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489 lines
15 KiB
Plaintext
489 lines
15 KiB
Plaintext
9 months ago
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*! Version 3.3 6 May 2014
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*! Jean-Benoit Hardouin
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************************************************************************************************************
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* hcavar: Hierachical Clusters Analysis (HCA) of variables
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* Version 3.3: May 7, 2014 /* HTML option, if option*/
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*
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* Use the Detect Stata program (ssc install detect)
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*
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* Historic :
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* Under the name of -hcaccprox-
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* Version 1 [2004-01-18], Jean-Benoit Hardouin
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* Version 2 [2004-05-12], Jean-Benoit Hardouin
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* Version 3 [2005-12-31], Jean-Benoit Hardouin
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* Version 3.1 [2006-01-15], Jean-Benoit Hardouin /* correction if there is only one individual with a given score*/
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* Version 3.2 [2010-04-15], Jean-Benoit Hardouin /* Possibility to use Polytomous Items with CCOR, CCOV and MH*/
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* Version 3.3 [2014-05-07], Jean-Benoit Hardouin, Bastien Perrot /* HTML option,, if option*/
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*
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* Jean-benoit Hardouin - Department of Biomathematics and Biostatistics - University of Nantes - France
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* EA 4275 "Biostatistics, Clinical Research and Subjective Measures in Health Sciences"
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* jean-benoit.hardouin@univ-nantes.fr
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*
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* News about this program :http://www.anaqol.org
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*
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* Copyright 2004-2006, 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 hcavar33, rclass
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version 9
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syntax varlist(min=2 numeric) [if] [in] [,PROX(string) METHod(string) PARTition(numlist) MEASures DETect MATrix(string) noDENDROgram HTML(string)]
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tempfile hcaccproxfile
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qui save `hcaccproxfile',replace
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preserve
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if "`if'"!="" {
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qui keep `if'
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}
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if "`html'"!="" {
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//set scheme sj
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//local htmlregion "graphregion(fcolor(white) ifcolor(white))"
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di "<!-- SphereCalc start of response -->"
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di "<pre>"
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}
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local nbitems : word count `varlist'
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tokenize `varlist'
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local type=0
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forvalues i=1/`nbitems' {
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qui drop if ``i''==.
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qui inspect ``i''
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if r(N_unique)>`type'&r(N_unique)!=. {
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local type=r(N_unique)
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}
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else if r(N_unique)>`type'&r(N_unique)==. {
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local type "100"
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}
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}
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if `type'==100 {
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local type ">99"
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}
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tempname proximity whereitems
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local prox=lower("`prox'")
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local method=lower("`method'")
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matrix define `proximity'=J(`nbitems',`nbitems',0)
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matrix define `whereitems'=J(`=`nbitems'-1',`nbitems',0)
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/**************************PROXIMITIES MEASURES DESCRIPTION************************/
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if "`matrix'"!="" {
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local desprox="Defined by the user"
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}
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if "`prox'"=="" {
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local prox="pearson"
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}
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else if "`prox'"=="a" {
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local prox="jaccard"
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}
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else if "`prox'"=="ad" {
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local prox="matching"
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}
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else if "`prox'"=="corr" {
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local prox="pearson"
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}
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if "`type'">"2"&"`prox'"!="pearson"&"`prox'"!="ccov"&"`prox'"!="ccor"&"`prox'"!="mh" {
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di in red "Only the {hi:pearson}, {hi:ccov} and {hi:ccor} measures of proximity are available with ordinal or numerous variables"
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di in red "Please correct your {hi:prox} option."
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exit
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}
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if "`type'">"2"&"`detect'"!="" {
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di in ye "The {hi:detect} option is available only with binary variables. This option is disabled."
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local detect
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di
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}
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local existmeas=0
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foreach i in jaccard matching pearson russel dice ccor mh ccov {
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if "`prox'"=="`i'" {
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local existmeas=1
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}
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}
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if `existmeas'==0 {
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di in red "You must define an existing measure of proximity (jaccard(a), matching(ad), pearson(cor), russel, dice, ccov, ccor, mh)."
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di in red "Please correct your {hi:prox} option."
