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917 lines
33 KiB
Plaintext
917 lines
33 KiB
Plaintext
8 months ago
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*! Version 2.14 20May2010
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*! Jean-Benoit Hardouin
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************************************************************************************************************
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* Stata program : clv
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* Clustering of variables around latent variables
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* Version 2.14 : May 20th, 2010 /*dim and std options for biplots*/
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*
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* Historic
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* Version 1 (2005-06-11): Jean-Benoit Hardouin
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* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/
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* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/
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* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/
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* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/
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* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/
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* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/
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* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/
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* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/
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* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/
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* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/
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* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/
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* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/
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* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/
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* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/
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* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/
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* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/
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* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/
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* Version 2.13 (2010-05-12): Jean-Benoit Hardouin /*corrections of bugs in KERNEL option and with METHOD(centroid)*/
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* Version 2.14 (2010-05-20): Jean-Benoit Hardouin /*DIM and STD options for biplots*/
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*
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* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences
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* Department of Biostatistics - France
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* jean-benoit.hardouin@univ-nantes.fr
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*
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* News about this program : http://anaqol.free.fr
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* FreeIRT Project : http://freeirt.free.fr
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*
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* Copyright 2005-2006, 2010 Jean-Benoit Hardouin
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*
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************************************************************************************************************
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program define clv,rclass
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version 9.0
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syntax [varlist(default=none)] [if] [in] [fweight] [, CUTnumber(int 40) bar CONSolidation(int 0) noDENdro SAVEDendro(string) noSTANDardized deltaT HORizontal SHOWcount ABBrev(int 14) TITle(string) CAPtion(string) KERnel(numlist) METHod(string) noBIPlot ADDvar genlv(string) replace TEXTSize(string) std dim(string)]
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preserve
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tempfile clvfile
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tempvar id
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gen `id'=_n
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qui save `clvfile',replace
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local matsize=c(matsize)
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local none=0
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if "`varlist'"=="" {
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capture confirm matrix r(vp)
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if _rc==0 {
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capture confirm matrix r(matclus)
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if _rc ==0 {
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local none=1
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}
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}
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if `none'==0 {
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di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}"
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error 198
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exit
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}
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}
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tempname matclus vp indexes
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/*********TESTS**********/
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if `none'==1 {
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matrix `vp'=r(vp)
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matrix `matclus'=r(matclus)
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local varlist `r(varlist)'
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tokenize `varlist'
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local nbitems=rowsof(`matclus')
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if "`method'"!="" {
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di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted."
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}
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local method `r(method)'
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local kernel `r(kernel)'
