[R-sig-ME] Modelling with uncertain (but not missing) categorical random effect values
Mike Lawson
mrm|500 @end|ng |rom york@@c@uk
Mon Dec 20 18:43:34 CET 2021
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
> >>>
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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
>
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