[R-sig-ME] Multiple-membership models with lme4

[Sijia Huang]

Thanks for the response, Thierry!

I am fitting a multiple-membership model. Since a participant can belong to
more than one cluster, it is possible that the cluster number (number of
random effects) is larger than the number of observations. I wonder if
there is any trick I can perform in lme4 if I want to fit this model with
lme4.

Thank you all!


Best,
sIJIA

On Sun, May 17, 2020 at 11:35 PM Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> Dear Sijia,
>
> The error message seems clear to me. The number of random effect levels
> must be less than the number of observations. Otherwise the can't distinct
> between the random effect and the residual.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
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> thierry.onkelinx using inbo.be
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> Op ma 18 mei 2020 om 07:32 schreef Sijia Huang <huangsjcc using gmail.com>:
>
>> Hi everyone,
>> I am working on estimating multiple membership models with lme4, following
>> the instructions posted here
>> https://bbolker.github.io/mixedmodels-misc/notes/multimember.html
>>
>> Below is my code, in which J2 is the number of clusters (in my case, the
>> clusters are clique-2s, and J2=1345) and N is the number of participants
>> (N=968). These participants belong to 0 to 11 of the clique-2s.
>>
>> I got the below error. Could anyone help? Thanks!
>>
>> > fake2 <- rep(1:J2, length.out=N)
>> > lmod  <- lFormula(formula=y~1+(1|fake2), data=data)
>> Error: number of levels of each grouping factor must be < number of
>> observations
>>
>>
>>
>> Best,
>> Sijia
>>
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>>
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