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exit
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}
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if "`prox'"=="ccov"|"`prox'"=="mh" {
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local proxmin=0
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}
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if "`prox'"=="matching" {
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local desprox="Matching"
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}
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else if "`prox'"=="jaccard" {
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local desprox="Jaccard"
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}
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else if "`prox'"=="russel" {
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local desprox="Russel"
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}
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else if "`prox'"=="dice" {
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local desprox="Dice"
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}
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else if "`prox'"=="pearson" {
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local desprox="Pearson"
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}
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else if "`prox'"=="ccov" {
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local desprox="Conditional covariances"
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}
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else if "`prox'"=="ccor" {
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local desprox="Conditional correlations"
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}
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else if "`prox'"=="mh" {
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local desprox="Mantel Hanzel"
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}
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/**************************PROXIMITIES MEASURES DESCRIPTION************************/
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if "`method'"=="upgma"|"`method'"=="" {
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local method="average"
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}
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if "`method'"=="wpgma"|"`method'"=="" {
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local method="waverage"
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}
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local vermethod=0
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foreach i in average waverage single centroid median complete wards {
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if "`method'"=="`i'" {
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local vermethod=1
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}
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}
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if `vermethod'==0 {
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di in red "You must define an existing method to define the proximity between two clusters of items:"
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di in red _col(10) "- single: single linkage"
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di in red _col(10) "- complete: complete linkage "
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di in red _col(10) "- average(UPGMA): Unweighted Pair-Group Method of Average"
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di in red _col(10) "- waverage(WPGMA): Unweighted Pair-Group Method of Average"
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di in red _col(10) "- wards: Ward's linkage"
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di in red "Please correct your method option"
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exit
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}
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if "`method'"=="single"|"`method'"=="singlelinkage" {
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local method single
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local desmethod="Single linkage"
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}
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else if "`method'"=="complete"|"`method'"=="completelinkage" {
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local desmethod="Complete linkage"
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}
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else if "`method'"=="median"|"`method'"=="medianlinkage" {
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local desmethod="Median linkage (no dendrogram)"
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}
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else if "`method'"=="centroid"|"`method'"=="centroidlinkage" {
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local desmethod="Centroid linkage (no dendrogram)"
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}
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else if "`method'"=="average"|"`method'"=="averagelinkage" {
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local desmethod="Unweighted Pair-Group Method of Average"
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}
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else if "`method'"=="waverage"|"`method'"=="waveragelinkage" {
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local desmethod="Weighted Pair-Group Method of Average"
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}
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else if "`method'"=="wards"|"`method'"=="wardslinkage" {
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local desmethod="Ward's linkage"
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}
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forvalues i=1/`nbitems' {
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matrix `whereitems'[1,`i']=`i'
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}
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tempvar score
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genscore `varlist',score(`score')
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qui su `score'
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local maxscore=r(max)
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forvalues k=0/`maxscore' {
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qui count if `score'==`k'
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local nk`k'=r(N)
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}
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qui count
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local N=r(N)
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di in green "{hline 80}"
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di in green "Number of individuals with none missing values: " in ye `N'
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di in green "Maximal number of modalities for a variable: " in ye "`type'"
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di in green "Proximity measures: " in ye "`desprox'"
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di in green "Method to aggregate clusters: " in ye "`desmethod'"
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di in green "{hline 80}"
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di
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di
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/*************************Measure of proximities*********************************/
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if "`matrix'"=="" {
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forvalues i=1/`nbitems' {
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forvalues j=`=`i'+1'/`nbitems' {
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/***********************************Proximity AD*************************/
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if "`prox'"=="matching" { /*ad*/
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qui count if ``i''==1&``j''==1
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local tmp11=r(N)
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qui count if ``i''==0&``j''==0
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local tmp00=r(N)
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matrix `proximity'[`i',`j']=sqrt(1-(`tmp11'+`tmp00')/`N')
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/***********************************Proximity A**************************/
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else if "`prox'"=="jaccard" { /*a*/
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qui count if ``i''==1&``j''==1
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local tmp11=r(N)
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qui count if ``i''==0&``j''==0
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local tmp00=r(N)
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matrix `proximity'[`i',`j']=sqrt(1-`tmp11'/(`N'-`tmp00'))
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/***********************************Proximity Russel**************************/
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else if "`prox'"=="russel" {
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qui count if ``i''==1&``j''==1
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local tmp11=r(N)
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matrix `proximity'[`i',`j']=sqrt(1-`tmp11'/`N')
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/***********************************Proximity A**************************/
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else if "`prox'"=="dice" {
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qui count if ``i''==1&``j''==1
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local tmp11=r(N)
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qui count if ``i''==0&``j''==0
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local tmp00=r(N)
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matrix `proximity'[`i',`j']=sqrt(1-2*`tmp11'/(`N'+`tmp11'-`tmp00'))
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/**********************************Proximity COR*************************/
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else if "`prox'"=="pearson" { /*corr*/
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qui corr ``i'' ``j''
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matrix `proximity'[`i',`j']=sqrt(2*(1-r(rho)))
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/***********************************Proximity CCOV**********************/
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else if "`prox'"=="ccov" {
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local dij=0
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local Ntemp=`N'
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forvalues k=1/`=`maxscore'-1' {
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if `nk`k''!=0 {
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if `nk`k''>1 {
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qui corr ``i'' ``j'' if `score'==`k',cov
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local covi`i'j`j'k`k'=r(cov_12)
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}
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else if `nk`k''==1 {
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local Ntemp=`Ntemp'-1
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local covi`i'j`j'k`k'=0
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}
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else {
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local covi`i'j`j'k`k'=0
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}
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local dij=`dij'+`covi`i'j`j'k`k''*`nk`k''
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}
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}
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matrix `proximity'[`i',`j']=-`dij'/`Ntemp'
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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if `proxmin'<`dij'/`Ntemp' {
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local proxmin=`dij'/`Ntemp'
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}
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}
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/***********************************Proximity CCOR**********************/
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else if "`prox'"=="ccor" {
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local dij=0
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local nnull=0
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local Ntemp=`N'
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forvalues k=1/`=`maxscore'-1' {
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if `nk`k''!=0 {
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if `nk`k''>1 {
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qui corr ``i'' ``j'' if `score'==`k'
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local cori`i'j`j'k`k'=r(rho)
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}
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else if `nk`k''==1 {
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local Ntemp=`Ntemp'-1
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local cori`i'j`j'k`k'=0
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}
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else {
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local cori`i'j`j'k`k'=0
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}
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if `cori`i'j`j'k`k''!=. {
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local dij=`dij'+`cori`i'j`j'k`k''*`nk`k''
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}
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else if `cori`i'j`j'k`k''==. {
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local nnull=`nnull'+`nk`k''
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}
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}
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}
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matrix `proximity'[`i',`j']=sqrt(2*(1-`dij'/(`Ntemp'-`nnull')))
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matrix `proximity'[`j',`i']=`proximity'[`i',`j']
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}
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/***********************************Proximity MH************************/
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else if "`prox'"=="mh" {
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local numij=0
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local denom=0
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forvalues k=1/`=`maxscore'-1' {
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if `nk`k''!=0 {
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qui count if ``i''==1&``j''==1&`score'==`k'
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local A=r(N)
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qui count if ``i''==0&``j''==1&`score'==`k'
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local B=r(N)
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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
|