]],]) then compare models using anova() anova(m1,m1b,test="F") Analysis of Deviance Table Model 1: count ~ siteall + yrs + yrs:district Model 2: count ~ siteall + yrs:district Resid. Df Resid. Dev Df Deviance F Pr(>F) 1 1936 75913 2 1936 75913 0 0
The d.f.'s are exactly the same, is this right? Can I only test the significance of a main effect when it is not in an interaction? Thanks in advance, Simon. Dr. Simon Pickett Research Ecologist Land Use Department Terrestrial Unit British Trust for Ornithology The Nunnery Thetford Norfolk IP242PU 01842750050 [[alternative HTML version deleted]]
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