From 238852b08b4a7e8d9a419fff5a4a978f88bfdd7c Mon Sep 17 00:00:00 2001 From: corentinchoisy Date: Mon, 19 Feb 2024 18:35:26 +0100 Subject: [PATCH] Computed theoretical power for N=100 and N=200 scenarios --- Modules/ado/personal/_/_graph.ado | 100 + Modules/ado/personal/a/anaoption.ado | 48 + Modules/ado/personal/a/anares.ado | 36 + Modules/ado/personal/b/backrasch.ado | 171 + Modules/ado/personal/b/backrasch.hlp | 54 + Modules/ado/personal/b/biplotvlab.ado | 74 + Modules/ado/personal/b/biplotvlab.hlp | 38 + Modules/ado/personal/bidon | 1 + Modules/ado/personal/c/calcscore.ado | 155 + Modules/ado/personal/c/calcul.ado | 395 + Modules/ado/personal/c/cfa.ado | 363 + Modules/ado/personal/c/checkfor2.ado | 168 + Modules/ado/personal/c/checkfor2.hlp | 88 + Modules/ado/personal/c/checkvars.ado | 133 + Modules/ado/personal/c/checkvars.hlp | 90 + Modules/ado/personal/c/clv.ado | 957 ++ Modules/ado/personal/c/clv.sthlp | 122 + Modules/ado/personal/c/clv212.ado | 907 ++ 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+syntax varlist, PARTition(numlist integer >0) [SCOrename(string)] calcmethod(string) + +qui set autotabgraphs on + +local P:word count `partition' + +if "`scorename'" == "" { + local nname + forvalues i = 1/`P' { + local name "Dim`i'" + local scorename `scorename' `name' + } +} + +local i = 1 +foreach x in `varlist' { + local var`i' = "`x'" + local `++i' +} + +*capture calcscore `varlist', partition(`partition') scorename(`scorename') calcmethod(`calcmethod') + + +if "`calcmethod'" == "stand" local w = 10 +if "`calcmethod'" == "sum" local b = 10 +if "`calcmethod'" == "mean" local w = 0.5 + +set graphics off +foreach s in `scorename' { + qui hist `s', name("`s'",replace) percent fcolor(emidblue) lcolor(none) width(`w') bin(`b') +} +set graphics on + +gr combine `scorename', name("Histograms_scores",replace) + +qui biplot `scorename', name("Biplot_dimensions",replace) norow std title("") xtitle("") ytitle("") + +qui biplot `varlist', name("temp",replace) norow std nograph + +mat a = r(V) + +tempvar a1 a2 +mat colnames a = `a1' `a2' +svmat a, names(col) + +tempvar mina1 mina2 maxa1 maxa2 +egen `mina1' = min(`a1') +egen `mina2' = min(`a2') +egen `maxa1' = max(`a1') +egen `maxa2' = max(`a2') +local mina1 = `mina1' +local mina2 = `mina2' +local maxa1 = `maxa1'+1.4 +local maxa1x = `maxa1'+0.3 +local maxa2 = `maxa2' + +local colors = "red blue black green ebblue mint erose orange maroon magenta mint gray teal navy olive sienna" +local i = 1 +foreach c in `colors' { + local col`i' = "`c'" + local `++i' +} + +local i = 1 +local y = 1 +local c = 1 +local bas = `maxa2'+0.2 +local droite = max(`maxa1',0.2) + +foreach x in `partition' { + tokenize `scorename' + if `i' == 1 local s = `x' + else local s = `s' +`x' + + *local r = round(runiform()*255) + *local g = round(runiform()*255) + *local b = round(runiform()*255) + + forvalues j=`y'/`s' { + local a = `a1'[`j'] + local b = `a2'[`j'] + local call `call' || pcarrowi 0 0 `b' `a' "`var`j''", mlabcolor(`col`i'') color(`col`i'') head + } + local bas = `bas'-0.2 + *local droite = `droite' + local call `call' text(`bas' `droite' "``i''", size(3) color(`col`i'')) /*`bas' `droite' "``i''", mlabcolor("`r' `g' `b'")*/ + local `++i' + local y = `s'+1 + local `++c' +} + +qui twoway `call' name("Biplot_items",replace) legend(off) xscale(range(`mina1' `maxa1x')) yscale(range(`mina2' `maxa2')) xtitle("") ytitle("") + + +end + +*_graph x1-x40, partition(5 5 5 5 5 5 5 5) scorename(HAaaaaa PSE W BCC AC AE LI MOC) +*_graph ioc1-ioc37, partition(4 4 7 3 3 4 7 5) scorename(HA PSE W BCC AC AE LI MOC) calcmethod(stand) diff --git a/Modules/ado/personal/a/anaoption.ado b/Modules/ado/personal/a/anaoption.ado new file mode 100644 index 0000000..46e436a --- /dev/null +++ b/Modules/ado/personal/a/anaoption.ado @@ -0,0 +1,48 @@ +*! version 1 27may2007 +*! Jean-Benoit Hardouin +* +************************************************************************************************************ +* Stata program : anaoption +* +* Historic +* Version 1 (2007-05-27): Jean-Benoit Hardouin +* +* Jean-benoit Hardouin, phD, Assistant Professor +* Team of Biostatistics, Clinical Research and Subjective Measures in Health Sciences +* University of Nantes - Faculty of Pharmaceutical Sciences +* France +* jean-benoit.hardouin@anaqol.org +* +* News about this program :http://www.anaqol.org +* FreeIRT Project website : http://www.freeirt.org +* +* Copyright 2007 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 anaoption ,rclas +version 7.0 +syntax [, DETails minvi(real .03) siglevel(real 0.05) minsize(real 0)] + +return scalar minvi=`minvi' +return scalar siglevel=`siglevel' +return scalar minsize=`minsize' +return local details `details' + + +end + diff --git a/Modules/ado/personal/a/anares.ado b/Modules/ado/personal/a/anares.ado new file mode 100644 index 0000000..ead6c30 --- /dev/null +++ b/Modules/ado/personal/a/anares.ado @@ -0,0 +1,36 @@ +program define raschres,rclass +syntax varlist [, resname(string)] + +tempname diff theta +matrix `diff'=r(beta) +matrix `theta'=r(theta) + +if "`resname'"=="" { + local resname=res +} + +tokenize `varlist' +local nbitems: word count `varlist' +tempname score lt +genscore `varlist',score(`score') +gen `lt'=. + +forvalues i=0/`nbitems' { + replace `lt'=`theta'[1,`i'] if `score'==`i' +} + +forvalues i=1/`nbitems'{ + tempname p`i' res`i' + gen `p`i''=exp(`lt'-`diff'[1,`i'])/(1+exp(`lt'-`diff'[1,`i']) + gen `resname'`i'=(1-`p`i'')/sqrt(`p`i''*(1-`p`i'')) +} +pca `resname'* + + + + + + + + +end \ No newline at end of file diff --git a/Modules/ado/personal/b/backrasch.ado b/Modules/ado/personal/b/backrasch.ado new file mode 100644 index 0000000..08e958b --- /dev/null +++ b/Modules/ado/personal/b/backrasch.ado @@ -0,0 +1,171 @@ +*! version 3 11june2014 +************************************************************************************************************ +* Backrasch : Backward procedure under a Rasch model +* +* Historic +* Version 1 (2004-02-13) : Jean-Benoit Hardouin +* Version 2 (2005-05-23) : Jean-Benoit Hardouin +* Version 3 (2014-06-11) : Jean-Benoit Hardouin /*id for raschtest*/ +* +* Needed modules : +* raschtestv7 version 7.2.1 (http://freeirt.free.fr) +* gammasym version 2.1 (http://freeirt.free.fr) +* gausshermite version 1 (http://freeirt.free.fr) +* geekel2d version 4.1 (http://freeirt.free.fr) +* ghquadm (findit ghquadm) +* gllamm version 2.3.10 (ssc describe gllamm) +* gllapred version 2.3.2 (ssc describe gllapred) +* elapse (ssc describe elapse) +* +* Jean-benoit Hardouin, Regional Health Observatory of Orléans - France +* jean-benoit.hardouin@orscentre.org +* +* News about this program : http://anaqol.free.fr +* FreeIRT Project : http://freeirt.free.fr +* +* Copyright 2004-2005, 2014 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 backrasch , rclass +version 8.0 +syntax varlist(min=3 numeric) , [p(real 0.05) Method(string) Test(string) NBSCales(integer 1) nodetail noAUTOGroup] +local nbitems : word count `varlist' +tokenize `varlist' +preserve + + +tempfile saveraschtest +qui save `saveraschtest' + +local autogroup2 +if "`autogroup'"=="" { + local autogroup2 autogroup +} +if "`method'"=="" { + local method cml +} +if "`test'"=="" { + local test R +} + +tempname select +matrix `select'=J(1,`nbitems',0) +local dim=1 +local less3items=0 + +while `dim'<=`nbscales'&`less3items'!=1 { + di + di in green _col(25) "subscale : " in yellow `dim' + di in green _col(25) "{hline 12}" + local nobaditem=0 + while `nobaditem'!=1 { + local varlistscale + local nbitemsscale=0 + forvalues i=1/`nbitems' { + if `select'[1,`i']==0 { + local nbitemsscale=`nbitemsscale'+1 + local ssitem`nbitemsscale'=`i' + local varlistscale `varlistscale' ``i'' + } + } + + if `nbitemsscale'<3 { + if "`detail'"=="" { + di in green "The " in yellow "`dim'th " in green "sub-scale can not be created, because there is less than three items remaining" + } + local `less3items'=1 + local dim=`dim'-1 + continue, break + } + else { + tempvar tmp + capture gen `tmp'=_n + qui raschtestv7 `varlistscale',m(`method') t(`test') `autogroup2' id(`tmp') + tempname itemFit + matrix `itemFit'=r(itemFit) + local minp=`p' + local deleteitem + local nobaditem=1 + forvalues i=1/`nbitemsscale' { + if `itemFit'[`i',3]<`minp' { + local minp=`itemFit'[`i',3] + local deleitem=`i' + local rowdeleteitem=`ssitem`i'' + local nobaditem=0 + } + } + if `nobaditem'==1 { + if "`detail'"=="" { + di in green "No more item to remove of the scale " in yellow "`dim'" + } + continue, break + } + else { + if "`detail'"=="" { + di in green "The item " in yellow "``rowdeleteitem'' " in green "is removed of the scale " in yellow "`dim'" in green " (p=" in yellow %6.4f `minp' in green ")" + } + matrix `select'[1,`rowdeleteitem']=-1 + } + } + } + + if `nbitemsscale'>=3 { + forvalues i=1/`nbitems' { + if `select'[1,`i']==0 { + matrix `select'[1,`i']==`dim' + } + if `select'[1,`i']==-1 { + matrix `select'[1,`i']==0 + } + } + local scale`dim' + forvalues i=1/`nbitems' { + if `select'[1,`i']==`dim' { + local scale`dim' "`scale`dim'' ``i''" + } + } + if "`scale`dim''"!="" { + di + di in green _col(4) "Number of selected items : " in yellow "`nbitemsscale'" + tempvar tmp2 + capture gen `tmp2'=_n + raschtestv7 `scale`dim'',m(`method') t(`test') `autogroup2' id(`tmp2') + di + di _dup(70) "-" + } + + + local dim=`dim'+1 + } + if `nbitemsscale'<3{ + forvalues i=1/`nbitems' { + if `select'[1,`i']==-1 { + matrix `select'[1,`i']==0 + } + } + continue, break + } + +} + +matrix colnames `select'=`varlist' +matrix rownames `select'=scale + +return matrix selection `select' +end + diff --git a/Modules/ado/personal/b/backrasch.hlp b/Modules/ado/personal/b/backrasch.hlp new file mode 100644 index 0000000..1046692 --- /dev/null +++ b/Modules/ado/personal/b/backrasch.hlp @@ -0,0 +1,54 @@ +{smcl} +{* 23may2005}{...} +{hline} +help for {hi:backrasch} +{hline} + +{title:Backward procedure on a Rasch model} + +{p 8 14 2}{cmd:backrasch} {it:varlist} {cmd:,} [{cmdab:m:ethod}({it:keyword}) +{cmdab:t:est}({it:keyword}) {cmdab:nodetail} +{cmdab:p}({it:#.###}) {cmdab:nbsc:ales}({it:#}) +{cmdab:noautog:roup}] + +{title:Description} + +{p 4 8 2}{cmd:backward} realizes a Backward procedure on a Rasch model: the +items are removed one per one if they have a bad fit to the Rasch model. The +fit of the items is valuated by a first-order statistics (test R1c, R1m or Q1) +It is possible to build several sub-scales of items, the second sub-scale is +build with the items unselected in the first sub-scales, the third one +with the items unselected in the two first sub-scales, and so on... By default, +the parameters of the Rasch model are estimated by conditional maximum +likelihood (CML), but it is possible to estimate them by marginal maximum +likelihood (MML) or generalized estimating equations (GEE). + +{title:Options} + +{p 4 8 2}{cmd:method}({it:cml/mml/gee}) defines the method of estimation of the difficulty parameters among conditional maximum likelihood (cml - by default), marginal maximum likelihood (mml) or generalized estimating equations (gee). + +{p 4 8 2}{cmd:test}({it:R/Q}) defines the first order statistics to use between R-type test (R1c or R1m - by default) or the Q1 test of Van den Wollenberg. + +{p 4 8 2}{cmd:nodetail} does not display the description of the algorithm. + +{p 4 8 2}{cmd:p}({it:#.###}) defines the level of signification who define a significantly bad fitted item (0.05 by default). + +{p 4 8 2}{cmd:nbscales}({it:#}) defines the maximal number of sub-scales to build. By default, the program builds only one sub-scale. + +{p 4 8 2}{cmd:noautograoup} forces the program to compute the first order fit statistics with the groups defined by the value of the score. by default, the scores are grouped to obtained groups of 30 individuals or more. + +{title:Examples} + +{p 4 8 2}{cmd:. backrasch item1 item2 item3 item4} /*estimation by CML, test R1c, only one scale is built*/ + +{p 4 8 2}{cmd:. backrasch item1 item2 item3 item4 , p(0.2) method(mml) nodetail} /*estimation by MML, test R1m, only one scale*/ + +{p 4 8 2}{cmd:. backrasch item1 item2 item3 item4 , p(0.1) nbsc(5) noautog} /*CLM estimation, R1c tests, 5 scales will be build*/ + +{title:Author} + +{p 4 8 2}Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte +Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France. You can contact the author at +{browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org} +and visit the websites {browse "http://anaqol.free.fr":AnaQol} and +{browse "http://freeirt.free.fr":FreeIRT} diff --git a/Modules/ado/personal/b/biplotvlab.ado b/Modules/ado/personal/b/biplotvlab.ado new file mode 100644 index 0000000..cc09ac5 --- /dev/null +++ b/Modules/ado/personal/b/biplotvlab.ado @@ -0,0 +1,74 @@ +*! version 1.0.1 30sep2005 +*! The first version of this module has been wroten by Ken Higbee (StataCorp) +*! Improvements by Jean-Benoit Hardouin + +program biplotvlab + + version 9 + + syntax varlist(numeric min=2) [if] [in] [, LABdes(string) stretch(int 1) *] + + // run biplot quietly (and nograph) so we can get r(V) + qui biplot `varlist' `if' `in' , `options' nograph + tempname V + mat `V' = r(V) + local nbvar:word count `varlist' + tokenize `varlist' + + // build the -text()- option + local topt "text(" + local i 0 + local miny=`V'[1,2] + local maxy=`V'[1,2] + local minx=`V'[1,1] + local maxx=`V'[1,1] + forvalues i=1/`nbvar' { + local miny=min(`V'[`i',2],`miny') + local maxy=max(`V'[`i',2],`maxy') + local minx=min(`V'[`i',1],`minx') + local maxx=max(`V'[`i',1],`maxx') + } + if `maxx'*`minx'>0 { + local coefx=max(abs(`maxx'),abs(`minx')) + local coefx=`coefx'/20 + } + else { + local coefx=abs(`maxx'-`minx')/20 + } + if `maxx'*`minx'>0 { + local coefy=max(abs(`maxy'),abs(`miny')) + local coefy=`coefy'/20 + } + else { + local coefy=abs(`maxy'-`miny')/20 + } + forvalues i=1/`nbvar' { + // y value + if `V'[`i',2]>0 { + local topt `"`topt' `= (`V'[`i',2]+`coefy')*`stretch''"' + } + else { + local topt `"`topt' `= (`V'[`i',2]-`coefy')*`stretch''"' + } + // x value + if `V'[`i',1]<0 { + local topt `"`topt' `= (`V'[`i',1]-`coefx')*`stretch''"' + } + else { + local topt `"`topt' `= (`V'[`i',1]+`coefx')*`stretch''"' + } + // variable label + local lab: var label ``i'' + if "`lab'"=="" { + local lab ``i'' + } + local topt `"`topt' `"`lab'"' "' + } + if "`labdes'"=="" { + local labdes size(vsmall) + } + local topt `"`topt',`labdes')"' + + // call with -colopts(nolabel)- and -text()- just built + biplot `varlist' `if' `in', `options' colopts(nolabel) `topt' stretch(`stretch') +end diff --git a/Modules/ado/personal/b/biplotvlab.hlp b/Modules/ado/personal/b/biplotvlab.hlp new file mode 100644 index 0000000..5eef3de --- /dev/null +++ b/Modules/ado/personal/b/biplotvlab.hlp @@ -0,0 +1,38 @@ +{smcl} +{* 30sept2005}{...} +{hline} +help for {hi:biplotvlab}{right:Jean-benoit Hardouin} +{hline} + +{title:Biplot with variable labels} + +{p 8 14 2}{cmd:biplotvlab} {it:varlist} [{cmd:,} {cmdab:lab:des}({help textbox_options}) {help biplot:biplot_options}) + +{title:Description} + +{p 4 8 2}{cmd:biplotvlab} allows displaying on a biplot graph the labels of the +variables instead of the names of the variables. If none label is defined for one +or several variables, the names of these variables are displayed instead. +{cmd:biplotvlab} replaces the official {cmd:biplot} command. + +{title:Options} + +{p 4 8 2}{cmd:labdes} defines the options to apply to the labels (color, size...). + +{title:Examples} + +{p 4 8 2}{cmd:. biplotvlab turn trunk mpg,norow} + +{p 4 8 2}{cmd:. biplotvlab turn trunk mpg,labdes(size(vsmall) color(blue))} + +{p 4 8 2}{cmd:. biplotvlab turn trunk mpg, stretch(12)} + +{title:Authors} + +{p 4 8 2}Ken Higbee (StataCorp) has wroten the first version of this module. + +{p 4 8 2}Improvements by Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte +Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France. You can contact the author at +{browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org} +and visit the websites {browse "http://anaqol.free.fr":AnaQol} and +{browse "http://freeirt.free.fr":FreeIRT} diff --git a/Modules/ado/personal/bidon b/Modules/ado/personal/bidon new file mode 160000 index 0000000..bdcabcb --- /dev/null +++ b/Modules/ado/personal/bidon @@ -0,0 +1 @@ +Subproject commit bdcabcbde5043d31555d7d58758441d90f92f235 diff --git a/Modules/ado/personal/c/calcscore.ado b/Modules/ado/personal/c/calcscore.ado new file mode 100644 index 0000000..c43587c --- /dev/null +++ b/Modules/ado/personal/c/calcscore.ado @@ -0,0 +1,155 @@ +capture program drop calcscore +program calcscore,rclass +syntax varlist, PARTition(numlist integer >0) [CALCmethod(string) SCOrename(string)] + +local y = 1 + +*di "{bf:Calculs des scores}" + +local C = 0 +foreach z in `partition' { + local C = `C' + `z' +} + +local nbvars : word count `varlist' +if `C' != `nbvars' { + di in red "The sum of the numbers in the partition option is different from the number of variables precised in varlist" + exit +} + +if "`scorename'" != "" { + local P:word count `partition' + local S:word count `scorename' + if `P'!