You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
490 lines
16 KiB
Plaintext
490 lines
16 KiB
Plaintext
7 months ago
|
*! Version 3.4 26 May 2014
|
||
|
|
*! Jean-Benoit Hardouin
|
||
|
|
************************************************************************************************************
|
||
|
|
* hcavar: Hierachical Clusters Analysis (HCA) of variables
|
||
|
|
* Version 3.4: May 26, 2014 /* DETECT option available for polytomous items */
|
||
|
|
*
|
||
|
|
* Use the Detect Stata program (ssc install detect)
|
||
|
|
*
|
||
|
|
* Historic :
|
||
|
|
* Under the name of -hcaccprox-
|
||
|
|
* Version 1 [2004-01-18], Jean-Benoit Hardouin
|
||
|
|
* Version 2 [2004-05-12], Jean-Benoit Hardouin
|
||
|
|
* Version 3 [2005-12-31], Jean-Benoit Hardouin
|
||
|
|
* Version 3.1 [2006-01-15], Jean-Benoit Hardouin /* correction if there is only one individual with a given score*/
|
||
|
|
* Version 3.2 [2010-04-15], Jean-Benoit Hardouin /* Possibility to use Polytomous Items with CCOR, CCOV and MH*/
|
||
|
|
* Version 3.3 [2014-05-07], Jean-Benoit Hardouin, Bastien Perrot /* HTML option, if option*/
|
||
|
|
* Version 3.4 [2014-05-26], Jean-Benoit Hardouin, Bastien Perrot /* DETECT option available for polytomous items */
|
||
|
|
*
|
||
|
|
* Jean-benoit Hardouin - Department of Biomathematics and Biostatistics - University of Nantes - France
|
||
|
|
* EA 4275 "Biostatistics, Clinical Research and Subjective Measures in Health Sciences"
|
||
|
|
* jean-benoit.hardouin@univ-nantes.fr
|
||
|
|
*
|
||
|
|
* News about this program :http://www.anaqol.org
|
||
|
|
*
|
||
|
|
* Copyright 2004-2006, 2010 Jean-Benoit Hardouin
|
||
|
|
*
|
||
|
|
* This program is free software; you can redistribute it and/or modify
|
||
|
|
* it under the terms of the GNU General Public License as published by
|
||
|
|
* the Free Software Foundation; either version 2 of the License, or
|
||
|
|
* (at your option) any later version.
|
||
|
|
*
|
||
|
|
* This program is distributed in the hope that it will be useful,
|
||
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||
|
|
* GNU General Public License for more details.
|
||
|
|
*
|
||
|
|
* You should have received a copy of the GNU General Public License
|
||
|
|
* along with this program; if not, write to the Free Software
|
||
|
|
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||
|
|
*
|
||
|
|
************************************************************************************************************
|
||
|
|
|
||
|
|
|
||
|
|
program define hcavar34, rclass
|
||
|
|
version 9
|
||
|
|
syntax varlist(min=2 numeric) [if] [in] [,PROX(string) METHod(string) PARTition(numlist) MEASures DETect MATrix(string) noDENDROgram HTML(string)]
|
||
|
|
|
||
|
|
tempfile hcaccproxfile
|
||
|
|
qui save `hcaccproxfile',replace
|
||
|
|
preserve
|
||
|
|
|
||
|
|
if "`if'"!="" {
|
||
|
|
qui keep `if'
|
||
|
|
}
|
||
|
|
|
||
|
|
if "`html'"!="" {
|
||
|
|
//set scheme sj
|
||
|
|
//local htmlregion "graphregion(fcolor(white) ifcolor(white))"
|
||
|
|
di "<!-- SphereCalc start of response -->"
|
||
|
|
di "<pre>"
|
||
|
|
}
|
||
|
|
|
||
|
|
local nbitems : word count `varlist'
|
||
|
|
tokenize `varlist'
|
||
|
|
local type=0
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
qui drop if ``i''==.
