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

[Shige Song]

I don't think SabreR handle nine outcomes (correct me I am wrong). In
theory, you can do this using aML (http://www.applied-ml.com/), now an
open source software. But, with nine outcomes, you will have a lots of
issues with the numerical routines (I only tried five but eventually
gave up).

Best,
Shige

On Fri, Apr 13, 2012 at 11:45 AM,  <Tom_Philippi at nps.gov> wrote:
> 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
>             <m.fairbrother at br                                          To
>             istol.ac.uk>              torvon at gmail.com
>             Sent by:                                                   cc
>             r-sig-mixed-model         r-sig-mixed-models at r-project.org
>             s-bounces at r-proje                                     Subject
>             ct.org                    Re: [R-sig-ME] Multivariate
>                                       Multilevel Mixed-Effects Model
>
>             04/13/2012 12:43
>             PM CET
>
>
>
>
>
>
>
> 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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