=`S' { + di in red "The number of score names given is different from the number of dimensions in the partition option" + exit 119 + } + foreach sco in `scorename' { + capture confirm variable `sco' + if !_rc { + di in red "`sco' is a variable of the dataset. Choose another name" + exit 119 + } + } +} + +/* +local cpt = 0 +if "`sum'" != "" { + local cpt `cpt' + 1 +} +if "`mean'" != "" { + local cpt `cpt' + 1 +} +if "`stand'" != "" { + local cpt `cpt' + 1 +} +if `cpt'>1 { + di in red "You must choose between mean, sum or stand (the options are exclusive)" + exit 119 +} +*/ + +local i = 1 +foreach x in `partition' { + tokenize `varlist' + if `i' == 1 local s = `x' + else local s = `s' +`x' + + local liste = "" + forvalues w = `y'/`s' { + local liste `liste' ``w'' + } + + tempvar nonmiss + qui egen `nonmiss' = rownonmiss(`liste') + + if "`scorename'" != "" { + tokenize `scorename' + local sc = "``i''" + } + else local sc = "Dim`i'" + +/* if "`calc_method'" == "" { + local calc_method = "mean" + } + + if "`calc_method'" != "sum" & "`calc_method'" != "mean" { + di in red "The calc_method option is invalid. Choose mean or sum." + exit 119 + } + + if "`calc_method'" == "sum" { + qui egen `sc' = rowmean(`liste') if `nonmiss' >= `x'/2 + if "`calc_stand'" != "" { + local maxs = 0 + foreach var in `liste' { + qui levelsof `var', local(levels) + local max = 0 + foreach l in `levels' { + if `l'>`max' local max = `l' + } + local maxs = `maxs' + `max' + } + di "`sc' : `maxs'" + qui replace `sc' = `sc'*`nonmiss'*100/`maxs' + } + else qui replace `sc' = `sc'*`nonmiss' + } + else if "`calc_method'" == "mean" { + qui egen `sc' = rowmean(`liste') if `nonmiss' >= `x'/2 + } +*/ + if "`calcmethod'" == "" local calcmethod = "mean" + + if "`calcmethod'" != "mean" & "`calcmethod'" != "sum" & "`calcmethod'" != "stand" { + di in red "option calcmethod incorrectly specified (choose among mean, sum and stand)" + error 198 + } + + if "`calcmethod'" == "sum" { + qui egen `sc' = rowmean(`liste') if `nonmiss' >= `x'/2 + qui replace `sc' = `sc'*`nonmiss' + } + + else if "`calcmethod'" == "stand" { + + qui egen `sc' = rowmean(`liste') if `nonmiss' >= `x'/2 + qui replace `sc' = `sc'*`nonmiss' + + tempvar min max + egen `min' = min(`sc') + egen `max' = max(`sc') + + + /* + foreach var in `liste' { + /*qui levelsof `var', local(levels) + local max = 0 + foreach l in `levels' { + if `l'>`max' local max = `l' + } + local maxs = `maxs' + `max'*/ + + local max = max(`levels') + } + + */ + *di "max : "`max' + *di "min : "`min' + + qui replace `sc' = (`sc'-`min')/(`max'-`min')*100 + } + + else { + qui egen `sc' = rowmean(`liste') if `nonmiss' >= `x'/2 + } + +local `i++' +local y = `s'+1 +} +end + +*calcscore ioc1-ioc37, partition(4 4 7 3 3 4 7 5) scorename(HA PSE W BCC AC AE LI MOC) calcmethod(stand) +*calcscore x1-x40, partition(5 5 5 5 5 5 5 5) calcmethod(stand) +*calcscore sf36_3q_intenses sf36_3q_moderees sf36_3q_soulever sf36_3q_etages sf36_3q_etage sf36_3q_pencher sf36_3q_15km sf36_3q_500m sf36_3q_100m sf36_3q_douche sf36_4q_limite_temps_travail sf36_4q_moins_choses sf36_4q_type_travail sf36_4q_effort sf36_7q_intensite_douleurs sf36_8q_douleurs_physiques sf36_1q sf36_11q_malade sf36_11q_porte_bien sf36_11q_degrade sf36_11q_excellente_sante sf36_9q_enthousiaste sf36_9q_energie sf36_9q_epuise sf36_9q_fatigue sf36_6q_vie_sociale sf36_10q_etat_mental sf36_5q_limite_temps_travail sf36_5q_moins_choses sf36_5q_accomplies_soigneusement sf36_9q_nerveux sf36_9q_triste sf36_9q_calme sf36_9q_maussade sf36_9q_heureux, partition(10 4 2 5 4 2 3 5) scorename(PF RP BP GH VT SF RE MH) calcmethod(mean) diff --git a/Modules/ado/personal/c/calcul.ado b/Modules/ado/personal/c/calcul.ado new file mode 100644 index 0000000..a1e15eb --- /dev/null +++ b/Modules/ado/personal/c/calcul.ado @@ -0,0 +1,395 @@ +*program drop calcul +program define calcul +syntax, s10(numlist) + +matrix define deces=J(140,6,0) +local j=0 +tcm, s10(`s10') anneepop(1989) annees(1988/1990) sexe(1) +matrix essai=r(donnees) +local TCMH89=r(TCM) +matrix deces[1,1]=essai[2..19,4] +matrix deces[25,1]=`TCMH89' +tcm, s10(`s10') anneepop(1989) annees(1988/1990) sexe(2) +matrix essai=r(donnees) +local TCMF89=r(TCM) +matrix deces[1,2]=essai[2..19,4] +matrix deces[25,2]=`TCMF89' +tcm, s10(`s10') anneepop(1982) annees(1981/1983) sexe(1) +matrix essai=r(donnees) +local TCMH82=r(TCM) +matrix deces[1,3]=essai[2..19,4] +matrix deces[20,1]=`TCMH82' +tcm, s10(`s10') anneepop(1982) annees(1981/1983) sexe(2) +matrix essai=r(donnees) +local TCMF82=r(TCM) +matrix deces[1,4]=essai[2..19,4] +matrix deces[20,2]=`TCMF82' + + + +tcm, s10(`s10') anneepop(1983) annees(1982/1984) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[21,1]=`TCMH' +tcm, s10(`s10') anneepop(1983) annees(1982/1984) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[21,2]=`TCMF' + +tcm, s10(`s10') anneepop(1984) annees(1983/1985) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[22,1]=`TCMH' +tcm, s10(`s10') anneepop(1984) annees(1983/1985) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[22,2]=`TCMF' + +tcm, s10(`s10') anneepop(1985) annees(1984/1986) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[23,1]=`TCMH' +tcm, s10(`s10') anneepop(1985) annees(1984/1986) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[23,2]=`TCMF' + +tcm, s10(`s10') anneepop(1986) annees(1985/1987) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[24,1]=`TCMH' +tcm, s10(`s10') anneepop(1986) annees(1985/1987) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[24,2]=`TCMF' + +tcm, s10(`s10') anneepop(1992) annees(1991/1993) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[26,1]=`TCMH' +tcm, s10(`s10') anneepop(1992) annees(1991/1993) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[26,2]=`TCMF' + +tcm, s10(`s10') anneepop(1993) annees(1992/1994) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[27,1]=`TCMH' +tcm, s10(`s10') anneepop(1993) annees(1992/1994) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[27,2]=`TCMF' + +tcm, s10(`s10') anneepop(1994) annees(1993/1995) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[28,1]=`TCMH' +tcm, s10(`s10') anneepop(1994) annees(1993/1995) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[28,2]=`TCMF' + +tcm, s10(`s10') anneepop(1995) annees(1994/1996) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[29,1]=`TCMH' +tcm, s10(`s10') anneepop(1995) annees(1994/1996) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[29,2]=`TCMF' + +tcm, s10(`s10') anneepop(1996) annees(1995/1997) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[30,1]=`TCMH' +tcm, s10(`s10') anneepop(1996) annees(1995/1997) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[30,2]=`TCMF' + +tcm, s10(`s10') anneepop(1997) annees(1996/1998) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[31,1]=`TCMH' +tcm, s10(`s10') anneepop(1997) annees(1996/1998) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[31,2]=`TCMF' + +tcm, s10(`s10') anneepop(1998) annees(1997/1999) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +matrix deces[32,1]=`TCMH' +tcm, s10(`s10') anneepop(1998) annees(1997/1999) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[32,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) +matrix essai=r(donnees) +local TCMH=r(TCM) +local TCMH9800reg=r(TCM) +matrix deces[33,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) +matrix essai=r(donnees) +local TCMF=r(TCM) +local TCMF9800reg=r(TCM) +matrix deces[33,2]=`TCMF' + + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(18) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,1]=essai[2..19,2] +matrix deces[53,1]=r(TCM) +matrix deces[54,1]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(18) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,1]=essai[2..19,2] +matrix deces[74,1]=r(TCM) +matrix deces[75,1]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(28) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,2]=essai[2..19,2] +matrix deces[53,2]=r(TCM) +matrix deces[54,2]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(28) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,2]=essai[2..19,2] +matrix deces[74,2]=r(TCM) +matrix deces[75,2]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(36) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,3]=essai[2..19,2] +matrix deces[53,3]=r(TCM) +matrix deces[54,3]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(36) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,3]=essai[2..19,2] +matrix deces[74,3]=r(TCM) +matrix deces[75,3]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(37) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,4]=essai[2..19,2] +matrix deces[53,4]=r(TCM) +matrix deces[54,4]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(37) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,4]=essai[2..19,2] +matrix deces[74,4]=r(TCM) +matrix deces[75,4]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(41) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,5]=essai[2..19,2] +matrix deces[53,5]=r(TCM) +matrix deces[54,5]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(41) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,5]=essai[2..19,2] +matrix deces[74,5]=r(TCM) +matrix deces[75,5]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(45) tcmcomp(`TCMH9800reg') +matrix essai=r(donnees) +local TCMH18=r(TCM) +matrix deces[35,6]=essai[2..19,2] +matrix deces[53,6]=r(TCM) +matrix deces[54,6]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(45) tcmcomp(`TCMF9800reg') +matrix essai=r(donnees) +local TCMF=r(TCM) +matrix deces[56,6]=essai[2..19,2] +matrix deces[74,6]=r(TCM) +matrix deces[75,6]=r(pvalue) + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2411) +local TCMH=r(TCM) +matrix deces[77,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2411) +local TCMF=r(TCM) +matrix deces[77,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2412) +local TCMH=r(TCM) +matrix deces[78,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2412) +local TCMF=r(TCM) +matrix deces[78,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2413) +local TCMH=r(TCM) +matrix deces[79,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2413) +local TCMF=r(TCM) +matrix deces[79,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2414) +local TCMH=r(TCM) +matrix deces[80,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2414) +local TCMF=r(TCM) +matrix deces[80,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2421) +local TCMH=r(TCM) +matrix deces[81,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2421) +local TCMF=r(TCM) +matrix deces[81,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2422) +local TCMH=r(TCM) +matrix deces[82,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2422) +local TCMF=r(TCM) +matrix deces[82,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2423) +local TCMH=r(TCM) +matrix deces[83,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2423) +local TCMF=r(TCM) +matrix deces[83,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2424) +local TCMH=r(TCM) +matrix deces[84,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2424) +local TCMF=r(TCM) +matrix deces[84,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2431) +local TCMH=r(TCM) +matrix deces[85,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2431) +local TCMF=r(TCM) +matrix deces[85,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2432) +local TCMH=r(TCM) +matrix deces[86,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2432) +local TCMF=r(TCM) +matrix deces[86,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2433) +local TCMH=r(TCM) +matrix deces[87,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2433) +local TCMF=r(TCM) +matrix deces[87,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2434) +local TCMH=r(TCM) +matrix deces[88,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2434) +local TCMF=r(TCM) +matrix deces[88,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2441) +local TCMH=r(TCM) +matrix deces[89,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2441) +local TCMF=r(TCM) +matrix deces[89,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2442) +local TCMH=r(TCM) +matrix deces[90,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2442) +local TCMF=r(TCM) +matrix deces[90,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2443) +local TCMH=r(TCM) +matrix deces[91,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2443) +local TCMF=r(TCM) +matrix deces[91,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2444) +local TCMH=r(TCM) +matrix deces[92,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2444) +local TCMF=r(TCM) +matrix deces[92,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2451) +local TCMH=r(TCM) +matrix deces[93,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2451) +local TCMF=r(TCM) +matrix deces[93,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2452) +local TCMH=r(TCM) +matrix deces[94,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2452) +local TCMF=r(TCM) +matrix deces[94,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2453) +local TCMH=r(TCM) +matrix deces[95,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2453) +local TCMF=r(TCM) +matrix deces[95,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2461) +local TCMH=r(TCM) +matrix deces[96,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2461) +local TCMF=r(TCM) +matrix deces[96,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2462) +local TCMH=r(TCM) +matrix deces[97,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2462) +local TCMF=r(TCM) +matrix deces[97,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2463) +local TCMH=r(TCM) +matrix deces[98,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2463) +local TCMF=r(TCM) +matrix deces[98,2]=`TCMF' + +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(1) codegeo(2464) +local TCMH=r(TCM) +matrix deces[99,1]=`TCMH' +tcm, s10(`s10') anneepop(1999) annees(1998/2000) sexe(2) codegeo(2464) +local TCMF=r(TCM) +matrix deces[99,2]=`TCMF' + +use "C:\ado\personal\files\dccentre8000reduit.dta", clear +tab annee deptdom if S10==`s10' & sexe==1 , matcell(nbH) +matrix deces[101,1]=nbH +tab annee deptdom if S10==`s10' & sexe==2 , matcell(nbF) +matrix deces[121,1]=nbF + +matrix list deces + + +end diff --git a/Modules/ado/personal/c/cfa.ado b/Modules/ado/personal/c/cfa.ado new file mode 100644 index 0000000..006cbf4 --- /dev/null +++ b/Modules/ado/personal/c/cfa.ado @@ -0,0 +1,363 @@ +capture program drop cfa +program cfa,rclass +syntax varlist, PARTition(numlist integer >0) [SCOrename(string) CFAMethod(string) CFAStand] + + +local C = 0 +foreach z in `partition' { + local C = `C' + `z' +} + +local nbvars : word count `varlist' +if `C' != `nbvars' { + di in red "The sum of the numbers in the partition option is different from the number of variables precised in varlist" + exit +} + +local P:word count `partition' +if "`scorename'" !="" { + local S:word count `scorename' + if `P'!