|
||
|
|
qui inspect ``i''
|
||
|
|
if r(N_unique)>`type'&r(N_unique)!=. {
|
||
|
|
local type=r(N_unique)
|
||
|
|
}
|
||
|
|
else if r(N_unique)>`type'&r(N_unique)==. {
|
||
|
|
local type "100"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if `type'==100 {
|
||
|
|
local type ">99"
|
||
|
|
}
|
||
|
|
|
||
|
|
tempname proximity whereitems
|
||
|
|
|
||
|
|
local prox=lower("`prox'")
|
||
|
|
local method=lower("`method'")
|
||
|
|
matrix define `proximity'=J(`nbitems',`nbitems',0)
|
||
|
|
matrix define `whereitems'=J(`=`nbitems'-1',`nbitems',0)
|
||
|
|
|
||
|
|
/**************************PROXIMITIES MEASURES DESCRIPTION************************/
|
||
|
|
|
||
|
|
if "`matrix'"!="" {
|
||
|
|
local desprox="Defined by the user"
|
||
|
|
}
|
||
|
|
if "`prox'"=="" {
|
||
|
|
local prox="pearson"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="a" {
|
||
|
|
local prox="jaccard"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="ad" {
|
||
|
|
local prox="matching"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="corr" {
|
||
|
|
local prox="pearson"
|
||
|
|
}
|
||
|
|
if "`type'">"2"&"`prox'"!="pearson"&"`prox'"!="ccov"&"`prox'"!="ccor"&"`prox'"!="mh" {
|
||
|
|
di in red "Only the {hi:pearson}, {hi:ccov} and {hi:ccor} measures of proximity are available with ordinal or numerous variables"
|
||
|
|
di in red "Please correct your {hi:prox} option."
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
|
||
|
|
if "`partition'"==""&"`detect'"!="" {
|
||
|
|
di in ye "option partition() required"
|
||
|
|
error 198
|
||
|
|
}
|
||
|
|
local existmeas=0
|
||
|
|
foreach i in jaccard matching pearson russel dice ccor mh ccov {
|
||
|
|
if "`prox'"=="`i'" {
|
||
|
|
local existmeas=1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if `existmeas'==0 {
|
||
|
|
di in red "You must define an existing measure of proximity (jaccard(a), matching(ad), pearson(cor), russel, dice, ccov, ccor, mh)."
|
||
|
|
di in red "Please correct your {hi:prox} option."
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
if "`prox'"=="ccov"|"`prox'"=="mh" {
|
||
|
|
local proxmin=0
|
||
|
|
}
|
||
|
|
|
||
|
|
if "`prox'"=="matching" {
|
||
|
|
local desprox="Matching"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="jaccard" {
|
||
|
|
local desprox="Jaccard"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="russel" {
|
||
|
|
local desprox="Russel"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="dice" {
|
||
|
|
local desprox="Dice"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="pearson" {
|
||
|
|
local desprox="Pearson"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="ccov" {
|
||
|
|
local desprox="Conditional covariances"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="ccor" {
|
||
|
|
local desprox="Conditional correlations"
|
||
|
|
}
|
||
|
|
else if "`prox'"=="mh" {
|
||
|
|
local desprox="Mantel Hanzel"
|
||
|
|
}
|
||
|
|
|
||
|
|
/**************************PROXIMITIES MEASURES DESCRIPTION************************/
|
||
|
|
|
||
|
|
if "`method'"=="upgma"|"`method'"=="" {
|
||
|
|
local method="average"
|
||
|
|
}
|
||
|
|
if "`method'"=="wpgma"|"`method'"=="" {
|
||
|
|
local method="waverage"
|
||
|
|
}
|
||
|
|
local vermethod=0
|
||
|
|
foreach i in average waverage single centroid median complete wards {
|
||
|
|
if "`method'"=="`i'" {
|
||
|
|
local vermethod=1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
if `vermethod'==0 {
|
||
|
|
di in red "You must define an existing method to define the proximity between two clusters of items:"
|
||
|
|
di in red _col(10) "- single: single linkage"
|
||
|
|
di in red _col(10) "- complete: complete linkage "
|
||
|
|
di in red _col(10) "- average(UPGMA): Unweighted Pair-Group Method of Average"
