type III effect from glm()

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.
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

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type III effect from glm()

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