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}
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if "`method'"=="" {
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local method classical
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}
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if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" {
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di in green "Initial variables are used with the {hi:polychoric} methods"
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di in green "But the procedure is based on the matrix of the polychoric correlations"
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di
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}
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if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" {
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di in red "The {hi:method} `method' is unknown"
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error 198
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exit
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}
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tokenize `varlist'
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local nbitems : word count `varlist'
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marksample touse
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qui keep if `touse'
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local mat=max(`matsize',`=`nbitems'*2')
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qui set matsize `mat'
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if `nbitems'<3&`none'!=1 {
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di in red "You need at least 3 variables"
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error 198
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exit
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}
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/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/
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forvalues i=1/`nbitems'{
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local label`i':variable label ``i''
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if "`label`i''"=="" {
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local label`i' ``i''
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}
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if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
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qui su ``i'' [`weight'`exp']
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local mean=r(mean)
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if "`standardized'"=="" {
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local sd=r(sd)
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}
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else {
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local sd=1
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}
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qui replace ``i''=(``i''-`mean')/`sd'
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}
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}
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tempfile clvfiletmp
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qui save `clvfiletmp',replace
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qui su `1' [`weight'`exp']
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local nbind=r(sum_w)
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local cons=`consolidation'
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/*COMPUTES THE TOTAL VARIANCE*/
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if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
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local totvar=0
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forvalues i=1/`nbitems' {
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qui su ``i'' [`weight'`exp']
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local totvar=`totvar'+`r(Var)'
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}
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}
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else {
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local totvar `nbitems'
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}
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local nbkerk=0
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local nbkerg=0
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/***** DEFINES THE KERNEL IF NECESSARY ********/
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if "`kernel'"!="" {
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local nbkerg:word count `kernel'
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local fin0=0
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forvalues i=1/`nbkerg' {
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local nbi`i':word `i' of `kernel'
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local nbkerk=`nbkerk'+`nbi`i''
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local deb`i'=`fin`=`i'-1''+1
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local fin`i'=`deb`i''+`nbi`i''-1
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local list`i'
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forvalues j=`deb`i''/`fin`i'' {
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local list`i' `list`i'' ``j''
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}
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}
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tempname kerclus
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matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0)
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local ligne=1
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forvalues g=1/`nbkerg' {
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matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
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matrix `kerclus'[`ligne',2]=`deb`g''
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matrix `kerclus'[`ligne',3]=`deb`g''+1
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local clus`g'=`nbitems'+`ligne'
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local ligne=`ligne'+1
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if `nbi`g''>2 {
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forvalues i=2/`=`nbi`g''-1' {
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matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
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matrix `kerclus'[`ligne',2]=`deb`g''+`i'
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matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1
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local clus`g'=`nbitems'+`ligne'