=`S' { + di in red "The number of score names given is different from the number of dimensions in the partition option" + exit + } +} + +local i = 1 +foreach x in `varlist' { + local var`i' = "`x'" + local `++i' +} + +local name +local nname +if "`scorename'"=="" { + forvalues i = 1/`P' { + local name "Dim`i'" + local nname `nname' `name' + } +local scorename = "`nname'" +} + +local upscorename = upper("`scorename'") + +*capture calcscore `varlist', scorename(`upscorename') partition(`partition') + +local i = 0 +local y = 1 +tokenize `upscorename' +foreach x in `partition' { + local `i++' + if `i' == 1 local s = `x' + else local s = `s' +`x' + + local liste = "" + forvalues w = `y'/`s' { + local liste `liste' `var`w'' + } + + local a = "(``i'' -> `liste')" + local z `z' `a' + + local y = `s'+1 +} +/* +local cpt = 0 +if "`cfa_ml'" != "" { + local method = "ml" + local cpt `cpt' + 1 +} +if "`cfa_mlmv'" != "" { + local method = "mlmv" + local cpt `cpt' + 1 +} +if "`cfa_adf'" != "" { + local method = "adf" + local cpt `cpt' + 1 +} +else local method = "ml" +if `cpt'>1 { + di in red "You must choose between cfa_ml, cfa_mlmv or cfa_adf (the options are exclusive)" + exit 119 +} +*/ + + +if "`cfamethod'" == "" local cfamethod = "ml" + +if "`cfamethod'" != "ml" & "`cfamethod'" != "mlmv" & "`cfamethod'" != "adf" { + di "`cfamethod'" + di in red "option cfamethod incorrectly specified (choose among ml, mlmv and adf)" + error 198 +} + + +if "`cfastand'" != "" local cfastand = "stand" + +di as result "{hline}" +di "{bf:Confirmatory factor analysis}" +di as result "{hline}" +di + +qui sem `z', method(`cfamethod') `cfastand' + +/* +sem (HA -> ioc1-ioc4) (PSE -> ioc5-ioc8) (W -> ioc9-ioc15) /// +(BCC -> ioc16-ioc18) (AC -> ioc19-ioc21) (AE -> ioc22-ioc25) /// +(LI -> ioc26-ioc32) (MOC -> ioc33-ioc37) +,stand +cov(e.ioc36*e.ioc37 +e.ioc28*e.ioc29 e.ioc14*e.ioc15 e.ioc23*e.ioc25 e.ioc33*e.ioc34 /// +e.ioc9*e.ioc10 e.ioc6*e.ioc8 e.ioc5*e.ioc7) // method(mlmv) +*/ + +/* factor loadings */ + +matrix r = r(table) +matrix r = r[1,1...] +matrix r = r' +local n = `nbvars'*2 + +matrix a = r[1,1] +forvalues i=3(2)`n' { + matrix b = r[`i',1] + matrix a = a\b +} + +/* standard error */ + +matrix r = r(table) +matrix r = r[2,1...] +matrix r = r' +local n = `nbvars'*2 + +matrix se = r[1,1] +forvalues i=3(2)`n' { + matrix b = r[`i',1] + matrix se = se\b +} + + +/* intercepts */ + +matrix r = r(table) +matrix r = r[1,1...] +matrix r = r' +local n = `nbvars'*2 + +matrix a2 = r[2,1] +forvalues i=4(2)`n' { + matrix b = r[`i',1] + matrix a2 = a2\b +} + +/* variances des erreurs */ + +local m = `n'+1 +matrix r = r(table) +matrix r = r[1,`m'...] +matrix r = r' + +matrix a3 = r[1,1] +forvalues i=2/`nbvars' { + matrix b = r[`i',1] + matrix a3 = a3\b +} + +/* variance des dimensions*/ + +matrix r = r(table) +local n = `nbvars'*3+1 +matrix r = r[1,`n'...] +matrix r = r' + +matrix var = r[1,1] +forvalues i=2/`P' { + matrix b = r[`i',1] + matrix var = var\b +} + + +local i = 1 +foreach v in `varlist' { + local var`i' = abbrev("`v'",10) + local `++i' +} + + +local i = 1 +foreach s in `scorename' { + local s`i' = abbrev("`s'",10) + local sc `sc' `s`i'' + local `++i' + +} + +local max = 10 +local dec = `max'+5 + +local max2 = 10 + +local dec2 = `dec'+`max2'+5 + +local a = e(N) +di "{text:Number of used individuals: `a'}" +di +di _col(`=`dec2'+17+4') "{bf:Estimation:}" + + +di as result "{bf:Item}" _c +di _col(`dec') "{bf:Dimension}" _c +*local col = `dec'+17 +di _col(`dec2') "{bf:Factor}" _c +*local col = `dec2'+17 +di _col(`=`dec2'+14') "{bf:Standard}" _c +*local col = `col'+17 +di _col(`=`dec2'+28') "{bf:Intercept}" _c +*local col = `col'+13 + + +if "`cfastand'" == "" { + di _col(`=`dec2'+42') "{bf:Variance of}" _c + di _col(`=`dec2'+56') "{bf:Variance of}" + + di _col(`dec2') "{bf:loading}" _c + *local col = `dec2'+17 + di _col(`=`dec2'+14') "{bf:error}" _c + *local col = `col'+30 + + di _col(`=`dec2'+42') "{bf:error}" _c + di _col(`=`dec2'+56') "{bf:dimension}" + local h = `dec2'+66 +} +else { + di _col(`=`dec2'+42') "{bf:Variance of}" + + di _col(`dec2') "{bf:loading}" _c + *local col = `dec2'+17 + di _col(`=`dec2'+14') "{bf:error}" _c + *local col = `col'+30 + + di _col(`=`dec2'+42') "{bf:errors}" + local h = `dec2'+52 +} + + +di "{hline `h'}" + +local i = 1 +local y = 1 +foreach x in `partition' { + if `i' == 1 local s = `x' + else local s = `s' +`x' + + forvalues z = `y'/`s' { + tokenize `sc' + di "{bf:`var`z''}"_c + di _col(`dec') "{bf:``i''}" _c + local t = a[`z',1] + local t : di %7.2f `t' + *local col = `dec'+17 + di _col(`dec2') "{text:`t'}" _c + + local t = se[`z',1] + local t : di %8.2f `t' + *local col = `dec2'+9 + di _col(`=`dec2'+14') "{text:`t'}" _c + + local t = a2[`z',1] + local t : di %9.2f `t' + *local col = `col'+17 + di _col(`=`dec2'+28') "{text:`t'}" _c + + local t = a3[`z',1] + local t : di %11.2f `t' + *local col = `col'+13 + + + if "`cfastand'" == "" & `z' == `y'{ + di _col(`=`dec2'+42') "{text:`t'}" _c + local t = var[`i',1] + local t : di %11.2f `t' + *local col = `dec2'+17+17+13+14 + di _col(`=`dec2'+56') "{text:`t'}" + } + else di _col(`=`dec2'+42') "{text:`t'}" + } + di + local `i++' + local y = `s'+1 +} + +qui estat gof, stats(all) + +local chi2 = r(chi2_ms) +local p = r(p_ms) +local ddl = r(df_ms) +local ratio = `chi2'/`ddl' +local rmsea = r(rmsea) +local lb = r(lb90_rmsea) +local ub = r(ub90_rmsea) +local nfi = 1-(r(chi2_ms)/r(chi2_bs)) +local rni = 1-(r(chi2_ms)-r(df_ms))/(r(chi2_bs)-r(df_bs)) +local cfi = r(cfi) +local ifi = (r(chi2_bs)-r(chi2_ms))/(r(chi2_bs)-r(df_ms)) +local mci = exp(-0.5*((r(chi2_ms)-r(df_ms))/(e(N)-1))) +local srmr = r(srmr) + +di +di "{bf:Goodness of fit}" +di + +di as result _col(4) "chi2" _c +di as result _col(20) "ddl" _c +di as result _col(28) "chi2/ddl" _c +di as result _col(42) "RMSEA [90% CI]" _c +di as result _col(64) "SRMR" _c +di as result _col(74) "NFI" _c +di as result _col(84) "RNI" _c +di as result _col(94) "CFI" _c +di as result _col(104) "IFI" _c +di as result _col(114) "MCI" + +*di as result "`P' dimensions" _c + +local t : di %7.2f `chi2' +di "{text:`t'}" _c +local t : di %3.0f `ddl' +di _col(20) "{text:`t'}" _c +local t : di %7.1f `ratio' +di _col(29) "{text:`t'}" _c +local t : di %5.3f `rmsea' +local l : di %5.3f `lb' +local u : di %5.3f `up' +di _col(40) "{text:`t' [`l' ; `u']}" _c +local t : di %5.3f `srmr' +di _col(63) "{text:`t'}" _c +local t : di %5.3f `nfi' +di _col(72) "{text:`t'}" _c +local t : di %5.3f `rni' +di _col(82) "{text:`t'}" _c +local t : di %5.3f `cfi' +di _col(92) "{text:`t'}" _c +local t : di %5.3f `ifi' +di _col(102) "{text:`t'}" _c +local t : di %5.3f `mci' +di _col(112) "{text:`t'}" +local p : di %5.3f `p' +di "{text:(p-value = `p')}" +di as result + + +/* +matrix ind = (`chi2',`ddl',`ratio',`rmsea',`nfi',`rni',`cfi',`ifi',`srmr') +matrix colnames ind = "chi2" "ddl" "chi2/ddl" "RMSEA" "NFI" "RNI" "CFI" "IFI" "SRMR" +matrix rownames ind = "" +di +di "{bf:Goodness of fit}" +matrix list ind, format(%6.3f) noheader +*/ +end + +*cfa ioc1-ioc37, partition(4 4 7 3 3 4 7 5) scorename(HAaaaaaaaaaaaaaaaaaaaaaaaaaz PSE W BCC AC AE LI MOC) cfamethod(ml) //cfastand +*cfa x1-x40, partition(5 5 5 5 5 5 5 5) cfastand diff --git a/Modules/ado/personal/c/checkfor2.ado b/Modules/ado/personal/c/checkfor2.ado new file mode 100644 index 0000000..a5e40ff --- /dev/null +++ b/Modules/ado/personal/c/checkfor2.ado @@ -0,0 +1,168 @@ +*!Version 1.1 +*!Data management utility: check for existence of variables in a dataset. +*!Authors: Amadou Bassirou DIALLO (World Bank) and Jean-Benoit Hardouin (Regional Health Observatory of Orléans) + +program checkfor2 , rclass +version 8 +syntax anything [if] [in] [, noList Tolerance(real 0) TAble noSUm GENMiss(namelist min=1 max=1) MISsing(string)] + +marksample touse +tempname rat +local av +local unav +local manymissings +local avnum + +quietly count if `touse' +local tot = r(N) + +qui isvar `anything' +local badlist `r(badlist)' +local varlist `r(varlist)' + +di _n +if "`table'"!="" { + if "`badlist'"!="" { + di _col(4) in green "{hline 39}" + di _col(4)in green "Unavailable variables: " + foreach i of local badlist { + di _col(4) in ye "`i'" + } + di _col(4) in green "{hline 39}" + di + } + di _col(4) in green "{hline 39}" + display _col(4) in gr "Existing" _col(15) in gr "Rate of" + display _col(4) in gr "Variable" _col(14) "missings" _col(26) "Type" _col(34) "Available" + di _col(4) in green "{hline 39}" +} + +tokenize `varlist' +local nbvar : word count `varlist' + +forvalues i=1/`nbvar' { + capture assert missing(``i'') if `touse' + local ty: type ``i'' + local tty = substr("`ty'", 1, 3) + if !_rc { + if "`table'"=="" { + display in ye "``i''" in gr " is empty in the database." in ye " ``i''" in gr " is not added to the available list." + } + else { + display _col(4) in gr "`=abbrev("``i''",8)'" _col(15) in ye "100.00%" _col(26) "`ty'" + } + local manymissings `manymissings' ``i'' + } + else { + if "`table'"=="" { + display in ye "``i''" in gr " exists and is not empty." + } + *Consider type + if "`tty'" == "str" { + qui count if (``i'' == ""|``i''=="`missing'") & `touse' + local num = r(N) + scalar `rat' = (`num'/`tot')*100 + } + else { + local avnum `avnum' ``i'' + capture confirm number `missing' + if _rc!=0 { + quietly count if ``i'' >= . & `touse' + } + else { + quietly count if (``i'' >= .|``i''==`missing') & `touse' + } + local num = r(N) + scalar `rat' = (`num'/`tot')*100 + } + if "`table'"=="" { + display in ye "``i''" in gr " has " in ye r(N) in gr " missings." + display in gr "Ratio number of missings of" in ye " ``i''" in gr " to total number of observations: " in ye %6.2f `rat' "%" + } + + if `rat' <= `tolerance' { + local av `av' ``i'' + if "`table'"=="" { + display in ye "``i''" in gr " is added to the available list." + } + else { + display _col(4) in gr "`=abbrev("``i''",8)'" in ye _col(15) %6.2f `rat' "%" _col(26) "`ty'" _col(34) "X" + } + } + else { + local manymissings `manymissings' ``i'' + if "`table'"=="" { + display in ye "``i''" in gr " has too many missings, compared to the tolerance level." + display in ye "``i''" in gr " is not added to the available list." + } + else { + display _col(4) in gr "`=abbrev("``i''",8)'" _col(15) in ye %6.2f `rat' "%" _col(26) "`ty'" + } + } + } + if "`table'"=="" { + di + } +} + +if "`table'"!="" { + di _col(4) in green "{hline 39}" +} + +return local available `av' +return local unavailable `badlist' +return local manymissings `manymissings' + +if "`avnum'" ~= ""&"`sum'"=="" { + display _newline + display in ye _col(14) "Unweighted summary statistics for available variables:" _n + capture confirm number `missing' + if _rc!=0 { + summarize `avnum' if `touse' + } + else { + foreach i of local avnum { + summarize `i' if `touse'&`i'!=`missing' + } + } +} + +if "`list'"== "" { + display _newline + display in ye _d(97) "_" + display _newline + if "`badlist'"~="" { + display in gr "Unavailable variables: " in ye _col(45) "`badlist'" _n + } + if "`av'"~="" { + display in gr "Available variables: " in ye _col(45) "`av'" _n + } + if "`manymissings'"~="" { + display in gr "Available variables but with too missings: " in ye _col(45) "`manymissings'" _n + } + display in ye _d(97) "_" +} + +if "`genmiss'" !="" { + capture confirm variable `genmiss' + if _rc!=0 { + qui gen `genmiss' = 0 + local nbav : word count `av' + tokenize `av' + forvalues i=1/`nbav' { + local ty: type ``i'' + local tty = substr("`ty'", 1, 3) + if "`tty'" == "str" { + qui replace `genmiss'=`genmiss'+1 if ``i''=="." + } + else { + qui replace `genmiss'=`genmiss'+1 if ``i''>=. + } + } + } + else { + di in green "The variable" in ye " `genmiss' " in green "already exists". + } +} + +end diff --git a/Modules/ado/personal/c/checkfor2.hlp b/Modules/ado/personal/c/checkfor2.hlp new file mode 100644 index 0000000..462848f --- /dev/null +++ b/Modules/ado/personal/c/checkfor2.hlp @@ -0,0 +1,88 @@ +{smcl} +{hline} +help for {cmd:checkfor2} {right:Amadou B. DIALLO} + {right:Jean-Benoit HARDOUIN} +{hline} + +{title:Allows checking whether a variable exists or not in a dataset.} + +{p 4 8 2}{cmd:checkfor2} {it:anything} [{cmd:,} + {cmdab:t:olerance}({it:#}) {cmdab:ta:ble} {cmdab:nol:ist} {cmdab:nosu:m} + {cmdab:genm:iss}({it:newvarname}) {cmdab:mis:sing}({it:string})] + +{title:Description} + +{p 4 4 2}{cmd:checkfor2} is a data management routine to check for existence of variables +within a (usually big) data set. + +{p 4 4 2}{cmd:checkfor2} searchs through the data whether each variable exists. +The variables are clustered between unavailable variables, available variables with +a little amount of missing values and available variables with too many missing values. + +{p 4 4 2}{cmd:isvar} must be installed ({stata ssc install isvar:ssc install isvar}). + +{title:Options} + +{p 4 4 2}{it:anything} is composed of variable names or lists of variables, + +{p 4 4 2}{cmd:tolerance} is the tolerance level (in percentage) to consider a variable as available, with default 0, + +{p 4 4 2}{cmd:nolist} avoids displaying availability status at the end of the process, + +{p 4 4 2}{cmd:nosum} avoids displaying summary statistics of available variables, + +{p 4 4 2}{cmd:table} displays the results in a table (instead as text), + +{p 4 4 2}{cmd:genmiss} creates a new variable containing the number of missing values among the available variables, + +{p 4 4 2}{cmd:missing} defines a specific value or string considered as a missing value. + + +{title:Saved results} + +{p 4 4 2} {cmd:r(unavailable)} names of unavailable variables.{p_end} + +{p 4 4 2} {cmd:r(available)} names of available variables with a small amount of missing values.{p_end} + +{p 4 4 2} {cmd:r(manymissings)} names of variables present but with too missings.{p_end} + +{title:Examples} + +{p 4 4 2}{cmd:. use mydata, clear }{p_end} + +{p 4 4 2}{cmd:. checkfor2 x y z , mis(99) genmiss(countmiss) }{p_end} + +{p 4 4 2}{cmd:. su `r(available)' }{p_end} + +{p 4 4 2}{cmd:. tab countmiss }{p_end} + +{p 4 4 2}{cmd:. u bigdataset in 1/100, clear // Big data set}{p_end} + +{p 4 4 2}{cmd:. checkfor2 v1 v2 v3 xx yy , nosum tol(5) tab}{p_end} + +{p 4 4 2}{cmd:. use `r(available)' using bigdataset, clear }{p_end} + +{title:Remarks} + +{p 4 4 2}{cmd:checkfor2} and its primary version ({cmd:checkfor}) have been primarily written for comparable surveys such as the Demography and +Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS). But this could easily applied +to any other survey. + +{title:Authors} + +{p 4 4 2}Amadou Bassirou DIALLO. +Poverty and Health Division, PREM, The World Bank.{p_end} +{p 4 4 2}Email: {browse "mailto:adiallo5@worldbank.org":adiallo5@worldbank.org} + +{p 4 4 2}Jean-Benoit HARDOUIN. +Regional Health Observatory of Orléans, France.{p_end} +{p 4 4 2}Email: {browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org} + +{title:Aknowledgements} + +{p 4 4 2}We would like to thank Christophe Rockmore and also Nick Cox +and Kit Baum for their comments. + +{title:Also see} + +{p 4 13 2}Online: help for {help checkfor}, {help isvar}, {help nmissing}, {help npresent}, {help missing} and {help dropmiss} if installed.{p_end} diff --git a/Modules/ado/personal/c/checkvars.ado b/Modules/ado/personal/c/checkvars.ado new file mode 100644 index 0000000..787c343 --- /dev/null +++ b/Modules/ado/personal/c/checkvars.ado @@ -0,0 +1,133 @@ +*!Version 1.1 +*!Data management utility: check for existence of variables in a dataset. +*!Authors: Amadou Bassirou DIALLO (World Bank) and Jean-Benoit Hardouin (Regional Health Observatory of Orléans) + +program checkvars, rclass +version 8 +syntax anything [if] [in] [, noList Tolerance(real 0) TAble noSUm GENMiss(namelist min=1 max=1)] + +marksample touse +tempname rat +local av +local unav +local manymissings + +quietly count if `touse' +local tot = r(N) + +qui isvar `anything' +local badlist `r(badlist)' +local varlist `r(varlist)' + +if "`table'"!="" { + if "`badlist'"!="" { + di _col(4)in green "Unavailable variables: " in ye "`badlist'" + di + } + di _col(4) in green "{hline 29}" + display _col(4) in gr "Existing" _col(15) in gr "Rate of" + display _col(4) in gr "Variable" _col(14) "missings" _col(24) "Available" + di _col(4) in green "{hline 29}" +} + +tokenize `varlist' +local nbvar:word count `varlist' + +forvalues i=1/`nbvar' { + capture assert missing(``i'') if `touse' + if !_rc { + if "`table'"=="" { + display in ye "``i''" in gr " is empty in the database." in ye " ``i''" in gr /// + " is not added to the available list" + } + else { + display _col(4) in gr "``i''" _col(14) "100.00%" + } + } + else { + if "`table'"=="" { + display in ye "``i''" in gr " exists and is not empty." + } + *if "`available'"~= "" { + quietly count if ``i'' >= . & `touse' + local num = r(N) + scalar `rat' = (`num'/`tot')*100 + if "`table'"=="" { + display in ye "``i''" in gr " has " in ye r(N) in gr " missings." + display in gr "Ratio number of missings of" in ye " ``i''" in gr /// + " to total number of observations: " in ye %6.2f `rat' "%" + } + if `rat' <= `tolerance' { + local av `av' ``i'' + if "`table'"=="" { + display in ye "``i''" in gr " is added to the available list." + } + else { + display _col(4) in gr "``i''" in ye _col(15) %6.2f `rat' "%" _col(32) "X" + } + } + else { + local manymissings `manymissings' ``i'' + if "`table'"=="" { + display in ye "``i''" in gr " has too many missings, compared to the tolerance level." + display in ye "``i''" in gr " is not added to the available list." + } + else { + display _col(4) in gr "``i''" _col(15) in ye %6.2f `rat' "%" + } + } + *} + } + if "`table'"=="" { + di + } +} +if "`table'"!="" { + di _col(4) in green "{hline 29}" +} + +return local available `av' +return local unavailable `badlist' +return local manymissings `manymissings' + +if "`av'" ~= ""&"`sum'"=="" { + display _newline + display in ye _col(14) "Unweighted summary statistics for available variables:" _n + summarize `av' if `touse' +} + + +if "`list'"== "" { + display _newline + display in ye _d(97) "_" + display _newline + if "`badlist'"~="" { + display in gr "Unavailable variables: " in ye _col(45) "`badlist'" _n + } + if "`av'"~="" { + display in gr "Available variables: " in ye _col(45) "`av'" _n + } + if "`manymissings'"~="" { + display in gr "Available variables but with too missings: " in ye _col(45) "`manymissings'" _n + } + display in ye _d(97) "_" +} + +if "`genmiss'" !="" { + capture confirm variable `genmiss' + if _rc!