|
||
|
|
di in red _col(10) "- waverage(WPGMA): Unweighted Pair-Group Method of Average"
|
||
|
|
di in red _col(10) "- wards: Ward's linkage"
|
||
|
|
di in red "Please correct your method option"
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
if "`method'"=="single"|"`method'"=="singlelinkage" {
|
||
|
|
local method single
|
||
|
|
local desmethod="Single linkage"
|
||
|
|
}
|
||
|
|
else if "`method'"=="complete"|"`method'"=="completelinkage" {
|
||
|
|
local desmethod="Complete linkage"
|
||
|
|
}
|
||
|
|
else if "`method'"=="median"|"`method'"=="medianlinkage" {
|
||
|
|
local desmethod="Median linkage (no dendrogram)"
|
||
|
|
}
|
||
|
|
else if "`method'"=="centroid"|"`method'"=="centroidlinkage" {
|
||
|
|
local desmethod="Centroid linkage (no dendrogram)"
|
||
|
|
}
|
||
|
|
else if "`method'"=="average"|"`method'"=="averagelinkage" {
|
||
|
|
local desmethod="Unweighted Pair-Group Method of Average"
|
||
|
|
}
|
||
|
|
else if "`method'"=="waverage"|"`method'"=="waveragelinkage" {
|
||
|
|
local desmethod="Weighted Pair-Group Method of Average"
|
||
|
|
}
|
||
|
|
else if "`method'"=="wards"|"`method'"=="wardslinkage" {
|
||
|
|
local desmethod="Ward's linkage"
|
||
|
|
}
|
||
|
|
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
matrix `whereitems'[1,`i']=`i'
|
||
|
|
}
|
||
|
|
|
||
|
|
tempvar score
|
||
|
|
genscore `varlist',score(`score')
|
||
|
|
qui su `score'
|
||
|
|
local maxscore=r(max)
|
||
|
|
forvalues k=0/`maxscore' {
|
||
|
|
qui count if `score'==`k'
|
||
|
|
local nk`k'=r(N)
|
||
|
|
}
|
||
|
|
|
||
|
|
qui count
|
||
|
|
local N=r(N)
|
||
|
|
|
||
|
|
|
||
|
|
di in green "{hline 80}"
|
||
|
|
di in green "Number of individuals with none missing values: " in ye `N'
|
||
|
|
di in green "Maximal number of modalities for a variable: " in ye "`type'"
|
||
|
|
di in green "Proximity measures: " in ye "`desprox'"
|
||
|
|
di in green "Method to aggregate clusters: " in ye "`desmethod'"
|
||
|
|
di in green "{hline 80}"
|
||
|
|
di
|
||
|
|
di
|
||
|
|
|
||
|
|
/*************************Measure of proximities*********************************/
|
||
|
|
if "`matrix'"=="" {
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
||
|
|
/***********************************Proximity AD*************************/
|
||
|
|
if "`prox'"=="matching" { /*ad*/
|
||
|
|
qui count if ``i''==1&``j''==1
|
||
|
|
local tmp11=r(N)
|
||
|
|
qui count if ``i''==0&``j''==0
|
||
|
|
local tmp00=r(N)
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(1-(`tmp11'+`tmp00')/`N')
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
/***********************************Proximity A**************************/
|
||
|
|
else if "`prox'"=="jaccard" { /*a*/
|
||
|
|
qui count if ``i''==1&``j''==1
|
||
|
|
local tmp11=r(N)
|
||
|
|
qui count if ``i''==0&``j''==0
|
||
|
|
local tmp00=r(N)
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(1-`tmp11'/(`N'-`tmp00'))
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
/***********************************Proximity Russel**************************/
|
||
|
|
else if "`prox'"=="russel" {
|
||
|
|
qui count if ``i''==1&``j''==1
|
||
|
|
local tmp11=r(N)
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(1-`tmp11'/`N')
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
/***********************************Proximity A**************************/
|
||
|
|
else if "`prox'"=="dice" {
|
||
|
|
qui count if ``i''==1&``j''==1
|
||
|
|
local tmp11=r(N)
|
||
|
|
qui count if ``i''==0&``j''==0
|
||
|
|
local tmp00=r(N)
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(1-2*`tmp11'/(`N'+`tmp11'-`tmp00'))
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
/**********************************Proximity COR*************************/
|
