Dear Mark and Simon, I assume from the variable names that siteall and district are factors and that yrs is numeric. If that's the case, then the second model formula, ~ siteall + district + yrs:district, nests yrs within district, that is, will fit a separate slope for years within each level of district -- what you'd get by ~ siteall + district/years or ~ siteall + district + yrs %in% district. This model is equivalent to ~ siteall + yrs*district, although it's parametrized differently. To see what's happening, check model.matrix(test1) and model.matrix(test2).
More generally, R avoids violating marginality. If you want "type-III" tests, you could use the Anova() function in the car package, but if I properly interpreted the meaning of the predictors, the "type-III" test for the "main effect" of yrs is simply the test that the slope for yrs is 0 in the first (reference) category of district, assuming that you're using the default dummy-coded (contr.treatment) contrasts -- not generally a particularly interesting hypothesis. Regards, John ------------------------------ John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of markle...@verizon.net > Sent: February-19-09 6:23 AM > To: Simon Pickett; r-help@r-project.org > Subject: Re: [R] type III effect from glm() > > Hi Simon: In below , test1 spelled out is count ~ siteall + yrs + > district + yrs:district so this is fine. > > but in test2 , you have years interacting with district but not the main > effect for years. this is against the rules of marginality so I still > think there's a problem. I would wait for John or the other wizaRds to > respond ( you know who you are ) because I don't feel particularly > confident giving advice on this because I bang my head against it often > also. Plus, I gotta go home because it's getting light out soon ( i'm in > the US on the east coast ). Good luck. > > > > > On Thu, Feb 19, 2009 at 6:10 AM, Simon Pickett wrote: > > > Cheers Mark, > > > > I did originally think too, i.e. that not including the main effect > > was the problem. However, the same thing happens when I include main > > effects.... > > > > > > test1<- > glm(count~siteall+yrs*district,family=quasipoisson,weights=weight,data=m[x[[ i > ]],]) > > > > test2<- > glm(count~siteall+district+yrs:district,family=quasipoisson,weights=weight,d a > ta=m[x[[i]],]) > > anova(test1,test2,test="F") > > > > Model 1: count ~ siteall + yrs * district > > Model 2: count ~ siteall + district + yrs:district > > Resid. Df Resid. Dev Df Deviance F Pr(>F) > > 1 1933 75665 > > 2 1933 75665 0 0 > > > > Simon. > > > > > > > > > > ----- Original Message ----- From: <markle...@verizon.net> > > To: "Simon Pickett" <simon.pick...@bto.org> > > Sent: Thursday, February 19, 2009 10:50 AM > > Subject: RE: [R] type III effect from glm() > > > > > >> Hi Simon: John Fox can say a lot more about below but I've been > >> reading his book over and over recently and one thing he constantly > >> stresses is marginality which he defines as always including the > >> lower order term if you include it in a higher order term. So, I > >> think below is problematic because you are including an interaction > >> that includes the main effect but not including the main effect. This > >> definitely causes problems when trying to interpret > >> the anova table or the Anova table. That's as much as I can say. I > >> highly recommed his text for this sort of thing and hopefully he will > >> respond. > >> > >> Oh, my point is that if you want to check the effect of yrs, then I > >> think you have to take it out of model 2 totally in order to > >> interpret the anova ( or the Anova ) table. > >> > >> On Thu, Feb 19, 2009 at 5:38 AM, Simon Pickett 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. > >> > > ______________________________________________ > 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. ______________________________________________ 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.
