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

[Phillip Alday]

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
>
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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
>>
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