[R-sig-ME] Heirarchical Multivariate Modeling?

[Rick Bilonick]

On Tue, 2008-09-23 at 09:53 +1000, Ken Beath wrote:
> On 19/09/2008, at 8:41 AM, Adam D. I. Kramer wrote:
> 
> > Dear colleagues,
> >
> > 	I have an interest in what I would call "heirarchical multivariate
> > modeling." In a sense, I'm interested in extending the mixed model  
> > procedure
> > to an "unpredicted" multivariate case, or an analysis which would be  
> > an
> > extension to princomp() or prcomp() just as lmer() is an extension  
> > to lm().
> >
> > 	My actual interest is in 1. estimating an aggregate PCA based on the
> > factor structures that exist within many individuals, each of which  
> > is based
> > on a different number of observations among the same set of  
> > variables, and
> > 2. testing whether factor structures differ across people (e.g.,  
> > whether
> > prediction improves if I model a random effect for subject). This  
> > can be
> > thought of as adding and testing a random effect to a PCA, or  
> > something
> > similar.
> >
> > 	My first intuition of how to go about this would be to use the glmer
> > procedure, and attempt to model the entire set of variables as being
> > predicted by a random "intercept" for each subject, but before I  
> > undertake
> > this analysis, I thought it might be wise to see if anyone on this  
> > list had
> > any suggestions of a better way to go about this in R (or  
> > suggestions that
> > the above way is inappropriate).
> >
> > 	Also, if anybody could recommend an article or two on the topic (I
> > have not seen any), I would be quite interested.
> 
> It is possible to create multilevel versions of multivariate methods,  
> maybe not PCA, but for factor analysis, yes. The sem package could  
> probably be coerced into fitting them for linear models, otherwise the  
> commercial programs Latent Gold and MPlus are the only solutions. The  
> Mplus site has lots of modelling info.
> 
> Ken


Possibly you could use the Mx program (Neale, http://www.vcu.edu/mx/) to
create a structural equation model. Mx is very flexible and freely
available.

Rick B.