[R-sig-ME] Multivariate Multilevel Mixed-Effects Model

[Tom_Philippi at nps.gov]

Eiko--

If you have a strong understanding of mixed models and are asking about
tools for fitting mixed models with multivariate responses, Rob Crouchley's
sabreR package may be the tool you need.  There's also a book: Berridge &
Crouchley 2011 Multivariate generalized linear mixed models using R, which
uses mostly social science examples, and has a companion website at:
http://sabre.lancs.ac.uk/.  From my experience, the R coding for this
approach is much less of a hurdle than actually understanding the models
and techniques and applying the methods to my questions, but that's for
ecological applications, and your mileage may vary if you have a better
background.

Tom 2

-------------------------------------------
Tom Philippi, Ph.D.
Quantitative Ecologist
Inventory and Monitoring Program
National Park Service
c/o Cabrillo National Monument
1800 Cabrillo Memorial Dr
San Diego, CA 92106
(619) 523-4576
Tom_philippi at NPS.gov
http://science.nature.nps.gov/im/monitor
-------------------------------------------



                                                                           
             Malcolm                                                       
             Fairbrother                                                   
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             istol.ac.uk>              torvon at gmail.com                    
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             04/13/2012 12:43                                              
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Dear Eiko,

You can certainly conduct this analysis in R, though some aspects of what
you want to do are not simple. You might do best to start with some general
reading/self-study re. mixed/multilevel models (natural scientists and
statisticians tend to refer to "mixed" models, social scientists to
"multilevel" or "random effects" or "hierarchical linear" models).
Alternatively, if your uncertainty is less about such models generally than
about R as a specific tool/software environment, the suggestions below may
get you started, and http://glmm.wikidot.com/ may be a good resource as
well (even though it's aimed at biological scientists).

R packages that may be useful to you are lme4, MCMCglmm, and nlme--the
latter because it would allow you to check for temporal autocorrelation,
which may be a concern particularly if your measurement occasions are close
together in time. There are helpful coursenotes and worked examples for the
first two packages on the web, and for nlme the best resource to start with
would probably be the book "Mixed Effects Models in S and S-Plus", by
Pinheiro and Bates.

There are ways of modelling multiple outcomes simultaneously (see the
MCMCglmm documentation on Multi-response models at
http://cran.md.tsukuba.ac.jp/web/packages/MCMCglmm/vignettes/CourseNotes.pdf
), but nine is a lot. Perhaps an exploratory factor analysis could help you
reduce the nine items to a smaller and more manageable number? There are
also techniques common in psychology that may be appropriate for what you
want, though I am less familiar with those (see e.g.,
http://www.jstatsoft.org/v20/a02/paper).

Hope that's useful.
- Malcolm


> Date: Fri, 13 Apr 2012 11:32:14 +0200
> From: Eiko Fried <torvon at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Multivariate Multilevel Mixed-Effects Model
>
> Hello.
>
> I have a problem that I have not been able to solve within the last
months.
> Maybe R offers options to tackle this.
>
> My dataset:
>
> * N = 1000, 5 measurement points
> * 9 categorical dependent variables (depressive symptoms, scored 0, 1, 2
or
> 3) that are intercorrelated
> * 7 time-varying covariates that are dichotomous life events (yes/no) at
> each measurement point (people can have multiple life events, therefor
this
> is not just one categorical with 0=no life event, 1=life event1, 2=life
> event2 etc.)
> * A bunch of time-invariant baseline predictors (e.g. neuroticism, early
> family environment, genotype, gender, family history of depression ... ),
> measured only once at the first measurement point.
>
> My core question is whether different life events lead to different
> depressive symptom profiles (controlling for baseline variables).
>
> I can confirm this hypothesis in nine univariate repeated measurement
> mixed-models (some life events are predictors for some symptoms but not
> others, and vice versa), but that invites the problem of (1) not being
able
> to control for multiple models, and (2) ignoring the correlated structure
> of my categorical response variables.
>
> So I'm looking for multivariate multilevel models currently to do all of
> this in one analysis.
> Could this be achieved with R? The more palpable the recommendations the
> better, I have little experience with R.
>
> Thank you
> Eiko

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