help for clvJean-Benoit Hardouin

Clustering around latent variables

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

Description

clv clusters variables around latent components. The variables are clustered 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 iterative consolidation procedure can be subsequently run which
allows each variable to be assigned to the latent component it is the most correlated with.

Options

Options concerning the method CLV

nostandardized uses centered variables instead of standardized variables.

kernel defines one or several kernels of variables (variables which are clustered together in an initial step). The first number #k1 indicates that the first #k1 variables are clustered together, the second number #k2 indicates that the following #k2 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.

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

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

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

Options concerning the drawing of the dendrogram

nodendro avoids to display of the dendrogram.

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 maximal number of clusters displayed in the dendrogram (40 by default).

showcount displays the number of variables in each cluster (useful 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 a 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.

Options concerning the others graphs

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

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

addvar allows drawing the items on the graphical representation on the biplot.

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

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

filesave allows saving the graphs in gph files on the default directory or on the directory defined by the dirsave option.

dirsave(string) allows determining the directory to save the graphs (usefull with the filesave option).

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

INSERM UMR 1246-SPHERE "MethodS in Patients-centered outcomes and HEalth ResEarch"

Nantes University - University of Tours

Institute for Research in Health 2 (IRS2), Nantes, France

Email:
jean-benoit.hardouin@univ-nantes.fr

Website AnaQol