[R] type III effect from glm()

[Simon Pickett]

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: <markleeds at verizon.net>
To: "Simon Pickett" <simon.pickett at 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]]
>>
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>