||
|
|
else if "`prox'"=="pearson" { /*corr*/
|
||
|
|
qui corr ``i'' ``j''
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(2*(1-r(rho)))
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
/***********************************Proximity CCOV**********************/
|
||
|
|
else if "`prox'"=="ccov" {
|
||
|
|
local dij=0
|
||
|
|
local Ntemp=`N'
|
||
|
|
forvalues k=1/`=`maxscore'-1' {
|
||
|
|
if `nk`k''!=0 {
|
||
|
|
if `nk`k''>1 {
|
||
|
|
qui corr ``i'' ``j'' if `score'==`k',cov
|
||
|
|
local covi`i'j`j'k`k'=r(cov_12)
|
||
|
|
}
|
||
|
|
else if `nk`k''==1 {
|
||
|
|
local Ntemp=`Ntemp'-1
|
||
|
|
local covi`i'j`j'k`k'=0
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local covi`i'j`j'k`k'=0
|
||
|
|
}
|
||
|
|
local dij=`dij'+`covi`i'j`j'k`k''*`nk`k''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=-`dij'/`Ntemp'
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
if `proxmin'<`dij'/`Ntemp' {
|
||
|
|
local proxmin=`dij'/`Ntemp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/***********************************Proximity CCOR**********************/
|
||
|
|
|
||
|
|
else if "`prox'"=="ccor" {
|
||
|
|
local dij=0
|
||
|
|
local nnull=0
|
||
|
|
local Ntemp=`N'
|
||
|
|
forvalues k=1/`=`maxscore'-1' {
|
||
|
|
if `nk`k''!=0 {
|
||
|
|
if `nk`k''>1 {
|
||
|
|
qui corr ``i'' ``j'' if `score'==`k'
|
||
|
|
local cori`i'j`j'k`k'=r(rho)
|
||
|
|
}
|
||
|
|
else if `nk`k''==1 {
|
||
|
|
local Ntemp=`Ntemp'-1
|
||
|
|
local cori`i'j`j'k`k'=0
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local cori`i'j`j'k`k'=0
|
||
|
|
}
|
||
|
|
if `cori`i'j`j'k`k''!=. {
|
||
|
|
local dij=`dij'+`cori`i'j`j'k`k''*`nk`k''
|
||
|
|
}
|
||
|
|
else if `cori`i'j`j'k`k''==. {
|
||
|
|
local nnull=`nnull'+`nk`k''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=sqrt(2*(1-`dij'/(`Ntemp'-`nnull')))
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
/***********************************Proximity MH************************/
|
||
|
|
|
||
|
|
else if "`prox'"=="mh" {
|
||
|
|
local numij=0
|
||
|
|
local denom=0
|
||
|
|
forvalues k=1/`=`maxscore'-1' {
|
||
|
|
if `nk`k''!=0 {
|
||
|
|
qui count if ``i''==1&``j''==1&`score'==`k'
|
||
|
|
local A=r(N)
|
||
|
|
qui count if ``i''==0&``j''==1&`score'==`k'
|
||
|
|
local B=r(N)
|
||
|
|
qui count if ``i''==1&``j''==0&`score'==`k'
|
||
|
|
local C=r(N)
|
||
|
|
qui count if ``i''==0&``j''==0&`score'==`k'
|
||
|
|
local D=r(N)
|
||
|
|
local numij=`numij'+`A'*`D'/`nk`k''
|
||
|
|
local denomij=`denomij'+`B'*`C'/`nk`k''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix `proximity'[`i',`j']=-log(`numij'/`denomij')
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
if `proxmin'<log(`numij'/`denomij') {
|
||
|
|
local proxmin=-`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if "`prox'"=="ccov"|"`prox'"=="mh" {
|
||
|
|
forvalues i=1/`nbitems' {
|
||
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
||
|
|
matrix `proximity'[`i',`j']=`proximity'[`i',`j']+`proxmin'
|
||
|
|
if `proximity'[`i',`j']<0 {
|
||
|
|
matrix `proximity'[`i',`j']=0
|
||
|
|
}
|
||
|
|
matrix `proximity'[`j',`i']=`proximity'[`i',`j']
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/**********************END OD THE COMPUTING OF THE PROXIMITIES**************************************/
|
||
|
|
else {
|
||
|
|
matrix `proximity'=`matrix'
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix rowname `proximity'=`varlist'
|
||
|
|
matrix colname `proximity'=`varlist'
|
||
|
|
|
||
|
|
if "`measures'"!="" {
|
||
|
|
di in green "{hline 50}"
|
||
|
|
di in green "Measures of proximity between the items"
|
||
|
|
di in green "{hline 50}"
|
||
|
|
matrix list `proximity', noheader
|
||
|
|
di
|
||
|
|
}
|
||
|
|
|
||
|
|