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local ligne=`ligne'+1
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}
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}
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local eigen2=0
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}
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}
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if `nbitems'<`nbkerk' {
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di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}"
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error 198
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exit
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}
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/*******DISPLAY THE FIRST RESULTS *******/
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di
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di in green "{hline 32}"
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di in green "TOTAL VARIANCE: " in ye %16.5f `totvar'
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di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind'
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di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'"
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di in green "{hline 32}"
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di
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if "`kernel'"!="" {
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forvalues i=1/`nbkerg' {
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di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''"
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di
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}
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}
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else {
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local nbkerk=0
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local nbkerg=0
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}
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/******** CLASSIFICATION PROCEDURE*******/
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tempname Ev
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if `none'!=1 {
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matrix `matclus'=J(`nbitems',`nbitems',0)
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matrix `vp'=J(`=2*`nbitems'-1',12,0)
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matrix `indexes'=J(`nbitems',8,0)
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forvalues i=1/`nbitems' {
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matrix `matclus'[`i',1]=`i'
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if "`method'"!="polychoric"&"`method'"!="polychoric" {
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qui su ``i'' [`weight'`exp']
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matrix `vp'[`i',10]=r(Var)
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}
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else {
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matrix `vp'[`i',10]=1
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}
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matrix `vp'[`i',1]=`i'
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matrix `vp'[`i',2]=`nbitems'
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matrix `vp'[`i',8]=`totvar'
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matrix `vp'[`i',9]=100
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}
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matrix `vp'[`nbitems',5]=`nbitems'
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if "`method'"=="centroid" {
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local crit G
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di in green "{hline 101}"
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di in green _col(93) "2nd order"
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di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative"
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di in green "Step" _col(8) "clusters" _col(20) "Child 1" _col(33) "Child 2" _col(46) "Parent" _col(53) "`crit' value" _col(61) "variation" _col(72) "Variance" _col(81) "Variation" _col(93) "Variation"
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di in green "{hline 101}"
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}
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else {
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local crit T
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di in green "{hline 111}"
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if "`method'"=="v2"|"`method'"=="polychoricv2" {
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di in green _col(84) "Maximal" _col(103) "2nd order"
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}
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else {
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di in green _col(84) "Current" _col(103) "2nd order"
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}
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di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative"
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di in green "Step" _col(8) "clusters" _col(20) "Child 1" _col(33) "Child 2" _col(46) "Parent" _col(53) "`crit' value" _col(61) "variation" _col(72) "Variance" _col(81) "Eigenvalue" _col(92) "Variation" _col(103) "Variation"
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di in green "{hline 111}"
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}
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tempname threshold
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matrix `threshold'=J(`nbitems',3,0)
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forvalues i=1/`=`nbitems'-1' {
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local clus=`nbitems'+`i'
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local minegenval=999999
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local minegenval2=999999
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forvalues k=1/`=`clus'-1' {
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local list`k'
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local numlist`k'
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forvalues j=1/`clus' {
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if (`matclus'[`j',`i']==`k') {
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local list`k' `list`k'' ``j''
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local numlist`k' `numlist`k'' `j'
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}
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}
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}
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if `clus'>`nbitems'+`nbkerk'-`nbkerg' {
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if "`method'"=="centroid" {
