[R] type III effect from glm()

[markleeds at verizon.net]

  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: <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
>>>
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>>>
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>>