[R] type III effect from glm()

[Simon Pickett]

Sorry, that was a typo in the email, not the model. So I still have the 
problem.....

Cheers, Simon.



----- Original Message ----- 
From: "Ted Harding" <Ted.Harding at manchester.ac.uk>
To: "Simon Pickett" <simon.pickett at bto.org>; <r-help at r-project.org>
Sent: Thursday, February 19, 2009 10:56 AM
Subject: RE: [R] type III effect from glm()


> On 19-Feb-09 10:38:50, 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]],])
>
> So, above, you have fitted two models: m1, m2
>
>> then compare models using anova()
>> anova(m1,m2,test="F")
>
> And here you are comparing two models: m1, m1b
>
> Could this be the reason for your result?
>
>> 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.
>
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> E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 19-Feb-09                                       Time: 10:56:12
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