Hi Simon, >> [On my response] ...not really a sensible question until...
On reading through this...what I mean is that yours seems not to be a "sensible approach," the question itself may be reasonable. What you want to be doing is testing whether the interaction term (yrs:district) gets dropped. Do it by comparing nested models (basically as you have done), or use dropterm() or stepAIC() [both are in MASS]. Regards, Mark. Mark Difford wrote: > > 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]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > > -- View this message in context: http://www.nabble.com/type-III-effect-from-glm%28%29-tp22097773p22099812.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.