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,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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