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help for clv                                               Jean-Benoit Hardouin
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Clustering of variables around latent components 

        clv [varlist] [if exp] [in range] [weight] [, nostandardized bar
              consolidation(#) nodendro savedendro(filename[,replace])
              cutnumber(#) showcount textsize(string) deltaT horizontal
              abbrev(#) title(string) caption(string) kernel(numlist)
              method(keyword) nobiplot addvar genlv(string) replace std
              dim(string)]

Description

    clv clusters variables around latent components. The variables are
        clustered stepwise by seeking to minimize at each step the decrease
        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 performed. A consolidation
        procedure can be run subsequently which allows each variable to be
        assigned to the latent component it is the most correlated with.

Options

    nostandardized uses centered variables instead of standardized variables.

    bar displays a chart of the decrease in the T criterion at each step.

    consolidation performs a consolidation procedure with the obtained
        partition into the specified number of clusters (by default, no
        consolidation procedure is performed).

    nodendro suppresses the display of the dendogram.

    savedendro saves the dendrogram in the file defined by this option. If
        this file already exists, it is possible to replace it with the
        replace option.

    cutnumber defines the number of clusters presented in the dendrogram (40
        by default).

    showcount displays the number of variables in each cluster (usefull with
        the cutnumber option).

    textsize defines the size of the labels of the variables on the
        dendrogram (see textsizestyle).

    deltaT uses the variation of the T criterion as height variable for the
        dendrogram.

    horizontal displays an horizontal (instead vertical) dendrogram.

    abbrev defines the length of the variables labels on the dendrogram (15
        characters by default).

    title defines the title of the dendrogram.

    caption defines the caption of the axis of the dendrogram which indicates
        the names of the variables.

    kernel defines one or several kernels of variables (variables which are
        clustered together in an initial step). The first number indicates
        that the first variables are clustered together, the second number
        indicates that the following variables are clustered together...

    method indicates the method to cluster the variables among classical (by
        default) for the method described by Vigneau and Qannari, polychoric
        for a use of the matrix of polychoric coefficients of correlation
        (instead of Pearson coefficients of correlation), v2 for a modified
        algorithm wich search to minimize the maximum second eigenvalue among
        the clusters of 2 variables and more, polychoricv2 which correspond
        to the v2 option with the matrix of polychoric coefficients of
        correlation, and centroid which is defined by Vigneau and Qannari as
        an adaptation of CLV when the sign of the correlation coefficients
        between the variables is important.

    nobiplot avoids to display a biplot of the latent variables with the
        consolidation option.

    genlv saves the latent variables in new variables with the string as
        prefix (followed by a number). This option must be used in
        conjonction with the consolidation option.

    replace allows replacing the variables creates with the genlv option if
        they already exist.

    std allows standardizing the latent variables for the graphical
        representation on the biplot.

    dim(string) allows choosing the axes represented on the biplot.

    If no varlist is indicated, the procedure uses the varlist from the last
        clv procedure, but does not perform the hierarchical cluster
        analysis.

Notes

    The classifications around latent variables (CLV) is defined by its
        authors (Vigneau and Qannari, 2003) only for continuous variables.
        Results with binary or ordinal variables must be interpreted with
        precautions.

    Only fweights are allowed. The biplots are disabled if weights are used.

    In this procedure, all the individuals with at least one missing value
        are omitted.

    With the polychoric and polychoricv2 methods, the nostandardized option
        is disabled.

    This module uses the following modules downloadable on SSC: ssc describe
        polychoric, ssc describe biplotvlab and ssc describe genscore

Example

        
    . clv var1-var15 /*performs the HCA procedure*/

        
    . clv var1-var15, cons(6) bar nodendro meth(centroid) /* performs the HCA
        procedure based on the centroid method followed by a consolidation
        procedure with 6 clusters*/

        
    . clv, cons(3) addvar /*performs only the consolidation procedure with 3
        clusters, based on the preceeding HCA procedure*/

Aknowledgements

    The author thanks Ronan Conroy for all the propositions of improvements.

Reference

    Vigneau E. and Qannari E. M. Clustering of variables around latent
        components. Communications in Statistics - Simulation and
        Computation. 32(4): 1131-1150, 2003.

Author

    Jean-Benoit Hardouin, PhD, assistant professor
    EA 4275 SPHERE "Team of Biostatistics, Clinical Research and Subjective
        Measures in Health Sciences"
    University of Nantes - Faculty of Pharmaceutical Sciences
    1, rue Gaston Veil - BP 53508
    44035 Nantes Cedex 1 - FRANCE
    Email:  jean-benoit.hardouin@univ-nantes.fr