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,data=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
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