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tempname centrj centrk diffjk
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}
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forvalues j=1/`clus' {
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local nblistj:word count `list`j''
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forvalues k=`=`j'+1'/`clus' {
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local nblistk:word count `list`k''
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if `nblistj'!=0&`nblistk'!=0 {
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if "`method'"=="centroid" {
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qui genscore `list`j'',score(`centrj') mean
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qui su `centrj' [`weight'`exp']
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local Varj=r(Var)
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qui genscore `list`k'',score(`centrk') mean
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qui su `centrk' [`weight'`exp']
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local Vark=r(Var)
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qui gen `diffjk'=`centrk'-`centrj'
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qui su `diffjk' [`weight'`exp']
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local Varjk=r(Var)
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drop `centrj' `centrk' `diffjk'
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local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk'
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if `ev'<`minegenval' {
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local minegenval=`ev'
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local minj `j'
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local mink `k'
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local eigen=0
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local eigen2=0
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}
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}
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else {
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if "`method'"=="classical"|"`method'"=="v2" {
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qui pca `list`j'' `list`k'' [`weight'`exp'] ,cov
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matrix `Ev'=e(Ev)
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}
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||
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else if "`method'"=="polychoric"|"`method'"=="polychoricv2" {
|
||
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qui polychoricpca `list`j'' `list`k'' [`weight'`exp']
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matrix `Ev'=r(eigenvalues)
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}
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|
|
local lambda1=`Ev'[1,1]
|
||
|
|
local lambda2=`Ev'[1,2]
|
||
|
|
local ev=`vp'[`j',10]+`vp'[`k',10]-`lambda1'
|
||
|
|
local ev2=max(`vp'[`j',11],`vp'[`k',11],`lambda2')
|
||
|
|
if ("`method'"=="v2"|"`method'"=="polychoricv2")&`ev'<`minegenval' {
|
||
|
|
local minegenval=`ev'
|
||
|
|
local eigen=`lambda1'
|
||
|
|
local minj `j'
|
||
|
|
local mink `k'
|
||
|
|
local eigen2=`lambda2'
|
||
|
|
}
|
||
|
|
else if ("`method'"=="classical"|"`method'"=="polychoric")&`ev2'<`minegenval2' {
|
||
|
|
local minegenval=`ev'
|
||
|
|
local minegenval2=`ev2'
|
||
|
|
local eigen=`lambda1'
|
||
|
|
local minj `j'
|
||
|
|
local mink `k'
|
||
|
|
local eigen2=`ev2'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local ligne=`clus'-`nbitems'
|
||
|
|
local j=`kerclus'[`ligne',2]
|
||
|
|
local k=`kerclus'[`ligne',3]
|
||
|
|
if "`method'"!="centroid" {
|
||
|
|
if "`method'"=="classical"|"`method'"=="v2" {
|
||
|
|
qui pca `list`j'' `list`k'' [`weight'`exp'],cov
|
||
|
|
matrix `Ev'=e(Ev)
|
||
|
|
}
|
||
|
|
else if "`method'"=="polychoric"|"`method'"=="polychoricv2"{
|
||
|
|
qui polychoricpca `list`j'' `list`k'' [`weight'`exp']
|
||
|
|
matrix `Ev'=r(eigenvalues)
|
||
|
|
}
|
||
|
|
local lambda1=`Ev'[1,1]
|
||
|
|
local lambda2=`Ev'[1,2]
|
||
|
|
local ev=`vp'[`j',10]+`vp'[`k',10]-`lambda1'
|
||
|
|
local minegenval=`ev'
|
||
|
|
local eigen=`lambda1'
|
||
|
|
local minj `j'
|
||
|
|
local mink `k'
|
||
|
|
local eigen2=`lambda2'
|
||
|
|
}
|
||
|
|
else if "`method'"=="centroid" {
|
||
|
|
local nblistj:word count `list`j''
|
||
|
|
local nblistk:word count `list`k''
|
||
|
|
tempname v1 v2 v12
|
||
|
|
qui genscore `list`j'',score(`v1') mean
|
||
|
|
qui genscore `list`k'',score(`v2') mean
|
||
|
|
qui gen `v12'=`v1'-`v2'
|
||
|
|
qui su `v12' [`weight'`exp']
|
||
|
|
local varj=r(Var)
|
||
|
|
local minegenval=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`varj'
|
||
|
|
local minj `j'
|
||
|
|
local mink `k'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if `minj'<=`nbitems' {
|
||
|
|
local nomj=abbrev("``minj''",14)
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local nomj `minj'
|
||
|
|
}
|
||
|
|
if `mink'<=`nbitems' {
|
||
|
|
local nomk=abbrev("``mink''",14)
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local nomk `mink'
|
||
|
|
}
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
matrix `matclus'[`j',`=`i'+1']=`matclus'[`j',`i']
|
||
|
|
}
|
||
|
|
matrix `vp'[`clus',1]=`nbitems'+`i' /*PARENT*/
|
||
|
|
matrix `vp'[`clus',2]=`=`nbitems'-`i'' /*NUMBER OF CLUSTERS*/
|
||
|
|
matrix `vp'[`clus',3]=`minj' /*CHILD 1*/
|
||
|
|
matrix `vp'[`clus',4]=`mink' /*CHILD 2*/
|
||
|
|
matrix `vp'[`clus',6]=`minegenval' /*VARIATION OF THE T or G CRITERION*/
|
||
|
|
matrix `vp'[`clus',5]=`vp'[`=`clus'-1',5]-`vp'[`clus',6] /*T or G CRITERION*/
|
||
|
|
matrix `vp'[`clus',7]=(`vp'[`clus',6]-`vp'[`=`clus'-1',6])/`vp'[`=`clus'-1',6] /*RELATIVE VARIATION OF THE T OR G CRITERION*/
|
||
|
|
matrix `vp'[`clus',8]=`vp'[`=`clus'-1',8]-`minegenval' /*EXPLAINED VARIANCE*/
|
||
|
|
matrix `vp'[`clus',9]=`vp'[`clus',8]/`totvar'*100 /*% OF EXPLAINED VARIANCE*/
|
||
|
|
if "`method'"!="centroid" {
|
||
|
|
matrix `vp'[`clus',10]=`eigen' /*FIRST EIGEN VALUE OF THE NEW CLUSTER*/
|
||
|
|
matrix `vp'[`clus',11]=`eigen2' /*SECOND EIGEN VALUE OF THE NEW CLUSTER*/
|
||
|
|
}
|
||
|
|
if `vp'[`=`clus'-1',7]!=0 {
|
||
|
|
matrix `vp'[`clus',12]=(`vp'[`clus',7]-`vp'[`=`clus'-1',7])/abs(`vp'[`=`clus'-1',7]) /*2ND ORDER RELATIVE VARIATION OF THE T or G CRITERION*/
|
||
|
|
}
|
||
|
|
matrix `indexes'[`i',1]=`i' /*PARENT*/
|
||
|
|
matrix `indexes'[`i',2]=`nbitems'-`i' /*NUMBER OF CLUSTERS*/
|
||
|
|
matrix `indexes'[`i',3]=`minegenval' /*VARIATION OF THE T or G CRITERION*/
|
||
|
|
matrix `indexes'[`i',4]=`vp'[`clus',7] /*RELATIVE VARIATION OF THE T OR G CRITERION*/
|
||
|
|
matrix `indexes'[`i',5]=max(`eigen2',`indexes'[`=`i'-1',5]) /*MAXIMUM SECOND EIGENVALUE*/
|
||
|
|
matrix `indexes'[`i',7]=`vp'[`clus',12] /*2nd order RELATIVE VARIATION OF THE T OR G CRITERION*/
|
||
|
|
