[R-sig-ME] Fitting multi-response mixed effects models with lmer

Tom Houslay hou@|@y @end|ng |rom gm@||@com
Sun Jul 14 12:23:50 CEST 2019

Hi Alejandro,

There are a few packages in R that you can use for multi-response mixed
models, such as MCMCglmm, brms, and wrappers for Stan, as well as ASreml-R
(if you have an ASreml licence). ASreml is the only one of those that uses
REML, while the others are Bayesian.

I have some tutorials on fitting these types of models in MCMCglmm and
ASreml-R here in case they are useful:




> Date: Sat, 13 Jul 2019 18:02:20 +0300
> From: Alejandro Catalina <alecatfel using gmail.com>
> To: r-sig-mixed-models using r-project.org
> Subject: [R-sig-ME] Fitting multi-response mixed effects models with
>         lmer
> Message-ID: <e44b3683-1a88-45f8-8dc8-1c07595c0dd7 using Spark>
> Content-Type: text/plain; charset="utf-8"
> Dear all,
> I found myself trying to fit a multi-response model with lmer the other
> day and today I learned that it is indeed not implemented. Is there anyone
> looking on that direction or does anyone have any pointers or suggestions?
> I guess I can iteratively fit one model for each response but I’m guessing
> that would be much slower. Furthermore, I would need to later combine all
> the models into a single object for my specific requirements. This is the
> issue I opened on lme4’s GitHub:
>         Hi,
> I am trying to solve the following formula with lmer:
> cbind(y.1, y.2, y.3) ~ u + (u | floor_id) + (u | county_id)
> which works fine for standard lm models without the group terms, but it
> fails when I have the mixed effects terms with the following error:
> Error in initializePtr() : updateMu: Size mismatch
> If this is not the right place to post this issue please tell me, I
> appreciate any pointers forward.
> Thank you all,
> Best,
> Alejandro

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