=0 { + qui gen `genmiss'=0 + local nbav:word count `av' + tokenize `av' + forvalues i=1/`nbav' { + qui replace `genmiss'=`genmiss'+1 if ``i''>=. + } + } + else { + di in green "The variable" in ye " `genmiss' " in green "already exists". + } + +} + + +end diff --git a/Modules/ado/personal/c/checkvars.hlp b/Modules/ado/personal/c/checkvars.hlp new file mode 100644 index 0000000..fc4d646 --- /dev/null +++ b/Modules/ado/personal/c/checkvars.hlp @@ -0,0 +1,90 @@ +{smcl} +{hline} +help for {cmd:checkvars} {right:Amadou B. DIALLO} + {right:Jean-Benoit HARDOUIN} +{hline} + +{title:Allows checking whether a variable exists or not in a dataset.} + + +{p 4 8 2}{cmd:checkvars} {it:anything} [{cmd:,} + {cmdab:t:olerance}({it:#}) {cmdab:ta:ble} {cmdab:nol:ist} {cmdab:nosu:m} + {cmdab:genm:iss}({it:newvarname})] + +{title:Description} + +{p 4 4 2}{cmd:checkvars} is a routine to check for existence of variables +within a (usually big) data set. + +{p 4 4 2}{cmd:checkvars} searchs through the data whether each variable exists. +The variables are clustered between unavailable variables, available variables with +a little amount of missing values and available variables with too many missing values. + +{p 4 4 2}{cmd:isvar} must be installed ({stata ssc install isvar:ssc install isvar}). + +{title:Options} + +{p 4 4 2}{it:anything} is composed of variable names or lists of variables, + +{p 4 4 2}{cmd:tolerance} is the tolerance level (in percentage) to consider a variable as available, with default 0, + +{p 4 4 2}{cmd:nolist} avoids displaying availability status at the end of the process, + +{p 4 4 2}{cmd:nosum} avoids displaying summary statistics of available variables, + +{p 4 4 2}{cmd:table} displays the results in a table (instead as text), + +{p 4 4 2}{cmd:genmiss} creates a new variable containing the number of missing values among the available variables. + + + +{title:Saved results} + +{p 4 4 2} {cmd:r(unavailable)} names of unavailable variables.{p_end} + +{p 4 4 2} {cmd:r(available)} names of available variables with a small amount of missing values.{p_end} + +{p 4 4 2} {cmd:r(manymissings)} names of variables but with too missings.{p_end} + +{title:Examples} + +{p 4 4 2}{cmd:. use mydata, clear }{p_end} + +{p 4 4 2}{cmd:. checkvars x y z ,genmiss(countmiss) }{p_end} + +{p 4 4 2}{cmd:. su `r(available)' }{p_end} + +{p 4 4 2}{cmd:. tab countmiss }{p_end} + +{p 4 4 2}{cmd:. u bigdataset in 1/100, clear // Big data set}{p_end} + +{p 4 4 2}{cmd:. checkvars v1 v2 v3 xx yy , nosum tol(5) tab}{p_end} + +{p 4 4 2}{cmd:. use `r(available)' using bigdataset, clear }{p_end} + +{title:Remarks} + +{p 4 4 2}{cmd:checkvars} and its primary versions ({cmd:checkfor} and {cmd:checkfor2}) have been primarily written for comparable surveys such as the Demography and +Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS). But this could easily applied +to any other survey. + +{title:Authors} + +{p 4 4 2}Amadou Bassirou DIALLO. +Poverty and Health Specialist. AFTPM, The World Bank.{p_end} +{p 4 4 2}Email: {browse "mailto:adiallo5@worldbank.org":adiallo5@worldbank.org} + +{p 4 4 2}Jean-Benoit HARDOUIN. +Regional Health Observatory of Orléans, France.{p_end} +{p 4 4 2}Email: {browse "mailto:jean-benoit.hardouin@orscentre.org":jean-benoit.hardouin@orscentre.org} + +{title:Aknowledgements} + +{p 4 4 2}We would like to thank Christophe Rockmore and also Nick Cox +and Kit Baum for their comments. + +{title:Also see} + +{p 4 13 2}Online: help for {help checkfor}, {help checkfor2}, {help isvar}, {help nmissing}, {help npresent}, {help missing} and {help dropmiss} if installed.{p_end} + + diff --git a/Modules/ado/personal/c/clv.ado b/Modules/ado/personal/c/clv.ado new file mode 100644 index 0000000..3804708 --- /dev/null +++ b/Modules/ado/personal/c/clv.ado @@ -0,0 +1,957 @@ +*! Version 2.17 10July2019 +*! Jean-Benoit Hardouin +************************************************************************************************************ +* Stata program : clv +* Clustering of variables around latent variables +* Version 2.14 : May 20th, 2010 /*dim and std options for biplots*/ +* +* Historic +* Version 1 (2005-06-11): Jean-Benoit Hardouin +* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/ +* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/ +* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/ +* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/ +* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/ +* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/ +* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/ +* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/ +* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/ +* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/ +* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/ +* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/ +* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/ +* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/ +* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/ +* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/ +* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/ +* Version 2.13 (2010-05-12): Jean-Benoit Hardouin /*corrections of bugs in KERNEL option and with METHOD(centroid)*/ +* Version 2.14 (2010-05-20): Jean-Benoit Hardouin /*DIM and STD options for biplots*/ +* Version 2.15 (2014-04-14): Jean-Benoit Hardouin /*save and use options*/ +* Version 2.16 (2014-04-30): Jean-Benoit Hardouin, Bastien Perrot /*HTML option*/ +* Version 2.17 (2019-07-10): Jean-Benoit Hardouin /*filesave and dirsave options*/ +* +* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences +* INSERM UMR 1246-SPHERE "Methods in Patient Centered Outcomes and Health Research", Nantes University, University of Tours +* jean-benoit.hardouin@univ-nantes.fr +* +* News about this program : http://anaqol.sphere-nantes.fr +* +* Copyright 2005-2006, 2010, 2014, 2019 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 clv,rclass +version 10 +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) save(string) use(string) FILESave DIRSave(string)] +preserve +tempfile clvfile +tempvar id +gen `id'=_n +qui save `clvfile',replace +local matsize=c(matsize) +local none=0 +if "`varlist'"==""&"`use'"=="" { + capture confirm matrix r(vp) + if _rc==0 { + capture confirm matrix r(matclus) + if _rc ==0 { + local none=1 + } + } + if `none'==0 { + di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}" + error 198 + exit + } +} + +if "`filesave'"!="" { + if "`dirsave'"=="" { + local dirsave `c(pwd)' + } + local fsb saving(`dirsave'//bar,replace) + local fsd saving(`dirsave'//dendrogram,replace) + local fsbi saving(`dirsave'//biplot,replace) +} +tempname matclus vp indexes + +/*********TESTS**********/ + +if "`use'"!="" { + local error=0 + capture matrix `vp'=`use'_vp + if _rc!=0 { + local error=_rc + } + capture matrix `matclus'=`use'_matclus + if _rc!=0 { + local error=_rc + } + local varlist $`use'_varlist + local method $`use'_method + local kernel $`use'_kernel + if "`varlist'"==""|"`method'"=="" { + local error=1 + } + if `error'!=0 { + di in red "You cannot use the {hi:use} option without a preliminary use of the {hi:save} option" + error 198 + exit + } +} +if `none'==1 { + matrix `vp'=r(vp) + matrix `matclus'=r(matclus) + local varlist `r(varlist)' + tokenize `varlist' + local nbitems=rowsof(`matclus') + if "`method'"!="" { + di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted." + } + local method `r(method)' + local kernel `r(kernel)' +} +if "`method'"=="" { + local method classical +} +if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" { + di in green "Initial variables are used with the {hi:polychoric} methods" + di in green "But the procedure is based on the matrix of the polychoric correlations" + di +} +if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" { + di in red "The {hi:method} `method' is unknown" + error 198 + exit +} + +tokenize `varlist' +local nbitems : word count `varlist' +marksample touse +qui keep if `touse' + +local mat=max(`matsize',`=`nbitems'*2') +qui set matsize `mat' + +if `nbitems'<3&`none'!=1 { + di in red "You need at least 3 variables" + error 198 + exit +} + + +/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/ +forvalues i=1/`nbitems'{ + local label`i':variable label ``i'' + if "`label`i''"=="" { + local label`i' ``i'' + } + if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + qui su ``i'' [`weight'`exp'] + local mean=r(mean) + if "`standardized'"=="" { + local sd=r(sd) + } + else { + local sd=1 + } + qui replace ``i''=(``i''-`mean')/`sd' + } +} + +tempfile clvfiletmp +qui save `clvfiletmp',replace + +qui su `1' [`weight'`exp'] +local nbind=r(sum_w) + + +local cons=`consolidation' + +/*COMPUTES THE TOTAL VARIANCE*/ + +if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + local totvar=0 + forvalues i=1/`nbitems' { + qui su ``i'' [`weight'`exp'] + local totvar=`totvar'+`r(Var)' + } +} +else { + local totvar `nbitems' +} +local nbkerk=0 +local nbkerg=0 + +/***** DEFINES THE KERNEL IF NECESSARY ********/ + +if "`kernel'"!="" { + local nbkerg:word count `kernel' + local fin0=0 + forvalues i=1/`nbkerg' { + local nbi`i':word `i' of `kernel' + local nbkerk=`nbkerk'+`nbi`i'' + local deb`i'=`fin`=`i'-1''+1 + local fin`i'=`deb`i''+`nbi`i''-1 + local list`i' + forvalues j=`deb`i''/`fin`i'' { + local list`i' `list`i'' ``j'' + } + } + tempname kerclus + matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0) + local ligne=1 + forvalues g=1/`nbkerg' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g'' + matrix `kerclus'[`ligne',3]=`deb`g''+1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + if `nbi`g''>2 { + forvalues i=2/`=`nbi`g''-1' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g''+`i' + matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + } + } + local eigen2=0 + } +} +if `nbitems'<`nbkerk' { + di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}" + error 198 + exit +} + +/*******DISPLAY THE FIRST RESULTS *******/ + +di +di in green "{hline 32}" +di in green "TOTAL VARIANCE: " in ye %16.5f `totvar' +di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind' +di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'" +di in green "{hline 32}" +di + +if "`kernel'"!="" { + forvalues i=1/`nbkerg' { + di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''" + di + } +} +else { + local nbkerk=0 + local nbkerg=0 +} + +/******** CLASSIFICATION PROCEDURE*******/ + +tempname Ev +if `none'!=1 { + matrix `matclus'=J(`nbitems',`nbitems',0) + matrix `vp'=J(`=2*`nbitems'-1',12,0) + matrix `indexes'=J(`nbitems',8,0) + forvalues i=1/`nbitems' { + matrix `matclus'[`i',1]=`i' + if "`method'"!="polychoric"&"`method'"!="polychoric" { + qui su ``i'' [`weight'`exp'] + matrix `vp'[`i',10]=r(Var) + } + else { + matrix `vp'[`i',10]=1 + } + matrix `vp'[`i',1]=`i' + matrix `vp'[`i',2]=`nbitems' + matrix `vp'[`i',8]=`totvar' + matrix `vp'[`i',9]=100 + } + matrix `vp'[`nbitems',5]=`nbitems' + if "`method'"=="centroid" { + local crit G + di in green "{hline 101}" + di in green _col(93) "2nd order" + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative" + 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" + di in green "{hline 101}" + } + else { + local crit T + di in green "{hline 111}" + if "`method'"=="v2"|"`method'"=="polychoricv2" { + di in green _col(84) "Maximal" _col(103) "2nd order" + } + else { + di in green _col(84) "Current" _col(103) "2nd order" + } + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative" + 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" + di in green "{hline 111}" + } + tempname threshold + matrix `threshold'=J(`nbitems',3,0) + forvalues i=1/`=`nbitems'-1' { + local clus=`nbitems'+`i' + local minegenval=999999 + local minegenval2=999999 + forvalues k=1/`=`clus'-1' { + local list`k' + local numlist`k' + forvalues j=1/`clus' { + if (`matclus'[`j',`i']==`k') { + local list`k' `list`k'' ``j'' + local numlist`k' `numlist`k'' `j' + } + } + } + if `clus'>`nbitems'+`nbkerk'-`nbkerg' { + if "`method'"=="centroid" { + tempname centrj centrk diffjk + } + forvalues j=1/`clus' { + local nblistj:word count `list`j'' + forvalues k=`=`j'+1'/`clus' { + local nblistk:word count `list`k'' + if `nblistj'!=0&`nblistk'!=0 { + if "`method'"=="centroid" { + qui genscore `list`j'',score(`centrj') mean + qui su `centrj' [`weight'`exp'] + local Varj=r(Var) + qui genscore `list`k'',score(`centrk') mean + qui su `centrk' [`weight'`exp'] + local Vark=r(Var) + qui gen `diffjk'=`centrk'-`centrj' + qui su `diffjk' [`weight'`exp'] + local Varjk=r(Var) + drop `centrj' `centrk' `diffjk' + local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk' + if `ev'<`minegenval' { + local minegenval=`ev' + local minj `j' + local mink `k' + local eigen=0 + local eigen2=0 + } + } + else { + 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 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" + qui graph twoway (bar v3 id, name(bar,replace) `fsb' 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'"!="" { + *matrix list clv + qui cluster dendro clv, name (dendrogram,replace) `fsd' hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0) labsize(`textsize')) `cut' + } + else { + qui cluster dendro clv, name(dendrogram,replace) `fsd' xtitle("`var'") `showcount' ytitle("`titleL'") title("`title'",span) ylabel(`yl') xlabel(,labsize(`textsize')) `cut' + } + if "`savedendro'"!="" { + qui 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 qui biplotvlab `latent' `add', name(biplot,replace) `fsbi' 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" +if "`save'"!="" { + qui matrix `save'_vp=`vp' + qui matrix `save'_matclus=`matclus' + qui global `save'_varlist `varlist' + qui global `save'_method `method' + qui global `save'_kernel `kernel' +} + +return matrix vp=`vp' +return matrix matclus=`matclus' +return local varlist `varlist' +return local method `method' +return local kernel `kernel' +restore,not +end diff --git a/Modules/ado/personal/c/clv.sthlp b/Modules/ado/personal/c/clv.sthlp new file mode 100644 index 0000000..e402a26 --- /dev/null +++ b/Modules/ado/personal/c/clv.sthlp @@ -0,0 +1,122 @@ +{smcl} +{* 29 juillet 2019}{* version 2.17}{...} +{hline} +help for {hi:clv}{right:Jean-Benoit Hardouin} +{hline} + +{title:Clustering around latent variables } + +{p 8 14 2}{cmd:clv} [{it:varlist}] [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:weight}] +[{cmd:,} {cmdab:nostand:ardized} {cmdab:ker:nel}({it:numlist}) {cmdab:meth:od}({it:keyword}) {cmdab:cons:olidation}({it:#}) {cmd:genlv}(string) {cmdab:rep:lace} +{cmdab:noden:dro} {cmdab:saved:endro}({it:filename}[,replace]) {cmdab:cut:number}({it:#}) {cmdab:show:count} {cmdab:texts:ize}({it:string}) {cmdab:deltaT} +{cmdab:hor:izontal} {cmdab:abb:rev}({it:#}) {cmdab:tit:le}({it:string}) {cmdab:cap:tion}({it:string}) +{cmdab:bar} {cmdab:nobip:lot} {cmdab:add:var} {cmd:std} {cmd:dim}({it:string}) {cmdab:files:ave} {cmdab:dirs:ave}({it:string})] + + + + +{title:Description} + +{p 4 8 2}{cmd:clv} clusters variables around latent components. The variables are clustered by +seeking to minimize at each step the decrease of the T criterion, computed as the sum of the +first eigenvalues of the matrices of data of all the clusters. A hierarchical cluster analysis +based on this criterion is performed. A iterative consolidation procedure can be subsequently run which +allows each variable to be assigned to the latent component it is the most correlated with. + +{title:Options} + +{p 0 8 2}{cmd:Options concerning the method CLV} + +{p 4 8 2}{cmd:nostandardized} uses centered variables instead of standardized variables. + +{p 4 8 2}{cmd:kernel} defines one or several kernels of variables (variables which are clustered together in an initial step). The first number #k1 indicates that the first #k1 variables are clustered together, the second number #k2 indicates that the following #k2 variables are clustered together... + +{p 4 8 2}{cmd:method} indicates the method to cluster the variables among {it:classical} (by default) for the method described by Vigneau and Qannari, + {it:polychoric} for a use of the matrix of polychoric coefficients of correlation (instead of Pearson coefficients of correlation), {it:v2} for a modified + algorithm wich search to minimize the maximum second eigenvalue among the clusters of 2 variables and more, {it:polychoricv2} which correspond to the {it:v2} + option with the matrix of polychoric coefficients of correlation, and {it:centroid} which is defined by Vigneau and Qannari as an adaptation of CLV when + the sign of the correlation coefficients between the variables is important. + +{p 4 8 2}{cmd:consolidation} performs a consolidation procedure with the obtained partition into the specified number of clusters (by default, no consolidation procedure is performed). + +{p 4 8 2}{cmd:genlv} saves the latent variables in new variables with the defined string as prefix (followed by a number). This option must be used in conjonction with the {cmd:consolidation} option. + +{p 4 8 2}{cmd:replace} allows replacing the created