foreach j of numlist `numlist`minj'' `numlist`mink'' {
|
||
|
|
matrix `matclus'[`j',`=`i'+1']=`clus'
|
||
|
|
}
|
||
|
|
local varlistgen
|
||
|
|
local nbvarlistgen
|
||
|
|
forvalues j=1/`=`nbitems'+`i'' {
|
||
|
|
local varlist`j'
|
||
|
|
forvalues k=1/`nbitems' {
|
||
|
|
if `matclus'[`k',`=`i'+1']==`j' {
|
||
|
|
local varlist`j' `varlist`j'' ``k''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
local nbvarlist`j': word count `varlist`j''
|
||
|
|
local varlistgen `varlistgen' `varlist`j''
|
||
|
|
local nbvarlistgen `nbvarlistgen' `nbvarlist`j''
|
||
|
|
}
|
||
|
|
local newlist
|
||
|
|
foreach m in `nbvarlistgen' {
|
||
|
|
if `m'!=0 {
|
||
|
|
local newlist `newlist' `m'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if "`kernel'"!=""&`i'==`=`nbkerk'-`nbkerg'+1' {
|
||
|
|
local T=`vp'[`=`clus'-1',8]
|
||
|
|
di _col(0) in ye "init" _col(12) %4.0f `=`nbitems'-`nbkerk'+`nbkerg'' _col(52) %8.4f `T' _col(62) %8.4f `=`totvar'-`T'' _col(72) %7.3f `=`T'/`totvar'*100' "%"
|
||
|
|
}
|
||
|
|
if `clus'>`nbitems'+`nbkerk'-`nbkerg' {
|
||
|
|
matrix `threshold'[`=`nbitems'-`i'+1',3]=`minegenval'
|
||
|
|
if `clus'==`nbitems'+`nbkerk'-`nbkerg'+1 {
|
||
|
|
local relv
|
||
|
|
local percent
|
||
|
|
local relv2
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local relv=`indexes'[`i',4]*100
|
||
|
|
local percent %
|
||
|
|
if `indexes'[`i',7]!=. {
|
||
|
|
local relv2=`indexes'[`i',7]*100
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local relv2=0
|
||
|
|
}
|
||
|
|
matrix `threshold'[`=`nbitems'-`i'+1',1]=`relv'
|
||
|
|
matrix `threshold'[`=`nbitems'-`i'+1',2]=`relv2'
|
||
|
|
}
|
||
|
|
if "`method'"=="centroid" {
|
||
|
|
di _col(0) in ye %4.0f `=`i'-`nbkerk'+`nbkerg'' _col(12) %4.0f `=`nbitems'-`i'' _col(20) "`nomj'" _col(33) "`nomk'" _col(45) %7.0f `=`i'+`nbitems'' _col(52) %8.4f `vp'[`clus',8] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',9] "%" _col(83) _col(84) %5.2f `relv' "`percent'" _col(93) %8.2f `relv2' "`percent'"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
di _col(0) in ye %4.0f `=`i'-`nbkerk'+`nbkerg'' _col(12) %4.0f `=`nbitems'-`i'' _col(20) "`nomj'" _col(33) "`nomk'" _col(45) %7.0f `=`i'+`nbitems'' _col(52) %8.4f `vp'[`clus',8] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',9] "%" _col(83) %8.4f `vp'[`clus',11] _col(94) %6.2f `relv' "`percent'" _col(103) %8.2f `relv2' "`percent'"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
matrix `indexes'[`nbitems',3]=`vp'[`=2*`nbitems'-1',5] /*RELATIVE VARIATION OF THE T OR G CRITERION*/
|
||
|
|
matrix `indexes'[`nbitems',7]=`indexes'[`nbitems',3]/`indexes'[`=`nbitems'-1',3] /*RELATIVE VARIATION OF THE T OR G CRITERION*/
|
||
|
|
local i=2*`nbitems'-1
|
||
|
|
matrix `threshold'[1,1]=`vp'[`i',5]/`vp'[`i',6]*100-100
|
||
|
|
matrix `threshold'[1,2]=(`threshold'[1,1]-`threshold'[2,1])/abs(`threshold'[2,1])*100
|
||
|
|
matrix `threshold'[1,3]=`vp'[`i',5]
|
||
|
|
if "`method'"=="centroid" {
|
||
|
|
di in ye _col(62) %8.4f `threshold'[1,3] _col(83) %6.2f `threshold'[1,1] "`percent'" _col(93) %8.2f `threshold'[1,2] "`percent'"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
di in ye _col(62) %8.4f `threshold'[1,3] _col(94) %6.2f `threshold'[1,1] "`percent'" _col(103) %8.2f `threshold'[1,2] "`percent'"
|
||
|
|
}
|
||
|
|
local best=0
|
||
|
|
local maxbest=0
|
||
|
|
local best2=0
|
||
|
|
local maxbest2=0
|
||
|
|
local demipart=int(`nbitems'/2)+1
|
||
|
|
forvalues i=1/`demipart' {
|
||
|
|
if `threshold'[`i',3]>`maxbest2' {
|
||
|
|
if `threshold'[`i',3]>`maxbest' {
|
||
|
|
local maxbest2=`maxbest'
|
||
|
|
local best2=`best'
|
||
|
|
local maxbest=`threshold'[`i',3]
|
||
|
|
local best=`i'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local maxbest2=`threshold'[`i',3]
|
||
|
|
local best2=`i'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di in green "{hline 111}"
|
||
|
|
di
|
||
|
|
di in green "{hline 60}"
|
||
|
|
di in green "PROPOSED BEST PARTITIONS (AMONG THE `demipart' SMALLER PARTITIONS)"
|
||
|
|
di in green "{hline 60}"
|
||
|
|
di
|
||
|
|
di in yellow _col(4) "Based on the variation of the T criterion: " _col(60) in gr "Partitions in " in ye `best' " or " `best2' in gr " clusters"
|
||
|
|
return local bestvariation `best' `best2'
|
||
|
|
local bestt=0
|
||
|
|
local bestt2=0
|
||
|
|
local var=0
|
||
|
|
local var2=0
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
if `threshold'[`i',1]>`var2'&`i'<`demipart' {
|
||
|
|
if `threshold'[`i',1]>`var' {
|
||
|
|
local bestt2=`bestt'
|
||
|
|
local var2=`var'
|
||
|
|
local var=`threshold'[`i',1]
|
||
|
|
local bestt=`i'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local var2=`threshold'[`i',1]
|
||
|
|
local bestt2=`i'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di in yellow _col(4) "Based on the research of a threshold: " _col(60) in gr "Partitions in " in ye `bestt' " or " `bestt2' in gr " clusters"
|
||
|
|
forvalues i=`=`nbitems'+1'/`=`nbitems'+`nbkerk'-`nbkerg'' {
|
||
|
|
matrix `vp'[`i',6]=`totvar'-`T'
|
||
|
|
matrix `vp'[`i',8]=`T'
|
||
|
|
matrix `vp'[`i',9]=`T'/`nbitems'*100
|
||
|
|
}
|
||
|
|
return local bestthresold `bestt' `bestt2'
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
if `threshold'[`i',2]>`var2'&`i'<`demipart' {
|
||
|
|
if `threshold'[`i',2]>`var' {
|
||
|
|
local bestt2=`bestt'
|
||
|
|
local var2=`var'
|
||
|
|
local var=`threshold'[`i',2]
|
||
|
|
local bestt=`i'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local var2=`threshold'[`i',2]
|
||
|
|
local bestt2=`i'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di in yellow _col(4) "Based on the research of a threshold (second order): " _col(60) in gr "Partitions in " in ye `bestt' " or " `bestt2' in gr " clusters"
|
||
|
|
return local bestthresold2 `bestt' `bestt2'
|
||
|
|
}
|
||
|
|
/******BAR CHART *******/
|
||
|
|
if "`bar'"!="" {
|
||
|
|
drop _all
|
||
|
|
qui set obs `nbitems'
|
||
|
|
qui svmat `indexes' ,names(v)
|
||
|
|
qui gen id=`nbitems'-_n
|
||
|
|
qui replace v7=. in 1
|
||
|
|
qui drop if id>`nbitems'-`nbkerk'+`nbkerg'-1
|
||
|
|
label variable id "Number of clusters"
|
||
|
|
label variable v3 "T variation"
|
||
|
|
qui su v3 if id!=0
|
||
|
|
local maxv3=ceil(r(max)*5)/5
|
||
|
|
local minv3=floor(r(min)*5)/5
|
||
|
|
label variable v4 "Relative T variation"
|
||
|
|
label variable v7 "Relative T variation order 2"
|
||
|
|
graph twoway (bar v3 id, name(bar,replace) vert yaxis(1))(line v4 id,yaxis(2))/*(line v6 id,yaxis(3))(line v5 id,yaxis(4))*/(line v7 id,yaxis(5)) if id!=0,ylabel(`minv3'(0.2)`maxv3') xlabel(1(1)`=`nbitems'-`nbkerk'+`nbkerg'-1')
|
||
|
|
}
|
||
|
|
/****** DENDROGRAM********/
|
||
|
|
drop _all
|
||
|
|
qui set obs `nbitems'
|
||
|
|
qui svmat `matclus' ,names(v)
|
||
|
|
local listorder
|
||
|
|
forvalues i=`nbitems'(-1)1 {
|
||
|
|
local listorder `listorder' v`i'
|
||
|
|
}
|
||
|
|
qui gen id=_n
|
||
|
|
qui sort `listorder'
|
||
|
|
|
||
|
|
capture cluster delete clv,zap
|
||
|
|
qui cluster complete v* ,name(clv)
|
||
|
|
|
||
|
|
qui replace clv_id=_n
|
||
|
|
qui replace clv_ord=id
|
||
|
|
qui replace clv_hgt=.
