Fitting multi-response mixed effects models with lmer
I thought Alejandro was interested in fitting these responses as *independent* outcomes (since he says below "I can iteratively fit one model for each response but I?m guessing that would be much slower"; I think the loop using refit() every time after the first would be reasonably fast - I certainly don't see a super-easy way to do it faster ...)
On 2019-07-14 4:36 p.m., Ian Dworkin wrote:
Alejandro, Ben B. and I taught some examples of "tricking" lmer for multivariate response models, see here https://mac-theobio.github.io/QMEE/MultivariateMixed.html Cheers Ian On Sun, 14 Jul 2019 at 10:33, jonnations <jonnations at gmail.com> wrote:
Hi Alejandro, This is easy to do in brms, if you?re willing to explore Bayesian options. There is a nice vignette (brms multivariate) that covers this exact thing. Jon On Sun, Jul 14, 2019 at 3:01 AM <r-sig-mixed-models-request at r-project.org> wrote:
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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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