[R-sig-ME] Multivariate mixed models with different outcome distributions
Timothy MacKenzie
|@w|@wt @end|ng |rom gm@||@com
Thu Dec 15 18:20:42 CET 2022
Dear Ben,
Thank you for your confirmation. There are two things that I want to
better understand.
First, brms::brm() etc. require wide-format data. For my data (below
see long-format data from a single student), wide-formatting it will
create 256 columns for each subject (attached)! Is using brm() etc.
really practical here?
Second, nlme::lme() allows modeling the residuals. If I model the
residuals from my responses (CAL_type) on their current scale (some
proportions, some normal ones) with lme() and find a relatively good
fitting model, would that be a second best solution?
Thanks,
Tim M
LONG-FORMAT="
Class Person Task_order Task_type Time Score CAL_type Mot_ex Mot_inr
Mot_ide Mot_int Mot_amot Eng_leng_txt Eng_time_on_tsk
1 1 S-C simple 1 5 com_mult 4 2
3 3 1 300 20
1 1 S-C simple 1 .3 com_dc/t 4 2
3 3 1 300 20
1 1 S-C simple 1 2 com_cn/t 4 2
3 3 1 300 20
1 1 S-C simple 1 3 com_cn/c 4 2
3 3 1 300 20
1 1 S-C simple 1 .4 ac 4 2
3 3 1 300 20
1 1 S-C simple 1 1 lex_vo 4 2
3 3 1 300 20
1 1 S-C simple 1 5 lex_fr 4 2
3 3 1 300 20
1 1 S-C complex 2 2 com_mult 3 4
2 1 2 200 25
1 1 S-C complex 2 .3 com_dc/t 3 4
2 1 2 200 25
1 1 S-C complex 2 4 com_cn/t 3 4
2 1 2 200 25
1 1 S-C complex 2 3 com_cn/c 3 4
2 1 2 200 25
1 1 S-C complex 2 .4 ac 3 4
2 1 2 200 25
1 1 S-C complex 2 4 lex_vo 3 4
2 1 2 200 25
1 1 S-C complex 2 5 lex_fr 3 4
2 1 2 200 25
1 1 S-C simple 3 4 com_mult 5 2
3 4 3 100 10
1 1 S-C simple 3 .2 com_dc/t 5 2
3 4 3 100 10
1 1 S-C simple 3 3 com_cn/t 5 2
3 4 3 100 10
1 1 S-C simple 3 3 com_cn/c 5 2
3 4 3 100 10
1 1 S-C simple 3 .6 ac 5 2
3 4 3 100 10
1 1 S-C simple 3 6 lex_vo 5 2
3 4 3 100 10
1 1 S-C simple 3 6 lex_fr 5 2
3 4 3 100 10
1 1 S-C complex 4 1 com_mult 1 3
2 5 4 400 35
1 1 S-C complex 4 .1 com_dc/t 1 3
2 5 4 400 35
1 1 S-C complex 4 1 com_cn/t 1 3
2 5 4 400 35
1 1 S-C complex 4 3 com_cn/c 1 3
2 5 4 400 35
1 1 S-C complex 4 .3 ac 1 3
2 5 4 400 35
1 1 S-C complex 4 5 lex_vo 1 3
2 5 4 400 35
1 1 S-C complex 4 5 lex_fr 1 3
2 5 4 400 35
"
On Wed, Dec 14, 2022 at 12:12 PM Ben Bolker <bbolker using gmail.com> wrote:
>
> I didn't realize that brms does multi-type models, but apparently it
> does:
>
> https://cran.r-project.org/web/packages/brms/vignettes/brms_multivariate.html
>
> ... so yes, I would go for brms in this case.
>
> cheers
> Ben
>
>
> On 2022-12-14 12:09 p.m., Timothy MacKenzie wrote:
> > Dear Ben,
> >
> > Thank you for the hint. Regarding MCMCglmm, I couldn't find "beta" in
> > the family of allowable distributions in the package. Did you have a
> > specific set of distribution families in mind to handle normal and
> > beta responses simultaneously?
> >
> > Also, I noticed the brms package apparently can handle different
> > response distributions, is there a reason, in your expert opinion, to
> > opt for MCMCglmm?
> >
> > Many thanks,
> > Tim M
> >
> > On Tue, Dec 13, 2022 at 9:28 PM Ben Bolker <bbolker using gmail.com> wrote:
> >>
> >> MCMCglmm can handle this case
> >>
> >> On Tue, Dec 13, 2022, 10:14 PM Timothy MacKenzie <fswfswt using gmail.com> wrote:
> >>>
> >>> Hello Colleagues,
> >>>
> >>> I have a multivariate data structure (below) where the dependent
> >>> variables (DV) seem to have different distributions.
> >>>
> >>> For instance, *ac* is measured in proportions and perhaps
> >>> beta-distributed, but *fl* and *le* may be normally distributed.
> >>>
> >>> Would it make methodological sense to fit such DVs in a multivariate
> >>> mixed model given that they are theoretically related but practically
> >>> measured on different scales?
> >>>
> >>> Any resources to provide mixed model strategies in such a situation?
> >>>
> >>> Many thanks for your help,
> >>> Tim M
> >>>
> >>> Score ~ DV + (1 | subj_id) ## Would this make sense?
> >>>
> >>> # Data structure:
> >>> subj_id DV Score
> >>> 1 ac .5
> >>> 1 fl 23.1
> >>> 1 le 1.4
> >>> 2 ac .7
> >>> 2 fl 19.6
> >>> 2 le 2.1
> >>>
> >>> _______________________________________________
> >>> 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
> (Acting) Graduate chair, Mathematics & Statistics
> > E-mail is sent at my convenience; I don't expect replies outside of
> working hours.
More information about the R-sig-mixed-models
mailing list