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

[Timothy MacKenzie]

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
>>
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