|
||
|
|
|
||
|
|
qui gen fait=0
|
||
|
|
qui gen clus=0
|
||
|
|
forvalues i=2/`nbitems' {
|
||
|
|
local ligne=`nbitems'+`i'-1
|
||
|
|
if (`vp'[`ligne',3]<=`nbitems') {
|
||
|
|
local first=`vp'[`ligne',3]
|
||
|
|
gsort +fait -v`i' +clv_id
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local first=`vp'[`ligne',4]
|
||
|
|
gsort +fait -v`i' +clv_id
|
||
|
|
}
|
||
|
|
if "`deltaT'"!="" {
|
||
|
|
qui replace clv_hgt=`vp'[`ligne',6] in 1
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
qui replace clv_hgt=100-`vp'[`ligne',9] in 1
|
||
|
|
}
|
||
|
|
qui replace fait=1 in 1
|
||
|
|
qui replace clus=`vp'[`ligne',1] in 1
|
||
|
|
}
|
||
|
|
if "`dendro'"=="" {
|
||
|
|
qui gen label=""
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
qui replace label=abbrev("`label`i''",`abbrev') if clv_id==`i'
|
||
|
|
}
|
||
|
|
sort clv_id
|
||
|
|
if `nbitems'>`cutnumber' {
|
||
|
|
local var "Groups of variables"
|
||
|
|
local cut cutnumber(`cutnumber') /*labcutn*/
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local var "Variables"
|
||
|
|
local cut label(label)
|
||
|
|
}
|
||
|
|
qui su clv_hgt
|
||
|
|
local tmp=r(max)
|
||
|
|
local max=floor(`tmp')+.5
|
||
|
|
if `tmp'>`max' {
|
||
|
|
local max=`max'+.5
|
||
|
|
}
|
||
|
|
local maxvar=`max'+5
|
||
|
|
if "`title'"=="" {
|
||
|
|
local title "Clustering around Latent Variables (CLV)"
|
||
|
|
}
|
||
|
|
if "`caption'"!="" {
|
||
|
|
local var "`caption'"
|
||
|
|
}
|
||
|
|
if "`deltaT'"!="" {
|
||
|
|
local titleL "Variation of the T criterion"
|
||
|
|
local yl "0(.5)`max'"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local titleL "% Unexplained Variance"
|
||
|
|
local yl "0(25)`maxvar'"
|
||
|
|
}
|
||
|
|
if "`textsize'"=="" {
|
||
|
|
local textsize: word `=min(int(`nbitems'/15)+1,5)' of medium medsmall small vsmall tiny
|
||
|
|
}
|
||
|
|
if "`horizontal'"!="" {
|
||
|
|
cluster dendro clv, name (dendrogram,replace) hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0) labsize(`textsize')) `cut'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
cluster dendro clv, name(dendrogram,replace) xtitle("`var'") `showcount' ytitle("`titleL'") title("`title'",span) ylabel(`yl') xlabel(,labsize(`textsize')) `cut'
|
||
|
|
}
|
||
|
|
if "`savedendro'"!="" {
|
||
|
|
graph save dendrogram `savedendro'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/***** END DENDROGRAM*****/
|
||
|
|
|
||
|
|
/**** TEST ********/
|
||
|
|
if `cons'>`nbitems'-`nbkerk'+`nbkerg' {
|
||
|
|
di in ye "The {hi:consolidation} is not possible for a number of clusters superior to the initial number of clusters"
|
||
|
|
local cons=0
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
/***** CONSOLIDATION PROCEDURE ********/
|
||
|
|
if `cons'!=0 {
|
||
|
|
sort v`=`nbitems'-`cons'+1'
|
||
|
|
gen cut`cons'=1
|
||
|
|
local g=1
|
||
|
|
forvalues i=2/`nbitems' {
|
||
|
|
if v`=`nbitems'-`cons'+1'[`i']!=v`=`nbitems'-`cons'+1'[`=`i'-1'] {
|
||
|
|
local g=`g'+1
|
||
|
|
}
|
||
|
|
qui replace cut`cons'=`g' in `i'
|
||
|
|
}
|
||
|
|
sort id
|
||
|
|
tempname group
|
||
|
|
mkmat cut`cons',matrix(`group')
|
||
|
|
|
||
|
|
use `clvfiletmp',replace
|
||
|
|
|
||
|
|
local n=1
|
||
|
|
local env=1
|
||
|
|
while (`env'==1) {
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
local list`g'
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
if `group'[`i',1]==`g' {
|
||
|
|
local list`g' `list`g'' ``i''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di
|
||
|
|
if `n'==1 {
|
||
|
|
di in green "{hline 30}"
|
||
|
|
di in green "PARTITION BEFORE CONSOLIDATION"
|
||
|
|
di in green "{hline 30}"
|
||
|
|
}
|
||
|
|
di
|
