[R-sig-ME] A question about multicollinear fixed/random factors

[João Veríssimo]

If I'm understanding this right, I don't think there is a problem at all.
Mixed-effects models can accommodate fixed effects at different levels 
and these can coexist with the random effects without any issues. Number 
of days is simply a 'Level-2' predictor, with a unique value for each 
meeting.

(for more complex random-effects structures, you might want to consider 
the inclusion of session as another random factor and/or of by-word 
random slopes)

João

On 20/08/2022 09:40, N o s t a l g i a wrote:
> Hi,
>
> I am looking at a character variation in Japanese parliamentary 
> minutes where the same character appears in two forms. In the 
> parliament, there are a number of different committee meetings within 
> the same session, and I am looking at 31 sessions over 10 years. The 
> factors I am considering are: upper/lower house distinction, meetings 
> (meetings within each session, which are different from session to 
> session), days between 1949/5/20 (when the first parliament was held) 
> and the meeting, and the word within which the character appears. Of 
> these, meetings and the words are random factors, and they have 
> hundreds of levels. The total number of cases is over one million.
>
> The model I am considering is:
>
> glmer (character ~ ul + days + (1|word) + (1|meeting), data = 
> glmmdata.1, family = binomial)
>
> And here is my question: Since a given meeting is a unique one not 
> only in each session but in all the data, there would be a 
> multicollinear relationship between the days and the meeting, so that 
> specification of some meeting would necessarily result in a specific 
> value of days. Is it a problem in GLMM to have such pair of fixed and 
> random factors? If it is so, is there any ways to avoid the problem?
>
> Thanks in advance,
>
> Kenjiro Matsuda
> Professor in Linguistics
> Kobe Shoin Women's University
> Kobe, Japan
>
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