/**********************CLUSTERING PROCEDURE **********************************************/
|
||
|
|
|
||
|
|
|
||
|
|
qui clustermat `method' `proximity',clear labelvar(name)
|
||
|
|
local hor "hor"
|
||
|
|
if "`method'"!="centroid"&"`method'"!="median"&"`dendrogram'"=="" {
|
||
|
|
if "`html'" != "" {
|
||
|
|
qui local saving "saving(`c(tmpdir)'/`html'_dendro,replace) nodraw"
|
||
|
|
qui cluster dendro ,labels(name) hor ylabel(,angle(0)) title("Hierarchical Cluster Analysis on variables") subtitle("`desmethod'") xtitle("`desprox' proximities") `saving'
|
||
|
|
qui graph use `c(tmpdir)'/`html'_dendro.gph
|
||
|
|
qui graph export `c(tmpdir)'/`html'_dendro.eps, replace
|
||
|
|
di "<br />"
|
||
|
|
di "<img src=" _char(34) "/data/`html'_dendro.png" _char(34)
|
||
|
|
di " class=" _char(34) "resgraph" _char(34) " alt=" _char(34) "dendro" _char(34) " title= " _char(34) "Hierarchical Cluster Analysis on variables - click to enlarge" _char(34) " width=" _char(34) "350" _char(34) " height=" _char(34) "240" _char(34) " >"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
qui cluster dendro ,labels(name) hor ylabel(,angle(0)) title("Hierarchical Cluster Analysis on variables") subtitle("`desmethod'") xtitle("`desprox' proximities")
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
if "`partition'"!="" {
|
||
|
|
foreach i of numlist `partition' {
|
||
|
|
qui cluster gen cluster`i'=group(`i')
|
||
|
|
}
|
||
|
|
tempname clusters
|
||
|
|
mkmat cluster* ,mat(`clusters')
|
||
|
|
matrix rownames `clusters'=`varlist'
|
||
|
|
local compteur=0
|
||
|
|
foreach i of numlist `partition' {
|
||
|
|
local ++compteur
|
||
|
|
di
|
||
|
|
di in green "{hline 30}"
|
||
|
|
di in green "Partition in `i' cluster(s)"
|
||
|
|
di in green "{hline 30}"
|
||
|
|
di
|
||
|
|
forvalues j=1/`i' {
|
||
|
|
local cluster`i'_`j'
|
||
|
|
local nbi`i'_`j'=0
|
||
|
|
forvalues k=1/`nbitems' {
|
||
|
|
if `clusters'[`k',`compteur']==`j' {
|
||
|
|
local cluster`i'_`j' `cluster`i'_`j'' ``k''
|
||
|
|
local ++nbi`i'_`j'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di in green "Cluster `j': " in ye "`cluster`i'_`j''"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return matrix clusters=`clusters'
|
||
|
|
}
|
||
|
|
|
||
|
|
/**********************DETECT OPTION **************************************************/
|
||
|
|
|
||
|
|
use `hcaccproxfile',clear
|
||
|
|
if "`detect'"!="" {
|
||
|
|
foreach i of numlist `partition' {
|
||
|
|
local liste
|
||
|
|
local part
|
||
|
|
forvalues j=1/`i' {
|
||
|
|
local liste "`liste' `cluster`i'_`j''"
|
||
|
|
local part "`part' `nbi`i'_`j''"
|
||
|
|
}
|
||
|
|
qui detect `liste',part(`part')
|
||
|
|
local detect`i'=r(DETECT)
|
||
|
|
local Iss`i'=r(Iss)
|
||
|
|
local R`i'=r(R)
|
||
|
|
}
|
||
|
|
tempname indexes
|
||
|
|
matrix define `indexes'=J(`compteur',4,0)
|
||
|
|
matrix colnames `indexes'=Clusters DETECT Iss R
|
||
|
|
di ""
|
||
|
|
di in green "{hline 50}"
|
||
|
|
di in green "Indexes to compare the partitions of the items"
|
||
|
|
di in green "{hline 50}"
|
||
|
|
di ""
|
||
|
|
di in green _col(29) "DETECT" _col(43) "Iss" _col(56) "R"
|
||
|
|
local compteur=0
|
||
|
|
foreach k of numlist `partition' {
|
||
|
|
local ++compteur
|
||
|
|
matrix `indexes'[`compteur',1]=`k'
|
||
|
|
matrix `indexes'[`compteur',2]=`detect`k''
|
||
|
|
matrix `indexes'[`compteur',3]=`Iss`k''
|
||
|
|
matrix `indexes'[`compteur',4]=`R`k''
|
||
|
|
di _col(5) in green "`k' cluster(s):" _col(27) in yellow %8.5f `detect`k'' _col(38) %8.5f `Iss`k'' _col(49) %8.5f `R`k''
|
||
|
|
}
|
||
|
|
return matrix indexes=`indexes'
|
||
|
|
}
|
||
|
|
return local nbvar=`nbitems'
|
||
|
|
return matrix measures=`proximity'
|
||
|
|
restore, not
|
||
|
|
*use `hcaccproxfile',clear
|
||
|
|
|
||
|
|
end
|