variables with the {cmd:genlv} option if they already exist. + +{p 0 8 2}{cmd:Options concerning the drawing of the dendrogram} + +{p 4 8 2}{cmd:nodendro} avoids to display of the dendrogram. + +{p 4 8 2}{cmd:savedendro} saves the dendrogram in the file defined by this option. If this file already exists, it is possible to replace it with the {cmd:replace} option. + +{p 4 8 2}{cmd:cutnumber} defines the maximal number of clusters displayed in the dendrogram (40 by default). + +{p 4 8 2}{cmd:showcount} displays the number of variables in each cluster (useful with the {cmd:cutnumber} option). + +{p 4 8 2}{cmd:textsize} defines the size of the labels of the variables on the dendrogram (see {help textsizestyle}). + +{p 4 8 2}{cmd:deltaT} uses the variation of the T criterion as height variable for the dendrogram. + +{p 4 8 2}{cmd:horizontal} displays an horizontal (instead a vertical) dendrogram. + +{p 4 8 2}{cmd:abbrev} defines the length of the variables labels on the dendrogram (15 characters by default). + +{p 4 8 2}{cmd:title} defines the title of the dendrogram. + +{p 4 8 2}{cmd:caption} defines the caption of the axis of the dendrogram which indicates the names of the variables. + +{p 0 8 2}{cmd:Options concerning the others graphs} + +{p 4 8 2}{cmd:bar} displays a chart of the decrease in the T criterion at each step. + +{p 4 8 2}{cmd:nobiplot} avoids to display a biplot of the latent variables with the {cmd:consolidation} option. + +{p 4 8 2}{cmd:addvar} allows drawing the items on the graphical representation on the biplot. + +{p 4 8 2}{cmd:std} allows standardizing the latent variables for the graphical representation on the biplot. + +{p 4 8 2}{cmd:dim}({it:string}) allows choosing the axes represented on the biplot. + +{p 4 8 2}{cmd:filesave} allows saving the graphs in gph files on the default directory or on the directory defined by the {cmd:dirsave} option. + +{p 4 8 2}{cmd:dirsave}({it:string}) allows determining the directory to save the graphs (usefull with the {cmd:filesave} option). + +{p 4 8 2} If no {it:varlist} is indicated, the procedure uses the varlist from the last {cmd:clv} procedure, but does not perform the hierarchical cluster analysis. + +{title:Notes} + +{p 4 8 2} The classifications around latent variables (CLV) is defined by its authors (Vigneau and Qannari, 2003) only for continuous variables. Results with binary or ordinal variables must be interpreted with precautions. + +{p 4 8 2} Only {cmd:fweights} are allowed. The biplots are disabled if weights are used. + +{p 4 8 2} In this procedure, all the individuals with at least one missing value are omitted. + +{p 4 8 2} With the {it:polychoric} and {it:polychoricv2} methods, the {cmd:nostandardized} option is disabled. + +{p 4 8 2} This module uses the following modules downloadable on SSC: {stata ssc describe polychoric}, {stata ssc describe biplotvlab} and {stata ssc describe genscore} + +{title:Example} + + {p 4 8 2}{cmd:. clv var1-var15} /*performs the HCA procedure*/ + + {p 4 8 2}{cmd:. clv var1-var15, cons(6) bar nodendro meth(centroid)} /* performs the HCA procedure based on the centroid method followed by a consolidation procedure with 6 clusters*/ + + {p 4 8 2}{cmd:. clv, cons(3) addvar} /*performs only the consolidation procedure with 3 clusters, based on the preceeding HCA procedure*/ + +{title:Aknowledgements} + +{p 4 8 2} The author thanks Ronan Conroy for all the propositions of improvements. + +{title:Reference} + +{p 4 8 2} Vigneau E. and Qannari E. M. Clustering of variables around latent components. Communications in Statistics - Simulation and Computation. 32(4): 1131-1150, 2003. + +{title:Author} + +{p 4 8 2}Jean-Benoit Hardouin, PhD, assistant professor{p_end} +{p 4 8 2}INSERM UMR 1246-SPHERE "MethodS in Patients-centered outcomes and HEalth ResEarch"{p_end} +{p 4 8 2}Nantes University - University of Tours{p_end} +{p 4 8 2}Institute for Research in Health 2 (IRS2), Nantes, France{p_end} +{p 4 8 2}Email: +{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}{p_end} +{p 4 8 2}Website {browse "http://www.anaqol.org":AnaQol} diff --git a/Modules/ado/personal/c/clv212.ado b/Modules/ado/personal/c/clv212.ado new file mode 100644 index 0000000..35d7f79 --- /dev/null +++ b/Modules/ado/personal/c/clv212.ado @@ -0,0 +1,907 @@ +*! Version 2.12 1December2006 +*! Jean-Benoit Hardouin +************************************************************************************************************ +* Stata program : clv +* Clustering of variables around latent variables +* Version 2.12 : December 1st, 2006 /*savedendro option*/ +* +* Historic +* Version 1 (2005-06-11): Jean-Benoit Hardouin +* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/ +* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/ +* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/ +* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/ +* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/ +* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/ +* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/ +* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/ +* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/ +* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/ +* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/ +* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/ +* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/ +* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/ +* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/ +* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/ +* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/ +* +* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences +* Department of Biostatistics - France +* jean-benoit.hardouin@univ-nantes.fr +* +* News about this program : http://anaqol.free.fr +* FreeIRT Project : http://freeirt.free.fr +* +* Copyright 2005-2006 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 clv,rclass +version 9.0 +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)] +preserve +tempfile clvfile +tempvar id +gen `id'=_n +qui save `clvfile',replace +local matsize=c(matsize) +local none=0 +if "`varlist'"=="" { + capture confirm matrix r(vp) + if _rc==0 { + capture confirm matrix r(matclus) + if _rc ==0 { + local none=1 + } + } + if `none'==0 { + di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}" + error 198 + exit + } +} + +tempname matclus vp indexes + +/*********TESTS**********/ + +if `none'==1 { + matrix `vp'=r(vp) + matrix `matclus'=r(matclus) + local varlist `r(varlist)' + tokenize `varlist' + local nbitems=rowsof(`matclus') + if "`method'"!="" { + di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted." + } + local method `r(method)' + local kernel `r(kernel)' +} +if "`method'"=="" { + local method classical +} +if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" { + di in green "Initial variables are used with the {hi:polychoric} methods" + di in green "But the procedure is based on the matrix of the polychoric correlations" + di +} +if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" { + di in red "The {hi:method} `method' is unknown" + error 198 + exit +} + +tokenize `varlist' +local nbitems : word count `varlist' +marksample touse +qui keep if `touse' + +local mat=max(`matsize',`=`nbitems'*2') +qui set matsize `mat' + +if `nbitems'<3&`none'!=1 { + di in red "You need at least 3 variables" + error 198 + exit +} + + +/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/ +forvalues i=1/`nbitems'{ + local label`i':variable label ``i'' + if "`label`i''"=="" { + local label`i' ``i'' + } + if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + qui su ``i'' [`weight'`exp'] + local mean=r(mean) + if "`standardized'"=="" { + local sd=r(sd) + } + else { + local sd=1 + } + qui replace ``i''=(``i''-`mean')/`sd' + } +} + +tempfile clvfiletmp +qui save `clvfiletmp',replace + +qui su `1' [`weight'`exp'] +local nbind=r(sum_w) + + +local cons=`consolidation' + +/*COMPUTES THE TOTAL VARIANCE*/ + +if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + local totvar=0 + forvalues i=1/`nbitems' { + qui su ``i'' [`weight'`exp'] + local totvar=`totvar'+`r(Var)' + } +} +else { + local totvar `nbitems' +} +local nbkerk=0 +local nbkerg=0 + +/***** DEFINES THE KERNEL IF NECESSARY ********/ + +if "`kernel'"!="" { + local nbkerg:word count `kernel' + local fin0=0 + forvalues i=1/`nbkerg' { + local nbi`i':word `i' of `kernel' + local nbkerk=`nbkerk'+`nbi`i'' + local deb`i'=`fin`=`i'-1''+1 + local fin`i'=`deb`i''+`nbi`i''-1 + local list`i' + forvalues j=`deb`i''/`fin`i'' { + local list`i' `list`i'' ``j'' + } + } + tempname kerclus + matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0) + local ligne=1 + forvalues g=1/`nbkerg' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g'' + matrix `kerclus'[`ligne',3]=`deb`g''+1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + if `nbi`g''>2 { + forvalues i=2/`=`nbi`g''-1' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g''+`i' + matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + } + } + } +} +if `nbitems'<`nbkerk' { + di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}" + error 198 + exit +} + +/*******DISPLAY THE FIRST RESULTS *******/ + +di +di in green "{hline 32}" +di in green "TOTAL VARIANCE: " in ye %16.5f `totvar' +di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind' +di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'" +di in green "{hline 32}" +di + +if "`kernel'"!="" { + forvalues i=1/`nbkerg' { + di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''" + di + } +} +else { + local nbkerk=0 + local nbkerg=0 +} + +/******** CLASSIFICATION PROCEDURE*******/ + +tempname Ev +if `none'!=1 { + matrix `matclus'=J(`nbitems',`nbitems',0) + matrix `vp'=J(`=2*`nbitems'-1',12,0) + matrix `indexes'=J(`nbitems',8,0) + forvalues i=1/`nbitems' { + matrix `matclus'[`i',1]=`i' + if "`method'"!="polychoric"&"`method'"!="polychoric" { + qui su ``i'' [`weight'`exp'] + matrix `vp'[`i',10]=r(Var) + } + else { + matrix `vp'[`i',10]=1 + } + matrix `vp'[`i',1]=`i' + matrix `vp'[`i',2]=`nbitems' + matrix `vp'[`i',8]=`totvar' + matrix `vp'[`i',9]=100 + } + matrix `vp'[`nbitems',5]=`nbitems' + if "`method'"=="centroid" { + local crit G + di in green "{hline 101}" + di in green _col(93) "2nd order" + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative" + 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" + di in green "{hline 101}" + } + else { + local crit T + di in green "{hline 111}" + if "`method'"=="v2"|"`method'"=="polychoricv2" { + di in green _col(84) "Maximal" _col(103) "2nd order" + } + else { + di in green _col(84) "Current" _col(103) "2nd order" + } + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative" + 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" + di in green "{hline 111}" + } + tempname threshold + matrix `threshold'=J(`nbitems',3,0) + forvalues i=1/`=`nbitems'-1' { + local clus=`nbitems'+`i' + local minegenval=999999 + local minegenval2=999999 + forvalues k=1/`=`clus'-1' { + local list`k' + local numlist`k' + forvalues j=1/`clus' { + if (`matclus'[`j',`i']==`k') { + local list`k' `list`k'' ``j'' + local numlist`k' `numlist`k'' `j' + } + } + } + if `clus'>`nbitems'+`nbkerk'-`nbkerg' { + if "`method'"=="centroid" { + tempname centrj centrk diffjk + } + forvalues j=1/`clus' { + local nblistj:word count `list`j'' + forvalues k=`=`j'+1'/`clus' { + local nblistk:word count `list`k'' + if `nblistj'!=0&`nblistk'!=0 { + if "`method'"=="centroid" { + qui genscore `list`j'',score(`centrj') mean + qui su `centrj' [`weight'`exp'] + local Varj=r(Var) + qui genscore `list`k'',score(`centrk') mean + qui su `centrk' [`weight'`exp'] + local Vark=r(Var) + qui gen `diffjk'=`centrk'-`centrj' + qui su `diffjk' [`weight'`exp'] + local Varjk=r(Var) + drop `centrj' `centrk' `diffjk' + local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk' + if `ev'<`minegenval' { + local minegenval=`ev' + local minj `j' + local mink `k' + local eigen=0 + local eigen2=0 + } + } + else { + 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 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' + } + 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') + set matsize `max' + if "`addvar'"!="" { + local add `varlist' + } + 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) + } + 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********/ + +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 diff --git a/Modules/ado/personal/c/clv214.ado b/Modules/ado/personal/c/clv214.ado new file mode 100644 index 0000000..bcb4db5 --- /dev/null +++ b/Modules/ado/personal/c/clv214.ado @@ -0,0 +1,916 @@ +*! Version 2.14 20May2010 +*! Jean-Benoit Hardouin +************************************************************************************************************ +* Stata program : clv +* Clustering of variables around latent variables +* Version 2.14 : May 20th, 2010 /*dim and std options for biplots*/ +* +* Historic +* Version 1 (2005-06-11): Jean-Benoit Hardouin +* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/ +* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/ +* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/ +* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/ +* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/ +* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/ +* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/ +* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/ +* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/ +* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/ +* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/ +* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/ +* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/ +* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/ +* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/ +* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/ +* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/ +* Version 2.13 (2010-05-12): Jean-Benoit Hardouin /*corrections of bugs in KERNEL option and with METHOD(centroid)*/ +* Version 2.14 (2010-05-20): Jean-Benoit Hardouin /*DIM and STD options for biplots*/ +* +* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences +* Department of Biostatistics - France +* jean-benoit.hardouin@univ-nantes.fr +* +* News about this program : http://anaqol.free.fr +* FreeIRT Project : http://freeirt.free.fr +* +* Copyright 2005-2006, 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 clv,rclass +version 9.0 +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)] +preserve +tempfile clvfile +tempvar id +gen `id'=_n +qui save `clvfile',replace +local matsize=c(matsize) +local none=0 +if "`varlist'"=="" { + capture confirm matrix r(vp) + if _rc==0 { + capture confirm matrix r(matclus) + if _rc ==0 { + local none=1 + } + } + if `none'==0 { + di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}" + error 198 + exit + } +} + +tempname matclus vp indexes + +/*********TESTS**********/ + +if `none'==1 { + matrix `vp'=r(vp) + matrix `matclus'=r(matclus) + local varlist `r(varlist)' + tokenize `varlist' + local nbitems=rowsof(`matclus') + if "`method'"!="" { + di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted." + } + local method `r(method)' + local kernel `r(kernel)' +} +if "`method'"=="" { + local method classical +} +if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" { + di in green "Initial variables are used with the {hi:polychoric} methods" + di in green "But the procedure is based on the matrix of the polychoric correlations" + di +} +if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" { + di in red "The {hi:method} `method' is unknown" + error 198 + exit +} + +tokenize `varlist' +local nbitems : word count `varlist' +marksample touse +qui keep if `touse' + +local mat=max(`matsize',`=`nbitems'*2') +qui set matsize `mat' + +if `nbitems'<3&`none'!=1 { + di in red "You need at least 3 variables" + error 198 + exit +} + + +/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/ +forvalues i=1/`nbitems'{ + local label`i':variable label ``i'' + if "`label`i''"=="" { + local label`i' ``i'' + } + if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + qui su ``i'' [`weight'`exp'] + local mean=r(mean) + if "`standardized'"=="" { + local sd=r(sd) + } + else { + local sd=1 + } + qui replace ``i''=(``i''-`mean')/`sd' + } +} + +tempfile clvfiletmp +qui save `clvfiletmp',replace + +qui su `1' [`weight'`exp'] +local nbind=r(sum_w) + + +local cons=`consolidation' + +/*COMPUTES THE TOTAL VARIANCE*/ + +if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + local totvar=0 + forvalues i=1/`nbitems' { + qui su ``i'' [`weight'`exp'] + local totvar=`totvar'+`r(Var)' + } +} +else { + local totvar `nbitems' +} +local nbkerk=0 +local nbkerg=0 + +/***** DEFINES THE KERNEL IF NECESSARY ********/ + +if "`kernel'"!