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

Ben Bolker bbo|ker @end|ng |rom gm@||@com
Mon Dec 20 19:10:52 CET 2021 

    Something like:

   lmod$reTrms$Ztlist <- list(Matrix(t(W)), Z2, Z3, Z4, ...)

where the additional `Z` components are the random-effects models for 
the additional terms you want (you could for example pull these from an 
`lFormula()` call using a formula that included those components in the 
random effects), and

   lmod$reTrms$Zt <- do.call(rbind, lmod$reTrms$Ztlist)

(I think)

On 12/20/21 12:43 PM, Mike Lawson wrote:
> Thanks for the suggestions Philip and Ben,
> 
> I'm coming back to this after a hiatus and this may be quite a basic question.
> 
> I managed to get the lme4 hack working with my data, but not if I
> include other random effects (that are not multi membership and not in
> the matrix).
> i.e. in the example you provide, if I add a new random predictor, how
> do I incorporate this into the model? As this line directly changes
> Ztlist and Zt to be the matrix:
> 
> lmod$reTrms$Zt <- lmod$reTrms$Ztlist[[1]] <- Matrix(t(W))
> 
> Many thanks,
> Mike
> 
> 
> On Tue, 3 Aug 2021 at 23:16, Ben Bolker <bbolker using gmail.com> wrote:
>>
>>     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
>>>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>>>> thierry.onkelinx using inbo.be
>>>> Havenlaan 88 bus 73, 1000 Brussel
>>>> www.inbo.be
>>>>
>>>> ///////////////////////////////////////////////////////////////////////////////////////////
>>>> To call in the statistician after the experiment is done may be no more
>>>> 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
>>>> The combination of some data and an aching desire for an answer does not
>>>> ensure that a reasonable answer can be extracted from a given body of data.
>>>> ~ John Tukey
>>>> ///////////////////////////////////////////////////////////////////////////////////////////
>>>>
>>>> <https://www.inbo.be>
>>>>
>>>>
>>>> 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
>>>>>
>>>>       [[alternative HTML version deleted]]
>>>>
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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
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

-- 
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
Graduate chair, Mathematics & Statistics

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