[R-sig-ME] Modelling with uncertain (but not missing) categorical random effect values

[Ben Bolker]

   Also see 
https://bbolker.github.io/mixedmodels-misc/notes/multimember.html (i.e. 
you can do it in lme4, but it takes a bit of hacking)

On 8/3/21 5:19 PM, Phillip Alday wrote:
> If I'm not mistaken, Thierry's suggestion is a particular case of
> multi-membership models, which you can also do in brms. See e.g.:
> 
> 
> https://rdrr.io/cran/brms/man/mm.html
> 
> https://github.com/paul-buerkner/brms/issues/130
> 
> https://discourse.mc-stan.org/t/cross-classified-multiple-membership-models-with-brms/8691
> 
> 
> On 13/07/2021 06:09, Thierry Onkelinx via R-sig-mixed-models wrote:
>> Dear Michael,
>>
>> Maybe something like (0 + w_1 | dad_1) + (0 + w_2 | dad_2) + (0 + w_3 |
>> dad_3). Where w_1 is the probability of dad_1.
>>
>> Make sure that dad_1, dad_2 and dad_3 are factors with the same levels.
>> Then INLA allows you to add this as f(dad_1, w_1, model = "iid") + f(dad_2,
>> w_2, copy = "dad_"1) + f(dad_3, w_3, copy = "dad_1"). So you end up with a
>> single random intercept for every dad (dad_2 and dad_3 share their
>> estimates with dad_1).
>>
>> mum_id  mum_sp  dad_sp dad_id                    con    dad_1   w_1 dad_2
>> w_ 2 dad_3 w_3
>>
>> Af1          A              A           Am1 / Am2             1      Am1
>> 0.6   Am2 0.4  NA 0
>> Af1          A              A           Am2                       1
>> Am2    1     NA     0     NA 0
>> Bf1          B             A           Am1 / Am2 / Am4   0      Am1    0.4
>>   Am2 0.3   Am4 0.3
>> Bf2          B              B          Bm1 / Bm3              1      Bm1
>> 0.5   Bm2  0.5  NA 0
>>
>> 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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>> www.inbo.be
>>
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>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
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>> ensure that a reasonable answer can be extracted from a given body of data.
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>>
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>>
>>
>> Op di 13 jul. 2021 om 12:30 schreef Michael Lawson via R-sig-mixed-models <
>> r-sig-mixed-models using r-project.org>:
>>
>>> I have a dataset where I have offspring paternity of females with
>>> males of different species. However, many of the offspring have
>>> ambiguous paternity - where I know the offspring must be from
>>> particular fathers, but not from others. The data currently looks a
>>> bit like this (but with many more rows per mum_id):
>>>
>>> mum_id  mum_sp  dad_sp dad_id                    con
>>>
>>> Af1          A              A           Am1 / Am2             1
>>> Af1          A              A           Am2                       1
>>> Bf1          B             A           Am1 / Am2 / Am4   0
>>> Bf2          B              B          Bm1 / Bm3              1
>>>
>>> Which I have so far run as a binomial GLMM with con (conspecific mating) as
>>> a binary response, mum_sp and dad_sp (species) as fixed factors and
>>> mum_id as a random factor - and have just not included dad_id as
>>> a random factor. The ambiguously assigned fathers in dad_id is also
>>> non-random, i.e.
>>> certain individuals are more likely to be ambiguously assigned than
>>> others, so just leaving these cases as NA is problematic.
>>>
>>> For some of the ambiguous assignments, I can also extract
>>> probabilities that a possible male is the father of the offspring,
>>> e.g. for the first row, father Am1 is 60% likely to be the father and
>>> Am2 40% likely.
>>>
>>> Are there any approaches where I can include the ambiguous dad_id in
>>> a GLMM framework? - where the uncertainty of the assignment contributes to
>>> the
>>> overall uncertainty in the tested relationship.
>>>
>>> Thank you for any suggestions,
>>> Mike
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
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-- 
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
Graduate chair, Mathematics & Statistics