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Re: st: Latent class estimation


From   "Eva Poen" <eva.poen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Latent class estimation
Date   Thu, 6 Dec 2007 16:58:09 +0000

Thanks a lot to everyone who replied. I will look into the software
and literature mentioned. The 140+ pages gllamm manual scares me a
little, I have to say...

Thanks again for all recommendations.
Regards,
Eva Poen

2007/12/5, Laura Gibbons <gibbonsl@u.washington.edu>:
> And the R package is -ltm-.  -Laura
>
> On Wed, 5 Dec 2007, Christian Deindl wrote:
>
> > to make the list complete: there is also a possibility in SAS to estimate LCA
> > models: PROC LCA.
> > you can download it via the methodology center of pennstate university
> > (http://methcenter.psu.edu/)
> >
> > it is really nice and easy to handle.
> >
> >
> > christian
> >
> > On Wed, 5 Dec 2007 12:45:26 -0500
> > "Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> wrote:
> >> Phil Schumm wrote:
> >>
> >>>>> I would like to offer an alternative opinion.  RE references that are
> >>
> >> accessible, Bartholomew's "Latent Variable Models and Factor  Analysis"
> >> (1987, Charles Griffin & Co.) offers a good theoretical  introduction, and
> >> Skrondal and Rabe-Hesketh's "Generalized Latent  Variable Modeling" (2005,
> >> Chapman & Hall) is superb in terms of both  its theoretical and applied
> >> presentation.  The latter should be read  in any case without question.<<<
> >>
> >> Bartholomew's book had a second edition (co-authored with Martin Knott).
> >>
> >>
> >> There's also a Stata-specific book Multilevel and Longitudinal Modeling
> >> Using Stata by Skrondal and Rabe-Hesketh. I would recommend seeing
> >> http://www.gllamm.org for example code, the GLLAMM manual, etc.
> >>
> >>>>> The program -gllamm- (type -ssc install gllamm-) is capable of
> >> estimating latent class models in a very flexible manner, and the  manual
> >> available with the program (as well as the book cited above)  offer several
> >> worked examples.  It can be slow for datasets with a  large number of
> >> unique covariate patterns, but this has to be  evaluated relative to the
> >> time required to learn how to use another  program and to move your data
> >> and results back and forth.  It's  certainly an excellent place to start
> >> and to run some preliminary  analyses; you can always then move to another
> >> piece of software if  speed becomes a problem.<<<
> >>
> >> Yeah, LatentGOLD, LEM, and Mplus would all be the ones I'd figure as
> >> places to go if GLLAMM doesn't work out, or else coding things up in
> >> software like winBUGS. There might be an R package of some sort that
> >> does this; there are so many I can't possibly keep up with them all.
> >>
> >> IMO it is wise to do the analysis in multiple software packages---these
> >> models are tricky. Mixture regression is particularly known for having
> >> multiple optima problems and run the serious risk of capitalizing on
> >> chance, especially in "exploratory" mode when there aren't covariates
> >> predicting class membership or other things to constrain the model.
> >> Jay
> >>
> >> *
> >> *   For searches and help try:
> >> *   http://www.stata.com/support/faqs/res/findit.html
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> >> *   http://www.ats.ucla.edu/stat/stata/
> >
> > __________________
> > Christian Deindl
> > Universität Zürich
> > Soziologisches Institut
> > Andreasstr. 15
> > CH - 8050 Zürich
> > Tel: 044/635 23 46
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/support/faqs/res/findit.html
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> >
>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Laura E. Gibbons, PhD
> General Internal Medicine, University of Washington
> Box 359780
> Harborview Medical Center
> 325 Ninth Avenue, Seattle, WA  98104
>
> phone: 206-744-1842   fax: 206-744-9917
> Office address: 401 Broadway, Suite 5122
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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