||
|
|
local col=13
|
||
|
|
local max=0
|
||
|
|
local critT=0
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
di _col(`col') in green "CLUSTER " %2.0f `g' _c
|
||
|
|
local col=`col'+12
|
||
|
|
local tmp`g':word count `list`g''
|
||
|
|
if `tmp`g''>`max' {
|
||
|
|
local max `tmp`g''
|
||
|
|
}
|
||
|
|
tempvar f1`g'
|
||
|
|
if "`method'"=="centroid" {
|
||
|
|
qui genscore `list`g'',score(`f1`g'') mean
|
||
|
|
qui su `f1`g'' [`weight'`exp']
|
||
|
|
local var=r(Var)
|
||
|
|
local critT=`critT'+`tmp`g''*`var'
|
||
|
|
qui pca `list`g'' [`weight'`exp'] ,cov
|
||
|
|
local trace=e(trace)
|
||
|
|
local explained`g'=`tmp`g''*`var'/`trace'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if `tmp`g''>1 {
|
||
|
|
if "`method'"=="classical"|"`method'"=="v2" {
|
||
|
|
qui pca `list`g'' [`weight'`exp'] ,cov
|
||
|
|
matrix `Ev'=e(Ev)
|
||
|
|
local trace=e(trace)
|
||
|
|
qui predict `f1`g''
|
||
|
|
}
|
||
|
|
else if "`method'"=="polychoric"|"`method'"=="polychoric" {
|
||
|
|
qui polychoricpca `list`g'' [`weight'`exp'] ,score(`f1`g'') nscore(1)
|
||
|
|
matrix `Ev'=r(eigenvalues)
|
||
|
|
local trace=0
|
||
|
|
forvalues m=1/`tmp`g''{
|
||
|
|
local trace =`trace'+`r(lambda`m')'
|
||
|
|
}
|
||
|
|
rename `f1`g''1 `f1`g''
|
||
|
|
}
|
||
|
|
local lambda1=`Ev'[1,1]
|
||
|
|
local explained`g'=`lambda1'/`trace'
|
||
|
|
local critT=`critT'+`lambda1'
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local explained`g'=1
|
||
|
|
qui gen `f1`g''=`list`g''
|
||
|
|
if "`standardized'"=="" {
|
||
|
|
local critT=`critT'+1
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
qui su [`weight'`exp']
|
||
|
|
local critT=`critT'+`r(Var)'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di
|
||
|
|
di _col(1) in green "ITEMS :" _c
|
||
|
|
forvalues i=1/`max' {
|
||
|
|
local col=15
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
local tmpv:word `i' of `list`g''
|
||
|
|
local tmpv=abbrev("`tmpv'",8)
|
||
|
|
di _col(`col') in ye %8s "`tmpv'" _c
|
||
|
|
local col= `col'+12
|
||
|
|
}
|
||
|
|
di
|
||
|
|
}
|
||
|
|
local col=16
|
||
|
|
di _col(1) in green "Expl. Var:" _c
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
di _col(`col') in ye %6.2f `=`explained`g''*100' in green "%" _c
|
||
|
|
local col= `col'+12
|
||
|
|
}
|
||
|
|
di
|
||
|
|
di
|
||
|
|
di in green "Variance Explained : " in ye %6.3f `=`critT'/`totvar'*100' in green "%"
|
||
|
|
di in green "T criterion : " in ye %6.4f `critT'
|
||
|
|
di
|
||
|
|
di in green "{hline 21}"
|
||
|
|
di in green "CONSOLIDATION: STEP `n'"
|
||
|
|
di in green "{hline 21}"
|
||
|
|
local n=`n'+1
|
||
|
|
local env=0
|
||
|
|
if "`method'"=="polychoric"|"`method'"=="polychoricv2" {
|
||
|
|
local command polychoric
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local command corr
|
||
|
|
}
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
local env`i'=0
|
||
|
|
local gr=`group'[`i',1]
|
||
|
|
qui `command' ``i'' `f1`gr'' [`weight'`exp']
|
||
|
|
local corr`i'=r(rho)
|
||
|
|
local corrs`i'=r(rho)
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
qui `command' ``i'' `f1`g'' [`weight'`exp']
|
||
|
|
local tmpcorr=r(rho)
|
||
|
|
if `g'!=`gr'&(((`corr`i'')<(`tmpcorr')&"`method'"=="centroid")|((`corr`i'')^2<(`tmpcorr')^2& "`method'"!="centroid")) {
|
||
|
|
local env=1
|
||
|
|
local env`i'=1
|
||
|
|
matrix `group'[`i',1]=`g'
|
||
|
|
local corr`i'=`tmpcorr'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if `env`i''==1 {
|
||
|
|
local g=`group'[`i',1]
|
||
|
|
di in green "The variable " in ye "``i'' " in green "is assigned to the `g'th group" _c
|
||
|
|
if "`method'"!="centroid" {
|
||
|
|
