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st: Two new modules on SSC


From   Jean-Benoit Hardouin <jean-benoit.hardouin@orscentre.org>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   st: Two new modules on SSC
Date   Sun, 12 Jun 2005 15:01:06 +0200

Thanks to Kit Baum, two new Stata  modules  have been added on SSC.

-Clv-
(Vigneau and Qannari, 2003) cluster variables around latent components. The (numerous [or binary or ordonned]) variables are clustered by searching to minimize at each step the decreasing of the T criterion computed as the sum of the first eigenvalues of the matrices of data of all the clusters. A hierarchical cluster analysis based on this criterion is realized. A consolidation procedure can be run in a second time, which allows assigning each variable to the more correlated latent components. This procedure is close of the Varclus procedure implemented in SAS but CLV is based on an ascending algorithm, Varclus is based on an descending algorithm.

-Backrasch
- realizes a backward procedure on a Rasch model: the items (binary variable) are removed one per one until there is no more item with a bad fit to the Rasch model. The fit of the items is valuated by a first-order statistics (test R1c, R1m or Q1) It is possible to build several sub-scales of items, the second sub-scale is build with the items unselected in the first sub-scales, the third one with the items unselected in the two first sub-scales, and so on... By default, the parameters of the Rasch model are estimated by conditional maximum likelihood (CML), but it is possible to estimate them by marginal maximum likelihood (MML) or generalized estimating equations (GEE).

More informations about these two modules on my website : http://anaqol.free.fr
Jean-Benoit Hardouin

-- 
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Jean-Benoit Hardouin
Biostatistician
Regional Health Observatory - Orléans - France
Email : jean-benoit.hardouin@orscentre.org
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