[R-sig-ME] Modelling with uncertain (but not missing) categorical random effect values
Mike Lawson
mrm|500 @end|ng |rom york@@c@uk
Tue Dec 21 09:48:19 CET 2021
Thanks for the quick response - that did the trick.
All the best,
Mike
On Mon, 20 Dec 2021 at 18:10, Ben Bolker <bbolker using gmail.com> wrote:
>
> 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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> >>>
> >>> _______________________________________________
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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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