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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] Random intercept/slopes on two correlated outcomes
From:       Ramon Diaz-Uriarte <rdiaz02 () gmail ! com>
Date:       2017-02-06 9:39:36
Message-ID: 874m078xpj.fsf () gmail ! com
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Hi Theodore,

If you want a reference for an example with lmer, Faraway's book,
"Extending the Linear Model with R: Generalized Linear, Mixed Effects and
Nonparametric Regression Models, 2nd ed.", on section 11.3 contains an
example on using lmer with multiple responses. But this is basically the
answer that Thierry gave you (and Thierry's answer is adapted to your own
problem).

Best,


R.



On Wed, 01-02-2017, at 15:44, Houslay, Tom <T.Houslay@exeter.ac.uk> wrote:
> Hi Theodore, just in case it's of interest, there is another option - ASReml \
> (commercial software from VSNi) can fit the type of model you are looking for, with \
> more complex variance structures (and it has an R interface). This thread from the \
> forum might be informative in terms of how to set up such a model: 
> http://www.vsni.co.uk/forum/viewtopic.php?t=1202
> 
> Cheers (and good luck!)
> 
> Tom
> 
> ________________________________
> From: R-sig-mixed-models <r-sig-mixed-models-bounces@r-project.org> on behalf of \
>                 r-sig-mixed-models-request@r-project.org \
>                 <r-sig-mixed-models-request@r-project.org>
> Sent: 01 February 2017 09:40
> To: r-sig-mixed-models@r-project.org
> Subject: R-sig-mixed-models Digest, Vol 122, Issue 1
> 
> 
> 
> Date: Wed, 01 Feb 2017 10:37:44 +0200
> From: Theodore Lytras <thlytras@gmail.com>
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Random intercept/slopes on two correlated outcomes
> Message-ID: <3197712.2r4pMF0lQW@equinox2>
> Content-Type: text/plain; charset="us-ascii"
> 
> Hi all,
> 
> I have repeated measures on individuals, and I'm fitting two LMMs with random
> intercepts and slopes per participant, on two outcomes (Y1, Y2) as follows:
> 
> library(lme4)
> m1 <- lmer(Y1 ~ age + X + (age | id), data=dat)
> m2 <- lmer(Y2 ~ age + X + (age | id), data=dat)
> 
> Fixed covariates for the two outcomes are the same, and id = participant ID.
> 
> However, my two continuous outcomes Y1 and Y2 are correlated (highly), thus I
> would like to jointly model them (including estimating their correlation).
> 
> What is the appropriate way to do so in this case? Can lme4 do it, or do I
> have to resort to MCMCglmm or JAGS? Could someone point me in the right
> direction (for either lme4, MCMCglmm or JAGS), including any helpful papers,
> guides, etc ??
> 
> Thank you,
> 
> Theodore Lytras
> 
> Epidemiologist, PhD student
> Hellenic Centre for Disease Control and Prevention
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


--
Ramon Diaz-Uriarte
Department of Biochemistry, Lab B-25
Facultad de Medicina
Universidad Autónoma de Madrid
Arzobispo Morcillo, 4
28029 Madrid
Spain

Phone: +34-91-497-2412

Email: rdiaz02@gmail.com
       ramon.diaz@iib.uam.es

http://ligarto.org/rdiaz

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