[R-sig-ME] Multivariate mixed models with different outcome, distributions

[Highland Statistics Ltd]

> Today's Topics:
>
>     1. Re: Multivariate mixed models with different outcome
>        distributions (Ben Bolker)
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 14 Dec 2022 13:12:08 -0500
> From: Ben Bolker <bbolker using gmail.com>
> To: Timothy MacKenzie <fswfswt using gmail.com>
> Cc: r-sig-mixed-models <r-sig-mixed-models using r-project.org>
> Subject: Re: [R-sig-ME] Multivariate mixed models with different
> 	outcome distributions
> Message-ID: <a31e3dac-2312-fa56-fc70-447569f83346 using gmail.com>
> Content-Type: text/plain; charset="utf-8"; Format="flowed"
>
>     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
>

Hello,

The R-INLA package can do this as well...and it can also do the beta 
distribution.

Kind regards,

Alain





> 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. Alain F. Zuur
Highland Statistics Ltd.
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