="" { + local nbkerg:word count `kernel' + local fin0=0 + forvalues i=1/`nbkerg' { + local nbi`i':word `i' of `kernel' + local nbkerk=`nbkerk'+`nbi`i'' + local deb`i'=`fin`=`i'-1''+1 + local fin`i'=`deb`i''+`nbi`i''-1 + local list`i' + forvalues j=`deb`i''/`fin`i'' { + local list`i' `list`i'' ``j'' + } + } + tempname kerclus + matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0) + local ligne=1 + forvalues g=1/`nbkerg' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g'' + matrix `kerclus'[`ligne',3]=`deb`g''+1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + if `nbi`g''>2 { + forvalues i=2/`=`nbi`g''-1' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g''+`i' + matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + } + } + local eigen2=0 + } +} +if `nbitems'<`nbkerk' { + di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}" + error 198 + exit +} + +/*******DISPLAY THE FIRST RESULTS *******/ + +di +di in green "{hline 32}" +di in green "TOTAL VARIANCE: " in ye %16.5f `totvar' +di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind' +di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'" +di in green "{hline 32}" +di + +if "`kernel'"!="" { + forvalues i=1/`nbkerg' { + di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''" + di + } +} +else { + local nbkerk=0 + local nbkerg=0 +} + +/******** CLASSIFICATION PROCEDURE*******/ + +tempname Ev +if `none'!=1 { + matrix `matclus'=J(`nbitems',`nbitems',0) + matrix `vp'=J(`=2*`nbitems'-1',12,0) + matrix `indexes'=J(`nbitems',8,0) + forvalues i=1/`nbitems' { + matrix `matclus'[`i',1]=`i' + if "`method'"!="polychoric"&"`method'"!="polychoric" { + qui su ``i'' [`weight'`exp'] + matrix `vp'[`i',10]=r(Var) + } + else { + matrix `vp'[`i',10]=1 + } + matrix `vp'[`i',1]=`i' + matrix `vp'[`i',2]=`nbitems' + matrix `vp'[`i',8]=`totvar' + matrix `vp'[`i',9]=100 + } + matrix `vp'[`nbitems',5]=`nbitems' + if "`method'"=="centroid" { + local crit G + di in green "{hline 101}" + di in green _col(93) "2nd order" + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative" + 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" + di in green "{hline 101}" + } + else { + local crit T + di in green "{hline 111}" + if "`method'"=="v2"|"`method'"=="polychoricv2" { + di in green _col(84) "Maximal" _col(103) "2nd order" + } + else { + di in green _col(84) "Current" _col(103) "2nd order" + } + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative" + 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" + di in green "{hline 111}" + } + tempname threshold + matrix `threshold'=J(`nbitems',3,0) + forvalues i=1/`=`nbitems'-1' { + local clus=`nbitems'+`i' + local minegenval=999999 + local minegenval2=999999 + forvalues k=1/`=`clus'-1' { + local list`k' + local numlist`k' + forvalues j=1/`clus' { + if (`matclus'[`j',`i']==`k') { + local list`k' `list`k'' ``j'' + local numlist`k' `numlist`k'' `j' + } + } + } + if `clus'>`nbitems'+`nbkerk'-`nbkerg' { + if "`method'"=="centroid" { + tempname centrj centrk diffjk + } + forvalues j=1/`clus' { + local nblistj:word count `list`j'' + forvalues k=`=`j'+1'/`clus' { + local nblistk:word count `list`k'' + if `nblistj'!=0&`nblistk'!=0 { + if "`method'"=="centroid" { + qui genscore `list`j'',score(`centrj') mean + qui su `centrj' [`weight'`exp'] + local Varj=r(Var) + qui genscore `list`k'',score(`centrk') mean + qui su `centrk' [`weight'`exp'] + local Vark=r(Var) + qui gen `diffjk'=`centrk'-`centrj' + qui su `diffjk' [`weight'`exp'] + local Varjk=r(Var) + drop `centrj' `centrk' `diffjk' + local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk' + if `ev'<`minegenval' { + local minegenval=`ev' + local minj `j' + local mink `k' + local eigen=0 + local eigen2=0 + } + } + else { + 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 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 diff --git a/Modules/ado/personal/c/clv215.ado b/Modules/ado/personal/c/clv215.ado new file mode 100644 index 0000000..8839023 --- /dev/null +++ b/Modules/ado/personal/c/clv215.ado @@ -0,0 +1,946 @@ +*! Version 2.15 14April2014 +*! Jean-Benoit Hardouin +************************************************************************************************************ +* Stata program : clv +* Clustering of variables around latent variables +* Version 2.14 : May 20th, 2010 /*dim and std options for biplots*/ +* +* Historic +* Version 1 (2005-06-11): Jean-Benoit Hardouin +* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/ +* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/ +* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/ +* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/ +* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/ +* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/ +* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/ +* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/ +* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/ +* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/ +* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/ +* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/ +* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/ +* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/ +* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/ +* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/ +* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/ +* Version 2.13 (2010-05-12): Jean-Benoit Hardouin /*corrections of bugs in KERNEL option and with METHOD(centroid)*/ +* Version 2.14 (2010-05-20): Jean-Benoit Hardouin /*DIM and STD options for biplots*/ +* Version 2.15 (2014-04-14): Jean-Benoit Hardouin /*save and use options*/ +* +* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences +* Department of Biostatistics - France +* jean-benoit.hardouin@univ-nantes.fr +* +* News about this program : http://anaqol.sphere-nantes.fr +* +* Copyright 2005-2006, 2010, 2014 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 clv215,rclass +version 9.0 +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) save(string) use(string)] +preserve +tempfile clvfile +tempvar id +gen `id'=_n +qui save `clvfile',replace +local matsize=c(matsize) +local none=0 +if "`varlist'"==""&"`use'"=="" { + capture confirm matrix r(vp) + if _rc==0 { + capture confirm matrix r(matclus) + if _rc ==0 { + local none=1 + } + } + if `none'==0 { + di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}" + error 198 + exit + } +} + +tempname matclus vp indexes + +/*********TESTS**********/ + +if "`use'"!="" { + local error=0 + capture matrix `vp'=`use'_vp + if _rc!=0 { + local error=_rc + } + capture matrix `matclus'=`use'_matclus + if _rc!=0 { + local error=_rc + } + local varlist $`use'_varlist + local method $`use'_method + local kernel $`use'_kernel + if "`varlist'"==""|"`method'"=="" { + local error=1 + } + if `error'!=0 { + di in red "You cannot use the {hi:use} option without a preliminary use of the {hi:save} option" + error 198 + exit + } +} +if `none'==1 { + matrix `vp'=r(vp) + matrix `matclus'=r(matclus) + local varlist `r(varlist)' + tokenize `varlist' + local nbitems=rowsof(`matclus') + if "`method'"!="" { + di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted." + } + local method `r(method)' + local kernel `r(kernel)' +} +if "`method'"=="" { + local method classical +} +if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" { + di in green "Initial variables are used with the {hi:polychoric} methods" + di in green "But the procedure is based on the matrix of the polychoric correlations" + di +} +if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" { + di in red "The {hi:method} `method' is unknown" + error 198 + exit +} + +tokenize `varlist' +local nbitems : word count `varlist' +marksample touse +qui keep if `touse' + +local mat=max(`matsize',`=`nbitems'*2') +qui set matsize `mat' + +if `nbitems'<3&`none'!=1 { + di in red "You need at least 3 variables" + error 198 + exit +} + + +/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/ +forvalues i=1/`nbitems'{ + local label`i':variable label ``i'' + if "`label`i''"=="" { + local label`i' ``i'' + } + if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + qui su ``i'' [`weight'`exp'] + local mean=r(mean) + if "`standardized'"=="" { + local sd=r(sd) + } + else { + local sd=1 + } + qui replace ``i''=(``i''-`mean')/`sd' + } +} + +tempfile clvfiletmp +qui save `clvfiletmp',replace + +qui su `1' [`weight'`exp'] +local nbind=r(sum_w) + + +local cons=`consolidation' + +/*COMPUTES THE TOTAL VARIANCE*/ + +if "`method'"!="polychoric"&"`method'"!="polychoricv2" { + local totvar=0 + forvalues i=1/`nbitems' { + qui su ``i'' [`weight'`exp'] + local totvar=`totvar'+`r(Var)' + } +} +else { + local totvar `nbitems' +} +local nbkerk=0 +local nbkerg=0 + +/***** DEFINES THE KERNEL IF NECESSARY ********/ + +if "`kernel'"!="" { + local nbkerg:word count `kernel' + local fin0=0 + forvalues i=1/`nbkerg' { + local nbi`i':word `i' of `kernel' + local nbkerk=`nbkerk'+`nbi`i'' + local deb`i'=`fin`=`i'-1''+1 + local fin`i'=`deb`i''+`nbi`i''-1 + local list`i' + forvalues j=`deb`i''/`fin`i'' { + local list`i' `list`i'' ``j'' + } + } + tempname kerclus + matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0) + local ligne=1 + forvalues g=1/`nbkerg' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g'' + matrix `kerclus'[`ligne',3]=`deb`g''+1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + if `nbi`g''>2 { + forvalues i=2/`=`nbi`g''-1' { + matrix `kerclus'[`ligne',1]=`nbitems'+`ligne' + matrix `kerclus'[`ligne',2]=`deb`g''+`i' + matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1 + local clus`g'=`nbitems'+`ligne' + local ligne=`ligne'+1 + } + } + local eigen2=0 + } +} +if `nbitems'<`nbkerk' { + di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}" + error 198 + exit +} + +/*******DISPLAY THE FIRST RESULTS *******/ + +di +di in green "{hline 32}" +di in green "TOTAL VARIANCE: " in ye %16.5f `totvar' +di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind' +di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'" +di in green "{hline 32}" +di + +if "`kernel'"!="" { + forvalues i=1/`nbkerg' { + di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''" + di + } +} +else { + local nbkerk=0 + local nbkerg=0 +} + +/******** CLASSIFICATION PROCEDURE*******/ + +tempname Ev +if `none'!=1 { + matrix `matclus'=J(`nbitems',`nbitems',0) + matrix `vp'=J(`=2*`nbitems'-1',12,0) + matrix `indexes'=J(`nbitems',8,0) + forvalues i=1/`nbitems' { + matrix `matclus'[`i',1]=`i' + if "`method'"!="polychoric"&"`method'"!="polychoric" { + qui su ``i'' [`weight'`exp'] + matrix `vp'[`i',10]=r(Var) + } + else { + matrix `vp'[`i',10]=1 + } + matrix `vp'[`i',1]=`i' + matrix `vp'[`i',2]=`nbitems' + matrix `vp'[`i',8]=`totvar' + matrix `vp'[`i',9]=100 + } + matrix `vp'[`nbitems',5]=`nbitems' + if "`method'"=="centroid" { + local crit G + di in green "{hline 101}" + di in green _col(93) "2nd order" + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative" + 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" + di in green "{hline 101}" + } + else { + local crit T + di in green "{hline 111}" + if "`method'"=="v2"|"`method'"=="polychoricv2" { + di in green _col(84) "Maximal" _col(103) "2nd order" + } + else { + di in green _col(84) "Current" _col(103) "2nd order" + } + di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative" + 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" + di in green "{hline 111}" + } + tempname threshold + matrix `threshold'=J(`nbitems',3,0) + forvalues i=1/`=`nbitems'-1' { + local clus=`nbitems'+`i' + local minegenval=999999 + local minegenval2=999999 + forvalues k=1/`=`clus'-1' { + local list`k' + local numlist`k' + forvalues j=1/`clus' { + if (`matclus'[`j',`i']==`k') { + local list`k' `list`k'' ``j'' + local numlist`k' `numlist`k'' `j' + } + } + } + if `clus'>`nbitems'+`nbkerk'-`nbkerg' { + if "`method'"=="centroid" { + tempname centrj centrk diffjk + } + forvalues j=1/`clus' { + local nblistj:word count `list`j'' + forvalues k=`=`j'+1'/`clus' { + local nblistk:word count `list`k'' + if `nblistj'!=0&`nblistk'!=0 { + if "`method'"=="centroid" { + qui genscore `list`j'',score(`centrj') mean + qui su `centrj' [`weight'`exp'] + local Varj=r(Var) + qui genscore `list`k'',score(`centrk') mean + qui su `centrk' [`weight'`exp'] + local Vark=r(Var) + qui gen `diffjk'=`centrk'-`centrj' + qui su `diffjk' [`weight'`exp'] + local Varjk=r(Var) + drop `centrj' `centrk' `diffjk' + local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk' + if `ev'<`minegenval' { + local minegenval=`ev' + local minj `j' + local mink `k' + local eigen=0 + local eigen2=0 + } + } + else { + 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 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" +if "`save'"!="" { + qui matrix `save'_vp=`vp' + qui matrix `save'_matclus=`matclus' + qui global `save'_varlist `varlist' + qui global `save'_method `method' + qui global `save'_kernel `kernel' +} + +return matrix vp=`vp' +return matrix matclus=`matclus' +return local varlist `varlist' +return local method `method' +return local kernel `kernel' +restore,not +end diff --git a/Modules/ado/personal/c/clv216.ado b/Modules/ado/personal/c/clv216.ado new file mode 100644 index 0000000..d4fb838 --- /dev/null +++ b/Modules/ado/personal/c/clv216.ado @@ -0,0 +1,1005 @@ +*! Version 2.16 30April2014 +*! Jean-Benoit Hardouin +************************************************************************************************************ +* Stata program : clv +* Clustering of variables around latent variables +* Version 2.14 : May 20th, 2010 /*dim and std options for biplots*/ +* +* Historic +* Version 1 (2005-06-11): Jean-Benoit Hardouin +* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/ +* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/ +* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/ +* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/ +* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/ +* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/ +* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/ +* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/ +* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/ +* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/ +* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/ +* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/ +* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/ +* Version 2.9 (2006-01-26): Jean-Benoit Hardouin /*save the latent variables*/ +* Version 2.10 (2006-07-10): Jean-Benoit Hardouin /*2nd order relative variation of the T criterion*/ +* Version 2.11 (2006-10-09): Jean-Benoit Hardouin /*Size of the text in the dendrogram*/ +* Version 2.12 (2006-12-01): Jean-Benoit Hardouin /*savedendro option*/ +* Version 2.13 (2010-05-12): Jean-Benoit Hardouin /*corrections of bugs in KERNEL option and with METHOD(centroid)*/ +* Version 2.14 (2010-05-20): Jean-Benoit Hardouin /*DIM and STD options for biplots*/ +* Version 2.15 (2014-04-14): Jean-Benoit Hardouin /*save and use options*/ +* Version 2.16 (2014-04-30): Jean-Benoit Hardouin, Bastien Perrot /*HTML option*/ +* +* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences +* Department of Biostatistics - France +* jean-benoit.hardouin@univ-nantes.fr +* +* News about this program : http://anaqol.sphere-nantes.fr +* +* Copyright 2005-2006, 2010, 2014 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 clv216,rclass +version 9.0 +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) save(string) use(string) HTML(string)] +preserve +tempfile clvfile +tempvar id +gen `id'=_n +qui save `clvfile',replace + +if "`if'"!="" { + qui keep `if' +} + +local matsize=c(matsize) +local none=0 +if "`varlist'"==""&"`use'"=="" { + capture confirm matrix r(vp) + if _rc==0 { + capture confirm matrix r(matclus) + if _rc ==0 { + local none=1 + } + } + if `none'==0 { + di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}" + error 198 + exit + } +} + +if "`html'"!="" { + //set scheme sj + //local htmlregion "graphregion(fcolor(white) ifcolor(white))" + di "" + di "
"
+}
+
+
+tempname matclus vp indexes
+
+/*********TESTS**********/
+
+if "`use'"!="" {
+   local error=0
+   capture matrix `vp'=`use'_vp
+   if _rc!=0 {
+      local error=_rc
+   }
+   capture matrix `matclus'=`use'_matclus
+   if _rc!=0 {
+      local error=_rc
+   }
+   local varlist $`use'_varlist
+   local method $`use'_method
+   local kernel $`use'_kernel
+   if "`varlist'"==""|"`method'"=="" {
+      local error=1
+   }
+   if `error'!=0 {
+      di in red "You cannot use the {hi:use} option without a preliminary use of the {hi:save} option"
+      error 198
+      exit
+   }
+}
+if `none'==1 {
+   matrix `vp'=r(vp)
+   matrix `matclus'=r(matclus)
+   local varlist `r(varlist)'
+   tokenize `varlist'
+   local nbitems=rowsof(`matclus')
+   if "`method'"!="" {
+      di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted."