di in green " (corr^2=" %6.4f in ye (`corr`i'')^2 in green " vs " in ye %6.4f (`corrs`i'')^2 in green ")"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
di in green " (corr=" %6.4f in ye (`corr`i'') in green " vs " in ye %6.4f (`corrs`i'') in green ")"
|
||
|
|
}
|
||
|
|
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if `env'==0 {
|
||
|
|
local latent
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
label variable `f1`g'' "Latent variable `g'"
|
||
|
|
if "`genlv'"!="" {
|
||
|
|
if "`replace'"!=""{
|
||
|
|
capture drop `genlv'`g'
|
||
|
|
}
|
||
|
|
gen `genlv'`g'=`f1`g''
|
||
|
|
}
|
||
|
|
local latent `latent' `f1`g''
|
||
|
|
return local cluster`g' `list`g''
|
||
|
|
}
|
||
|
|
matrix `group'=`group''
|
||
|
|
matrix colnames `group'=`varlist'
|
||
|
|
return matrix affect=`group'
|
||
|
|
di in ye "Stability of the partition is achieved"
|
||
|
|
if `cons'<=7 {
|
||
|
|
di
|
||
|
|
di in green "{hline 42}"
|
||
|
|
di in green "CORRELATION MATRIX OF THE LATENT VARIABLES"
|
||
|
|
di in green "{hline 42}"
|
||
|
|
di
|
||
|
|
di in green "{hline `=(`cons')*13+15'}"
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
di _col(`=13*(`g'-1)+23') in green "Latent" _c
|
||
|
|
}
|
||
|
|
di
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
di _col(`=13*(`g'-1)+19') in green "variable `g'" _c
|
||
|
|
}
|
||
|
|
di
|
||
|
|
di in green "{hline `=(`cons')*13+15'}"
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
di in green "Latent variable `g'" _c
|
||
|
|
forvalues h=1/`g' {
|
||
|
|
local loc=13*`h'+10
|
||
|
|
qui corr `f1`g'' `f1`h'' [`weight'`exp']
|
||
|
|
local rho=r(rho)
|
||
|
|
di _col(`loc') in ye %6.4f `rho' _c
|
||
|
|
}
|
||
|
|
di
|
||
|
|
}
|
||
|
|
di in green "{hline `=(`cons')*13+15'}"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
if `nbind'<=800&"`biplot'"==""&"`weight'"=="" {
|
||
|
|
local max=max(`matsize',`nbind')
|
||
|
|
qui set matsize `max'
|
||
|
|
if "`addvar'"!="" {
|
||
|
|
local add `varlist'
|
||
|
|
}
|
||
|
|
if "`dim'"=="" {
|
||
|
|
local dim 1 2
|
||
|
|
}
|
||
|
|
qui biplotvlab `latent' `add', name(biplot,replace) norow colopts(name(latent variables)) alpha(0) title(Biplot of the latent variables) labdes(size(vsmall) color(blue)) stretch(1) `std' dim(`dim')
|
||
|
|
}
|
||
|
|
else if `nbind'>800&"`biplot'"==""&"`weight'"==""{
|
||
|
|
di in green "There is more than 800 individuals, so the {hi:biplot} option is disabled"
|
||
|
|
}
|
||
|
|
else if "`weight'"!=""&&"`biplot'"==""{
|
||
|
|
di in green "The {hi:biplot} option is disabled because you use weights"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
forvalues g=1/`cons' {
|
||
|
|
drop `f1`g''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/***** END OF THE CONSOLIDATION PROCEDURE********/
|
||
|
|
|
||
|
|
qui set matsize `matsize'
|
||
|
|
if "`genlv'"!="" {
|
||
|
|
qui keep `id' `genlv'1-`genlv'`cons'
|
||
|
|
tempfile lvfile
|
||
|
|
qui sort `id'
|
||
|
|
qui save `lvfile',replace
|
||
|
|
}
|
||
|
|
use `clvfile',replace
|
||
|
|
if "`genlv'"!="" {
|
||
|
|
qui sort `id'
|
||
|
|
qui merge `id' using `lvfile'
|
||
|
|
}
|
||
|
|
qui drop `id'
|
||
|
|
capture drop _merge
|
||
|
|
capture cluster delete clv,zap
|
||
|
|
matrix colnames `vp'="Parent" "Number of clusters" "Child 1" "Child 2" "T" "DeltaT" "deltaT" "Explained Variance" "Explained Variance (%)" "First eigenvalue" "Second Eigenvalue" "2nd order deltaT"
|
||
|
|
return matrix vp=`vp'
|
||
|
|
return matrix matclus=`matclus'
|
||
|
|
return local varlist `varlist'
|
||
|
|
return local method `method'
|
||
|
|
return local kernel `kernel'
|
||
|
|
restore,not
|
||
|
|
end
|