Roughly yes (normally distributed) and roughly 300 observations per 
participant. The data are mood data (2 dimensions: valence and arousal. 
Russell grid, if you are familiar with. LINK1 
<http://www.lycaeum.org/mv/circumplex/affect_grid.html>; LINK2 
<https://www2.bc.edu/%7Erusseljm/publications/JPSP1989.pdf>) although i 
think that this is not that important. I guess it is similar to latitude 
and longitude (I like to think of it like that).


Regards,
Marko




On 21.02.2014 11:56, Ludovico Frate wrote:
> Hi Marko,
> My firstquestion: is your response variable normally distributed? I 
> don't understand which type of data you are analyzing.
> Second, How many time observation do you have for the same subject (ID)?
>
> Regards,
> Ludovico
> __
> Ludovico Frate
>
> PhD student (University of Molise - Italy)
> Environmetrics Lab
> http://www.distat.unimol.it/STAT/environmetrica/organico/collaboratori/ludovico-frate-1
> Department of Biosciences and Territory- DiBT
> Università del Molise.
> Contrada Fonte Lappone,
> 86090 - Pesche (IS)
> ITALIA.
> Cel: ++39 3333767557
> Fax: ++39 (0874) 404123
> E-mail ludovico.frate@unimol.it
> ludovicofrate@hotmail.it
>
>
> > Date: Fri, 21 Feb 2014 11:45:52 +0100
> > From: mtoncic@ffri.hr
> > To: r-sig-mixed-models@r-project.org
> > Subject: [R-sig-ME] Help with analyzing multivariate outcomes
> >
> > I have some problems analyzing multivariate dependent variables using a
> > mixed-effect approach.
> > My dataset consists of two-dimensional outcome (like a position on a
> > grid) measured approx 300 times for 100 subjects.
> > For one dimension the model would be something like that:
> >
> > m1<-lmer(y1~time+(time|id),...)
> >
> > but I would like to analyze the y2 as well (not in an independent
> > analysis). I do not wont to decompose the data (PCA or similar) and 
> than
> > conduct the analysis on on the first component (i guess that it would
> > not be appropriate). I somehow want to model the trajectories (basic
> > idea). I know that the problem is not so well explained and fuzzy 
> (it is
> > like this in my head to)
> >
> > Any ideas, suggestions, readings would be greatly appreciated.
> >
> > Thanks a lot,
> >
> > Marko
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


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