+   }
+   local method `r(method)'
+   local kernel `r(kernel)'
+}
+if "`method'"=="" {
+   local method classical
+}
+if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" {
+   di in green "Initial variables are used with the {hi:polychoric} methods"
+   di in green "But the procedure is based on the matrix of the polychoric correlations"
+   di
+}
+if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   di in red "The {hi:method} `method' is unknown"
+   error 198
+   exit
+}
+
+tokenize `varlist'
+local nbitems : word count `varlist'
+marksample touse
+qui keep if `touse'
+
+local mat=max(`matsize',`=`nbitems'*2')
+qui set matsize `mat'
+
+if `nbitems'<3&`none'!=1 {
+   di in red "You need at least 3 variables"
+   error 198
+   exit
+}
+
+
+/*******DEFINES THE LABELS AND STANDARDIZED THE VARIABLES (IF NECESSARY)*******/
+forvalues i=1/`nbitems'{
+   local label`i':variable label ``i''
+   if "`label`i''"=="" {
+      local label`i' ``i''
+   }
+   if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+      qui su ``i'' [`weight'`exp']
+      local mean=r(mean)
+      if "`standardized'"=="" {
+         local sd=r(sd)
+      }
+      else {
+         local sd=1
+      }
+      qui replace ``i''=(``i''-`mean')/`sd'
+   }
+}
+
+tempfile clvfiletmp
+qui save `clvfiletmp',replace
+
+qui su `1' [`weight'`exp']
+local nbind=r(sum_w)
+
+
+local cons=`consolidation'
+
+/*COMPUTES THE TOTAL VARIANCE*/
+
+if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   local totvar=0
+   forvalues i=1/`nbitems' {
+      qui su ``i'' [`weight'`exp']
+      local totvar=`totvar'+`r(Var)'
+   }
+}
+else {
+   local totvar `nbitems'
+}
+local nbkerk=0
+local nbkerg=0
+
+/***** DEFINES THE KERNEL IF NECESSARY ********/
+
+if "`kernel'"!="" {
+   local nbkerg:word count `kernel'
+   local fin0=0
+   forvalues i=1/`nbkerg' {
+      local nbi`i':word `i' of `kernel'
+      local nbkerk=`nbkerk'+`nbi`i''
+      local deb`i'=`fin`=`i'-1''+1
+      local fin`i'=`deb`i''+`nbi`i''-1
+      local list`i'
+      forvalues j=`deb`i''/`fin`i'' {
+         local list`i' `list`i'' ``j''
+      }
+   }
+   tempname kerclus
+   matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0)
+   local ligne=1
+   forvalues g=1/`nbkerg' {
+       matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+       matrix `kerclus'[`ligne',2]=`deb`g''
+       matrix `kerclus'[`ligne',3]=`deb`g''+1
+       local clus`g'=`nbitems'+`ligne'
+       local ligne=`ligne'+1
+       if `nbi`g''>2 {
+          forvalues i=2/`=`nbi`g''-1' {
+             matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+             matrix `kerclus'[`ligne',2]=`deb`g''+`i'
+             matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1
+             local clus`g'=`nbitems'+`ligne'
+             local ligne=`ligne'+1
+          }
+      }
+      local eigen2=0
+   }
+}
+if `nbitems'<`nbkerk' {
+   di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}"
+   error 198
+   exit
+}
+
+/*******DISPLAY THE FIRST RESULTS *******/
+
+di
+di in green "{hline 32}"
+di in green "TOTAL VARIANCE: " in ye %16.5f `totvar'
+di in green "NUMBER OF INDIVIDUALS: " in ye %9.0f `nbind'
+di in green "METHOD:" in ye _col(`=33-length("`method'")') "`=upper("`method'")'"
+di in green "{hline 32}"
+di
+
+if "`kernel'"!="" {
+   forvalues i=1/`nbkerg' {
+      di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''"
+      di
+   }
+}
+else {
+   local nbkerk=0
+   local nbkerg=0
+}
+
+/******** CLASSIFICATION PROCEDURE*******/
+
+tempname Ev
+if `none'!=1 {
+   matrix `matclus'=J(`nbitems',`nbitems',0)
+   matrix `vp'=J(`=2*`nbitems'-1',12,0)
+   matrix `indexes'=J(`nbitems',8,0)
+   forvalues i=1/`nbitems' {
+      matrix `matclus'[`i',1]=`i'
+      if "`method'"!="polychoric"&"`method'"!="polychoric" {
+         qui su ``i'' [`weight'`exp']
+         matrix `vp'[`i',10]=r(Var)
+      }
+      else {
+         matrix `vp'[`i',10]=1
+      }
+      matrix `vp'[`i',1]=`i'
+      matrix `vp'[`i',2]=`nbitems'
+      matrix `vp'[`i',8]=`totvar'
+      matrix `vp'[`i',9]=100
+   }
+   matrix `vp'[`nbitems',5]=`nbitems'
+   if "`method'"=="centroid" {
+      local crit G
+      di in green "{hline 101}"
+      di in green _col(93) "2nd order"
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative" _col(94) "Relative"
+      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"
+      di in green "{hline 101}"
+   }
+   else {
+      local crit T
+      di in green "{hline 111}"
+      if "`method'"=="v2"|"`method'"=="polychoricv2" {
+         di in green _col(84) "Maximal" _col(103) "2nd order"
+      }
+      else {
+         di in green _col(84) "Current" _col(103) "2nd order"
+      }
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative" _col(104) "Relative"
+      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"
+      di in green "{hline 111}"
+   }
+   tempname threshold
+   matrix `threshold'=J(`nbitems',3,0)
+   forvalues i=1/`=`nbitems'-1' {
+      local clus=`nbitems'+`i'
+      local minegenval=999999
+      local minegenval2=999999
+      forvalues k=1/`=`clus'-1' {
+         local list`k'
+         local numlist`k'
+         forvalues j=1/`clus' {
+            if (`matclus'[`j',`i']==`k') {
+               local list`k' `list`k'' ``j''
+               local numlist`k' `numlist`k'' `j'
+            }
+         }
+      }
+      if `clus'>`nbitems'+`nbkerk'-`nbkerg' {
+         if "`method'"=="centroid" {
+            tempname centrj centrk diffjk
+         }
+         forvalues j=1/`clus' {
+            local nblistj:word count `list`j''
+            forvalues k=`=`j'+1'/`clus' {
+               local nblistk:word count `list`k''
+               if `nblistj'!=0&`nblistk'!=0 {
+                  if "`method'"=="centroid" {
+                     qui genscore `list`j'',score(`centrj') mean
+                     qui su `centrj' [`weight'`exp']
+                     local Varj=r(Var)
+                     qui genscore `list`k'',score(`centrk') mean
+                     qui su `centrk' [`weight'`exp']
+                     local Vark=r(Var)
+                     qui gen `diffjk'=`centrk'-`centrj'
+                     qui su `diffjk' [`weight'`exp']
+                     local Varjk=r(Var)
+                     drop `centrj' `centrk' `diffjk'
+                     local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk'
+                     if `ev'<`minegenval' {
+                        local minegenval=`ev'
+                        local minj `j'
+                        local mink `k'
+                        local eigen=0
+                        local eigen2=0
+                     }
+                  }
+                  else {
+                     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 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"
+   
+   if "`html'" != "" {
+		qui local saving "saving(`c(tmpdir)'/`html'_bar,replace) nodraw"
+		qui 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') `saving'
+		qui graph use `c(tmpdir)'/`html'_bar.gph
+		qui graph export `c(tmpdir)'/`html'_bar.eps, replace
+		di "
"
+		di " _char(34) "
+	}
+	else {
+		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'"!="" {
+	   if "`html'" != "" {
+			qui local saving "saving(`c(tmpdir)'/`html'_dendrogram,replace) nodraw"
+            qui cluster dendro clv,  name (dendrogram,replace) hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0)  labsize(`textsize')) `cut' `saving'
+			qui graph use `c(tmpdir)'/`html'_dendrogram.gph
+			qui graph export `c(tmpdir)'/`html'_dendrogram.eps, replace
+			di "
"
+			di " _char(34) "
+		}
+		else {
+            cluster dendro clv,  name (dendrogram,replace) hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0)  labsize(`textsize')) `cut'
+		}
+   }
+   else {
+	  if "`html'" != "" {
+			qui local saving "saving(`c(tmpdir)'/`html'_dendrogram,replace) nodraw"
+			qui cluster dendro clv, name(dendrogram,replace)  xtitle("`var'") `showcount' ytitle("`titleL'") title("`title'",span) ylabel(`yl') xlabel(,labsize(`textsize')) `cut' `saving'
+			qui graph use `c(tmpdir)'/`html'_dendrogram.gph
+			qui graph export `c(tmpdir)'/`html'_dendrogram.eps, replace
+			di "
"
+			di " _char(34) "
+		}
+		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
+            }
+			 if "`html'" != "" {
+				qui local saving "saving(`c(tmpdir)'/`html'_biplot,replace) nodraw"
+                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') `saving'
+				qui graph use `c(tmpdir)'/`html'_biplot.gph
+				qui graph export `c(tmpdir)'/`html'_biplot.eps, replace
+				di "
"
+				di " _char(34) "
+			}
+			else {
+				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"
+if "`save'"!="" {
+   qui matrix `save'_vp=`vp'
+   qui matrix `save'_matclus=`matclus'
+   qui global `save'_varlist `varlist'
+   qui global `save'_method `method'
+   qui global `save'_kernel `kernel'
+}
+
+return matrix vp=`vp'
+return matrix matclus=`matclus'
+return local varlist `varlist'
+return local method `method'
+return local kernel `kernel'
+restore,not
+end
diff --git a/Modules/ado/personal/c/clv27.ado b/Modules/ado/personal/c/clv27.ado
new file mode 100644
index 0000000..2b29ea0
--- /dev/null
+++ b/Modules/ado/personal/c/clv27.ado
@@ -0,0 +1,806 @@
+*! Version 2.7 6October2005
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* Stata program : clv
+* Clustering of variables around latent variables
+* Version 2.7 : October 6, 2005 /*return, multiple graphs, polychoric+consolidation*/
+*
+* Historic
+* Version 1 (2005-06-11): Jean-Benoit Hardouin
+* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/
+* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/
+* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/
+* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/
+* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/
+* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/
+* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/
+* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/
+* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/
+* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/
+* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/
+*
+* Jean-benoit Hardouin, Regional Health Observatory of Orléans - France
+* jean-benoit.hardouin@orscentre.org
+*
+* News about this program : http://anaqol.free.fr
+* FreeIRT Project : http://freeirt.free.fr
+*
+* Copyright 2005 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 clv,rclass
+version 9.0
+syntax [varlist(default=none)] [if] [in] [, CUTnumber(int 30) bar CONSolidation(int 0) noDENdro noSTANDardized deltaT HORizontal SHOWcount ABBrev(int 14) TITle(string) CAPtion(string) KERnel(numlist) METHod(string) noBIPlot ADDvar]
+preserve
+tempfile clvfile
+qui save `clvfile',replace
+local matsize=c(matsize)
+local none=0
+if "`varlist'"=="" {
+*set trace on
+   capture confirm matrix r(vp)
+   if _rc==0 {
+      capture confirm matrix r(matclus)
+      if _rc ==0 {
+         local none=1
+      }
+   }
+   if `none'==0 {
+      di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}"
+      error 198
+      exit
+   }
+}
+
+tempname matclus vp
+
+if `none'==1 {
+   matrix `vp'=r(vp)
+   matrix `matclus'=r(matclus)
+   local varlist `r(varlist)'
+   tokenize `varlist'
+   local nbitems=rowsof(`matclus')
+   if "`method'"!="" {
+      di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted."
+   }
+   local method `r(method)'
+   local kernel `r(kernel)'
+/*
+   if "`method'"=="polychoric" {
+      di in red "The {hi:consolidation} is not possible with the {hi:polychoric} option"
+      error 198
+      exit
+   }
+*/
+}
+if "`method'"=="" {
+   local method classical
+}
+if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" {
+   di in green "Initial variables are used with the {hi:polychoric} methods"
+   di in green "But the procedure is based on the matrix of the polychoric correlations"
+   di
+}
+if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   di in red "The {hi:method} `method' is unknown"
+   error 198
+   exit
+}
+
+tokenize `varlist'
+local nbitems : word count `varlist'
+marksample touse
+qui keep if `touse'
+
+local mat=max(`matsize',`=`nbitems'*2')
+qui set matsize `mat'
+
+if `nbitems'<3&`none'!=1 {
+   di in red "You need at least 3 variables"
+   error 198
+   exit
+}
+forvalues i=1/`nbitems'{
+   local label`i':variable label ``i''
+   if "`label`i''"=="" {
+      local label`i' ``i''
+   }
+   if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+      qui su ``i''
+      local mean=r(mean)
+      if "`standardized'"=="" {
+         local sd=r(sd)
+      }
+      else {
+         local sd=1
+      }
+      qui replace ``i''=(``i''-`mean')/`sd'
+   }
+}
+
+tempfile clvfiletmp
+qui save `clvfiletmp',replace
+
+qui count
+local nbind=r(N)
+
+local cons=`consolidation'
+
+if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   local totvar=0
+   forvalues i=1/`nbitems' {
+      qui su ``i''
+      local totvar=`totvar'+`r(Var)'
+   }
+}
+else {
+   local totvar `nbitems'
+}
+local nbkerk=0
+local nbkerg=0
+
+if "`kernel'"!="" {
+   local nbkerg:word count `kernel'
+   local fin0=0
+   forvalues i=1/`nbkerg' {
+      local nbi`i':word `i' of `kernel'
+      local nbkerk=`nbkerk'+`nbi`i''
+      local deb`i'=`fin`=`i'-1''+1
+      local fin`i'=`deb`i''+`nbi`i''-1
+      local list`i'
+      forvalues j=`deb`i''/`fin`i'' {
+         local list`i' `list`i'' ``j''
+      }
+   }
+   tempname kerclus
+   matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0)
+   local ligne=1
+   forvalues g=1/`nbkerg' {
+       matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+       matrix `kerclus'[`ligne',2]=`deb`g''
+       matrix `kerclus'[`ligne',3]=`deb`g''+1
+       local clus`g'=`nbitems'+`ligne'
+       local ligne=`ligne'+1
+       if `nbi`g''>2 {
+          forvalues i=2/`=`nbi`g''-1' {
+             matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+             matrix `kerclus'[`ligne',2]=`deb`g''+`i'
+             matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1
+             local clus`g'=`nbitems'+`ligne'
+             local ligne=`ligne'+1
+          }
+      }
+   }
+}
+if `nbitems'<`nbkerk' {
+   di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}"
+   error 198
+   exit
+}
+di
+di in green "{hline 30}"
+di in green "TOTAL VARIANCE: " in ye %14.3f `totvar'
+di in green "NUMBER OF INDIVIDUALS: " in ye %7.0f `nbind'
+di in green "METHOD:" in ye _col(`=31-length("`method'")') "`=upper("`method'")'"
+di in green "{hline 30}"
+di
+if "`kernel'"!="" {
+   forvalues i=1/`nbkerg' {
+      di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''"
+      di
+   }
+}
+else {
+   local nbkerk=0
+   local nbkerg=0
+}
+tempname Ev
+if `none'!=1 {
+   matrix `matclus'=J(`nbitems',`nbitems',0)
+   matrix `vp'=J(`=2*`nbitems'-1',10,0)
+   forvalues i=1/`nbitems' {
+      matrix `matclus'[`i',1]=`i'
+      if "`method'"!="polychoric"&"`method'"!="polychoric" {
+         qui su ``i''
+         matrix `vp'[`i',1]=r(Var)
+      }
+      else {
+         matrix `vp'[`i',1]=1
+      }
+      matrix `vp'[`i',8]=`i'
+      matrix `vp'[`i',9]=`totvar'
+      matrix `vp'[`i',10]=100
+   }
+   matrix `vp'[`nbitems',3]=`nbitems'
+   if "`method'"=="centroid" {
+      local crit G
+      di in green "{hline 89}"
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative"
+      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"
+      di in green "{hline 89}"
+   }
+   else {
+      local crit T
+      di in green "{hline 100}"
+      if "`method'"=="v2"|"`method'"=="polychoricv2" {
+         di in green _col(84) "Maximal"
+      }
+      else {
+         di in green _col(84) "Current"
+      }
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative"
+      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"
+      di in green "{hline 100}"
+   }
+   tempname threshold
+   matrix `threshold'=J(`nbitems',3,0)
+   forvalues i=1/`=`nbitems'-1' {
+      local clus=`nbitems'+`i'
+      local minegenval=999999
+      local minegenval2=999999
+      forvalues k=1/`=`clus'-1' {
+         local list`k'
+         local numlist`k'
+         forvalues j=1/`clus' {
+            if (`matclus'[`j',`i']==`k') {
+               local list`k' `list`k'' ``j''
+               local numlist`k' `numlist`k'' `j'
+            }
+         }
+      }
+      if `clus'>`nbitems'+`nbkerk'-`nbkerg' {
+         if "`method'"=="centroid" {
+            tempname centrj centrk diffjk
+         }
+         forvalues j=1/`clus' {
+            local nblistj:word count `list`j''
+            forvalues k=`=`j'+1'/`clus' {
+               local nblistk:word count `list`k''
+               if `nblistj'!=0&`nblistk'!=0 {
+                  if "`method'"=="centroid" {
+                     qui genscore `list`j'',score(`centrj') mean
+                     qui su `centrj'
+                     local Varj=r(Var)
+                     qui genscore `list`k'',score(`centrk') mean
+                     qui su `centrk'
+                     local Vark=r(Var)
+                     qui gen `diffjk'=`centrk'-`centrj'
+                     qui su `diffjk'
+                     local Varjk=r(Var)
+                     drop `centrj' `centrk' `diffjk'
+                     local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk'
+                     if `ev'<`minegenval' {
+                        local minegenval=`ev'
+                        local minj `j'
+                        local mink `k'
+                        local eigen=0
+                        local eigen2=0
+                     }
+                  }
+                  else {
+                     if "`method'"=="classical"|"`method'"=="v2" {
+                        qui pca `list`j'' `list`k'',cov
+                        matrix `Ev'=e(Ev)
+                     }
+                     else if "`method'"=="polychoric"|"`method'"=="polychoricv2" {
+                        qui polychoricpca `list`j'' `list`k''
+                        matrix `Ev'=r(eigenvalues)
+                     }
+                     local lambda1=`Ev'[1,1]
+                     local lambda2=`Ev'[1,2]
+                     local ev=`vp'[`j',1]+`vp'[`k',1]-`lambda1'
+/*
+                     local t1=`vp'[`j',1]
+                     local t2=`vp'[`k',1]
+                     di "`ev'=`t1'+`t2'-`lambda1'"
+*/
+                     local ev2=max(`vp'[`j',5],`vp'[`k',5],`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'',cov
+               matrix `Ev'=e(Ev)
+            }
+            else if "`method'"=="polychoric"|"`method'"=="polychoricv2"{
+               qui polychoricpca `list`j'' `list`k''
+               matrix `Ev'=r(eigenvalues)
+            }
+            local lambda1=`Ev'[1,1]
+            local lambda2=`Ev'[1,2]
+            local ev=`vp'[`j',1]+`vp'[`k',1]-`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'
