Thank you Ben.

On Fri, Jan 6, 2012 at 10:40 AM, Ben Bolker <bbolker@gmail.com> wrote:

> Antonio P. Ramos <ramos.grad.student@...> writes:
>
> >
> > Thanks for your replies. To complement my own question: would  MCMCglmm
> do
> > the job?
>
>   I don't think so, because your models all seem to be expressed in
> terms of R-side correlation, and MCMCglmm (although quite a bit more
> flexible than lmer) does not seem to be as flexible as lme (or GLIMMIX/
> NLMIXED) in specifying R-side correlations.  There aren't a huge
> number of examples of the use of the 'rcov' argument in the MCMCglmm
> course notes, but the examples there are show things like
>
>  rcov=~us(trait):units
>
>  this is for a multi-response model, so the residual variance is
> modeled within units, with 'us' (unstructured) and different variances
> for each trait (so, e.g. ~us(time):units should fit an unstructured
> correlation model)
>
>  rcov=~idh(trait):units allows different variances by trait but
> enforces independence
>
>  The only other option that makes sense here is ~idv (equal variances)
> which basically reduces to unstructured residuals.
>
>    I started working on a 'corClass' definition that follows
> the antecedent model you requested (and, if it worked, could serve
> as a model for implementing other possibilities, such as the
> anisotropic spatial correlation models people have asked about
> previously on the list).  It's not quite working -- I still
> have some confusions about when transformed vs untransformed
> (or in lme's terminology "unconstrained" vs "constrained"
> parameters are used -- but it's a start, and in case I don't
> get around to doing anything further with it I thought I
> would make it available at
> <http://www.math.mcmaster.ca/bolker/R/misc/newcorstruct.R>
>
> >
> > On Mon, Jan 2, 2012 at 8:13 AM, Joshua Wiley <jwiley.psych@...> wrote:
> >
> > > Hi Antonio,
> > >
> > > I am not familiar with antedependence models so no comment there.
> > >
> > > For factor analysis and that genre, I like OpenMx (also see sem and
> > > lavaan).  One thing I like about OpenMx is while it caters to SEM, it
> > > is a general purpose matrix optimizer, and it really is not difficult
> > > to access that power.  So in principal, you can have whatever matrices
> > > you want, roll your own objective function, and away it'll go.
> > >
> > > For BUGS you have a lot of options including: R2OpenBUGS and R2WinBUGS
> > > among others.
> > >
> > > Cheers,
> > >
> > > Josh
> > >
> > > On Mon, Jan 2, 2012 at 12:41 AM, Antonio P. Ramos
> > > <ramos.grad.student@...> wrote:
> > > > Hi all,
> > > >
> > > > I've trying to use R to fit some longitudinal models, mostly via lme
> and
> > > > nlme packages. However, it seems that many standard models are
> lacking,
> > > > such as antedependence models or factor analytic models for
> covariance
> > > > matrices. These models are readily available in SAS. Does an
> recommend
> > > > other packages for the job in R? I don't really care if I am in
> > > frequentist
> > > > or bayesian world as long as I have more modeling flexibility. I
> would
> > > also
> > > > be interested in doing that in WINBUGS/JAGS.
> > > >
> > > > All the best,
> > > >
> > > > Antonio.
> > > >
> > > >        [[alternative HTML version deleted]]
> > > >
> > > > _______________________________________________
> > > > R-sig-mixed-models@... mailing list
> > > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >
> > >
> > >
> > > --
> > > Joshua Wiley
> > > Ph.D. Student, Health Psychology
> > > Programmer Analyst II, Statistical Consulting Group
> > > University of California, Los Angeles
> > > https://joshuawiley.com/
> > >
> >
> >       [[alternative HTML version deleted]]
> >
> >
>
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