+            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'
+      }
+      *set trace off
+      if `mink'<=`nbitems' {
+         local nomk=abbrev("``mink''",14)
+      }
+      else {
+         local nomk `mink'
+      }
+      forvalues j=1/`nbitems' {
+         matrix `matclus'[`j',`=`i'+1']=`matclus'[`j',`i']
+      }
+      if "`method'"!="centroid" {
+         matrix `vp'[`clus',1]=`eigen'                            /*FIRST EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',2]=`minegenval'                       /*VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',3]=`vp'[`=`clus'-1',3]-`vp'[`clus',2] /*T CRITERION*/
+         matrix `vp'[`clus',4]=`vp'[`clus',2]/`vp'[`=`clus'-1',3] /*RELATIVE VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',5]=`eigen2'                           /*SECOND EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',6]=`minj'                             /*CHILD 1*/
+         matrix `vp'[`clus',7]=`mink'                             /*CHILD 2*/
+         matrix `vp'[`clus',8]=`nbitems'+`i'                      /*NUMBER OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',9]=`vp'[`=`clus'-1',9]-`minegenval'   /*EXPLAINED VARIANCE*/
+         matrix `vp'[`clus',10]=`vp'[`clus',9]/`totvar'*100       /*% OF EXPLAINED VARIANCE*/
+      }
+      else {
+         matrix `vp'[`clus',1]=0                                  /*FIRST EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',2]=`minegenval'                       /*VARIATION OF THE G CRITERION*/
+         matrix `vp'[`clus',3]=`vp'[`=`clus'-1',3]-`vp'[`clus',2] /*G CRITERION*/
+         matrix `vp'[`clus',4]=`vp'[`clus',2]/`vp'[`=`clus'-1',3] /*RELATIVE VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',5]=0                                  /*SECOND EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',6]=`minj'                             /*CHILD 1*/
+         matrix `vp'[`clus',7]=`mink'                             /*CHILD 2*/
+         matrix `vp'[`clus',8]=`nbitems'+`i'                      /*NUMBER OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',9]=`vp'[`=`clus'-1',9]-`minegenval'   /*EXPLAINED VARIANCE*/
+         matrix `vp'[`clus',10]=`vp'[`clus',9]/`totvar'*100       /*% OF EXPLAINED VARIANCE*/
+      }
+      foreach j of numlist `numlist`minj'' `numlist`mink''  {
+         matrix `matclus'[`j',`=`i'+1']=`clus'
+      }
+      if "`kernel'"!=""&`i'==`=`nbkerk'-`nbkerg'+1' {
+         local T=`vp'[`=`clus'-1',9]
+         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' {
+         if  `clus'==`nbitems'+`nbkerk'-`nbkerg'+1 {
+            local relv
+            local percent
+         }
+         else {
+            local relv=(`minegenval'-`vp'[`=`clus'-1',2])/`vp'[`=`clus'-1',3]*100
+            local percent %
+            matrix `threshold'[`=`nbitems'-`i'+1',1]=`relv'
+            matrix `threshold'[`=`nbitems'-`i'+1',3]=`minegenval'
+            if `i'>1 {
+               matrix `threshold'[`=`nbitems'-`i'+1',2]=`relv'-`threshold'[`=`nbitems'-`i'+2',1]
+            }
+         }
+         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',9] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',10] "%" _col(83) _col(84) %5.2f `relv' "`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',9] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',10] "%" _col(83) %8.4f `vp'[`clus',5] _col(95) %5.2f `relv' "`percent'"
+         }
+      }
+   }
+   local i=2*`nbitems'-1
+   local relv=(`vp'[`i',3]-`vp'[`i',2])/`vp'[`i',3]*100
+   if "`method'"=="centroid" {
+      di in ye _col(84) %5.2f `relv' "`percent'"
+   }
+   else {
+      di in ye _col(95) %5.2f `relv' "`percent'"
+   }
+   matrix `threshold'[1,1]=`relv'
+   matrix `threshold'[1,2]=`relv'-`threshold'[2,1]
+   matrix `threshold'[1,3]=`vp'[`i',3]
+   *matrix list `threshold'
+   local best=0
+   local maxbest=0
+   local best2=0
+   local maxbest2=0
+   forvalues i=1/`nbitems' {
+      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 100}"
+   di in green "Proposed best partitions: "
+   di in green "Based on the variation of the T criterion"
+   di in green _col(10) "1. Partitions in " in ye `best' in green " clusters"
+   di in green _col(10) "2. Partitions in " in ye `best2' in green " 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',2]>`var2'&`i'<`nbitems'-1 {
+           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 green "Based on a research of a threshold"
+   di in green _col(10) "1. Partitions in " in ye `bestt' in green " clusters"
+   di in green _col(10) "2. Partitions in " in ye `bestt2' in green " clusters"
+   forvalues i=`=`nbitems'+1'/`=`nbitems'+`nbkerk'-`nbkerg'' {
+      matrix `vp'[`i',2]=`totvar'-`T'
+      matrix `vp'[`i',9]=`T'
+      matrix `vp'[`i',10]=`T'/`nbitems'*100
+   }
+   return local bestthresold `bestt' `bestt2'
+}
+
+if "`bar'"!="" {
+   drop _all
+   qui set obs `nbitems'
+   qui svmat `vp' ,names(v)
+   qui drop in 1/`nbitems'
+   qui gen id=`nbitems'-_n
+   qui drop if id>`nbitems'-`nbkerk'+`nbkerg'-1
+   label variable id "Number of clusters"
+   label variable v2 "T variation"
+   graph twoway bar v2 id, name(bar,replace) vert ,ylabel(0(0.5)2) xlabel(1(1)`=`nbitems'-`nbkerk'+`nbkerg'-1')
+}
+
+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',6]<=`nbitems') {
+      local first=`vp'[`ligne',6]
+      gsort +fait -v`i'  +clv_id
+   }
+   else {
+      local first=`vp'[`ligne',7]
+      gsort +fait -v`i'  +clv_id
+   }
+   if "`deltaT'"!="" {
+      qui replace clv_hgt=`vp'[`ligne',2] in 1
+   }
+   else {
+      qui replace clv_hgt=100-`vp'[`ligne',10] in 1
+   }
+   qui replace fait=1 in 1
+   qui replace clus=`vp'[`ligne',8] in 1
+}
+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 "`dendro'"=="" {
+   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 "`horizontal'"!="" {
+      cluster dendro clv,  name (dendrogram,replace) hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0)) `cut'
+   }
+   else {
+      cluster dendro clv, name(dendrogram,replace)  xtitle("`var'") `showcount' ytitle("`titleL'") title("`title'",span) ylabel(`yl') `cut'
+   }
+}
+
+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
+}
+
+/*
+if `cons'!=0&("`method'"=="polychoric"|"`method'"=="polychoricv2") {
+   di in ye "The {hi:consolidation} is not possible with the {hi:polychoric} methods"
+   local cons=0
+} 
+*/
+
+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')
+
+   *cluster generate cut = groups(2/9) , name(clv)
+   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=1
+      local max=0
+      *set trace on
+      local critT=0
+      forvalues g=1/`cons' {
+         di _col(`col') in green "GROUP " %2.0f `g' _c
+         local col=`col'+10
+         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''
+               local var=r(Var)
+               local critT=`critT'+`tmp`g''*`var'
+         }
+         else {
+            if `tmp`g''>1 {
+               if "`method'"=="classical"|"`method'"=="v2" {
+                  qui pca `list`g'',cov
+                  matrix `Ev'=e(Ev)
+                  qui predict `f1`g''
+               }
+               else if "`method'"=="polychoric"|"`method'"=="polychoric" {
+                  qui polychoricpca `list`g'',score(`f1`g'') nscore(1)
+                  matrix `Ev'=r(eigenvalues)
+                  rename `f1`g''1 `f1`g''
+               }
+               local lambda1=`Ev'[1,1]
+               local critT=`critT'+`lambda1'
+            }
+            else {
+               qui gen `f1`g''=`list`g''
+               if "`standardized'"=="" {
+                   local critT=`critT'+1
+               }
+               else  {
+                  qui su
+                  local critT=`critT'+`r(Var)'
+               }
+            }
+         }
+      }
+      di
+      forvalues i=1/`max' {
+         local col=1
+         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'+10
+         }
+         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''
+         local corr`i'=r(rho)
+         local corrs`i'=r(rho)
+         forvalues g=1/`cons' {
+            qui `command' ``i'' `f1`g''
+            local tmpcorr=r(rho)
+            if ((`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'"
+             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/`cons' {
+                  local loc=13*`h'+10
+                  qui corr `f1`g'' `f1`h''
+                  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'"=="" {
+            local max=max(`matsize',`nbind')
+            set matsize `max'
+            if "`addvar'"!="" {
+               local add `varlist'
+            }
+            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)
+         }
+         else {
+            di in green "There is more than 800 individuals, so the {hi:biplot} is disabled"
+         }
+      }
+      forvalues g=1/`cons' {
+         drop `f1`g''
+      }
+   }
+}
+set matsize `matsize'
+use `clvfile',replace
+capture cluster delete clv,zap
+return matrix vp=`vp'
+return matrix matclus=`matclus'
+return local varlist `varlist'
+return local method `method'
+return local kernel `kernel'
+end
diff --git a/Modules/ado/personal/c/clv29.ado b/Modules/ado/personal/c/clv29.ado
new file mode 100644
index 0000000..4a9180b
--- /dev/null
+++ b/Modules/ado/personal/c/clv29.ado
@@ -0,0 +1,798 @@
+*! Version 2.9 9December2005
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* Stata program : clv
+* Clustering of variables around latent variables
+* Version 2.9 : December 9, 2005 /*save the latent variables*/
+*
+* Historic
+* Version 1 (2005-06-11): Jean-Benoit Hardouin
+* Version 1.1 (2005-07-07): Jean-Benoit Hardouin /*small bug in the consolidation process with cluster of only one variable*/
+* Version 1.2 (2005-07-08): Jean-Benoit Hardouin /*Bug in the consolidation procedure when there is negative correlation*/
+* Version 2 (2005-09-03): Jean-Benoit Hardouin /*Horizontal dendrograms (with Stata 9)*/
+* Version 2.1 (2005-09-08): Jean-Benoit Hardouin /*More flexibility to abbreviate the names of the variables (with Stata 9)*/
+* Version 2.1.1 (2005-09-08): Jean-Benoit Hardouin /*Integration of some requests of Ronan Conroy*/
+* Version 2.1.2 (2005-09-08): Jean-Benoit Hardouin /*Possibility to give a title and an X/Y caption*/
+* Version 2.2 (2005-09-11): Jean-Benoit Hardouin /*Kernel option*/
+* Version 2.3 (2005-09-12): Jean-Benoit Hardouin /*Polychoric option*/
+* Version 2.4 (2005-09-13): Jean-Benoit Hardouin /*v2 option*/
+* Version 2.5 (2005-09-21): Jean-Benoit Hardouin /*corrections*/
+* Version 2.6 (2005-10-02): Jean-Benoit Hardouin /*centroid method, biplot*/
+* Version 2.7 (2005-10-06): Jean-Benoit Hardouin /*return, multiple graphs, polychoric+consolidation*/
+* Version 2.8 (2005-10-06): Jean-Benoit Hardouin /*fweights*/
+*
+* Jean-benoit Hardouin, Regional Health Observatory of Orléans - France
+* jean-benoit.hardouin@orscentre.org
+*
+* News about this program : http://anaqol.free.fr
+* FreeIRT Project : http://freeirt.free.fr
+*
+* Copyright 2005 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 clv,rclass
+version 9.0
+syntax [varlist(default=none)] [if] [in] [fweight] [, CUTnumber(int 30) bar CONSolidation(int 0) noDENdro noSTANDardized deltaT HORizontal SHOWcount ABBrev(int 14) TITle(string) CAPtion(string) KERnel(numlist) METHod(string) noBIPlot ADDvar genlv(string)]
+preserve
+tempfile clvfile
+qui save `clvfile',replace
+local matsize=c(matsize)
+local none=0
+if "`varlist'"=="" {
+   capture confirm matrix r(vp)
+   if _rc==0 {
+      capture confirm matrix r(matclus)
+      if _rc ==0 {
+         local none=1
+      }
+   }
+   if `none'==0 {
+      di in red "You cannot use the {hi:clv} command without {hi:varlist} if you have not already run {hi:clv}"
+      error 198
+      exit
+   }
+}
+
+tempname matclus vp
+
+if `none'==1 {
+   matrix `vp'=r(vp)
+   matrix `matclus'=r(matclus)
+   local varlist `r(varlist)'
+   tokenize `varlist'
+   local nbitems=rowsof(`matclus')
+   if "`method'"!="" {
+      di in green "The {hi:method} option can not be modified without specification of the varlist. {hi:method} is omitted."
+   }
+   local method `r(method)'
+   local kernel `r(kernel)'
+}
+if "`method'"=="" {
+   local method classical
+}
+if ("`method'"=="polychoric"|"`method'"=="polychoricv2")&"`standardized'"!="" {
+   di in green "Initial variables are used with the {hi:polychoric} methods"
+   di in green "But the procedure is based on the matrix of the polychoric correlations"
+   di
+}
+if "`method'"!="classical"&"`method'"!="v2"&"`method'"!="centroid"&"`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   di in red "The {hi:method} `method' is unknown"
+   error 198
+   exit
+}
+
+tokenize `varlist'
+local nbitems : word count `varlist'
+marksample touse
+qui keep if `touse'
+
+local mat=max(`matsize',`=`nbitems'*2')
+qui set matsize `mat'
+
+if `nbitems'<3&`none'!=1 {
+   di in red "You need at least 3 variables"
+   error 198
+   exit
+}
+forvalues i=1/`nbitems'{
+   local label`i':variable label ``i''
+   if "`label`i''"=="" {
+      local label`i' ``i''
+   }
+   if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+      qui su ``i'' [`weight'`exp']
+      local mean=r(mean)
+      if "`standardized'"=="" {
+         local sd=r(sd)
+      }
+      else {
+         local sd=1
+      }
+      qui replace ``i''=(``i''-`mean')/`sd'
+   }
+}
+
+tempfile clvfiletmp
+qui save `clvfiletmp',replace
+
+qui su `1' [`weight'`exp']
+local nbind=r(sum_w)
+
+
+local cons=`consolidation'
+
+if "`method'"!="polychoric"&"`method'"!="polychoricv2" {
+   local totvar=0
+   forvalues i=1/`nbitems' {
+      qui su ``i'' [`weight'`exp']
+      local totvar=`totvar'+`r(Var)'
+   }
+}
+else {
+   local totvar `nbitems'
+}
+local nbkerk=0
+local nbkerg=0
+
+if "`kernel'"!="" {
+   local nbkerg:word count `kernel'
+   local fin0=0
+   forvalues i=1/`nbkerg' {
+      local nbi`i':word `i' of `kernel'
+      local nbkerk=`nbkerk'+`nbi`i''
+      local deb`i'=`fin`=`i'-1''+1
+      local fin`i'=`deb`i''+`nbi`i''-1
+      local list`i'
+      forvalues j=`deb`i''/`fin`i'' {
+         local list`i' `list`i'' ``j''
+      }
+   }
+   tempname kerclus
+   matrix `kerclus'=J(`=`nbkerk'-`nbkerg'',3,0)
+   local ligne=1
+   forvalues g=1/`nbkerg' {
+       matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+       matrix `kerclus'[`ligne',2]=`deb`g''
+       matrix `kerclus'[`ligne',3]=`deb`g''+1
+       local clus`g'=`nbitems'+`ligne'
+       local ligne=`ligne'+1
+       if `nbi`g''>2 {
+          forvalues i=2/`=`nbi`g''-1' {
+             matrix `kerclus'[`ligne',1]=`nbitems'+`ligne'
+             matrix `kerclus'[`ligne',2]=`deb`g''+`i'
+             matrix `kerclus'[`ligne',3]=`nbitems'+`ligne'-1
+             local clus`g'=`nbitems'+`ligne'
+             local ligne=`ligne'+1
+          }
+      }
+   }
+}
+if `nbitems'<`nbkerk' {
+   di in red "You cannot define more variables in the {hi:kernel} option than items in the {hi:varlist}"
+   error 198
+   exit
+}
+di
+di in green "{hline 30}"
+di in green "TOTAL VARIANCE: " in ye %14.3f `totvar'
+di in green "NUMBER OF INDIVIDUALS: " in ye %7.0f `nbind'
+di in green "METHOD:" in ye _col(`=31-length("`method'")') "`=upper("`method'")'"
+di in green "{hline 30}"
+di
+if "`kernel'"!="" {
+   forvalues i=1/`nbkerg' {
+      di in green "The kernel numbered " in ye `clus`i'' in green " is composed of `nbi`i'' variables: " in ye "`list`i''"
+      di
+   }
+}
+else {
+   local nbkerk=0
+   local nbkerg=0
+}
+tempname Ev
+if `none'!=1 {
+   matrix `matclus'=J(`nbitems',`nbitems',0)
+   matrix `vp'=J(`=2*`nbitems'-1',10,0)
+   forvalues i=1/`nbitems' {
+      matrix `matclus'[`i',1]=`i'
+      if "`method'"!="polychoric"&"`method'"!="polychoric" {
+         qui su ``i'' [`weight'`exp']
+         matrix `vp'[`i',1]=r(Var)
+      }
+      else {
+         matrix `vp'[`i',1]=1
+      }
+      matrix `vp'[`i',8]=`i'
+      matrix `vp'[`i',9]=`totvar'
+      matrix `vp'[`i',10]=100
+   }
+   matrix `vp'[`nbitems',3]=`nbitems'
+   if "`method'"=="centroid" {
+      local crit G
+      di in green "{hline 89}"
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(82) "Relative"
+      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"
+      di in green "{hline 89}"
+   }
+   else {
+      local crit T
+      di in green "{hline 100}"
+      if "`method'"=="v2"|"`method'"=="polychoricv2" {
+         di in green _col(84) "Maximal"
+      }
+      else {
+         di in green _col(84) "Current"
+      }
+      di in green _col(7) "Number of" _col(69) "`crit'" _col(71) "Explained" _col(85) "Second" _col(93) "Relative"
+      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"
+      di in green "{hline 100}"
+   }
+   tempname threshold
+   matrix `threshold'=J(`nbitems',3,0)
+   forvalues i=1/`=`nbitems'-1' {
+      local clus=`nbitems'+`i'
+      local minegenval=999999
+      local minegenval2=999999
+      forvalues k=1/`=`clus'-1' {
+         local list`k'
+         local numlist`k'
+         forvalues j=1/`clus' {
+            if (`matclus'[`j',`i']==`k') {
+               local list`k' `list`k'' ``j''
+               local numlist`k' `numlist`k'' `j'
+            }
+         }
+      }
+      if `clus'>`nbitems'+`nbkerk'-`nbkerg' {
+         if "`method'"=="centroid" {
+            tempname centrj centrk diffjk
+         }
+         forvalues j=1/`clus' {
+            local nblistj:word count `list`j''
+            forvalues k=`=`j'+1'/`clus' {
+               local nblistk:word count `list`k''
+               if `nblistj'!=0&`nblistk'!=0 {
+                  if "`method'"=="centroid" {
+                     qui genscore `list`j'',score(`centrj') mean
+                     qui su `centrj' [`weight'`exp']
+                     local Varj=r(Var)
+                     qui genscore `list`k'',score(`centrk') mean
+                     qui su `centrk' [`weight'`exp']
+                     local Vark=r(Var)
+                     qui gen `diffjk'=`centrk'-`centrj'
+                     qui su `diffjk' [`weight'`exp']
+                     local Varjk=r(Var)
+                     drop `centrj' `centrk' `diffjk'
+                     local ev=(`nblistj'*`nblistk')/(`nblistj'+`nblistk')*`Varjk'
+                     if `ev'<`minegenval' {
+                        local minegenval=`ev'
+                        local minj `j'
+                        local mink `k'
+                        local eigen=0
+                        local eigen2=0
+                     }
+                  }
+                  else {
+                     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',1]+`vp'[`k',1]-`lambda1'
+                     local ev2=max(`vp'[`j',5],`vp'[`k',5],`lambda2')
+                     if ("`method'"=="v2"|"`method'"=="polychoricv2")&`ev'<`minegenval' {
+                        local eigen2=`lambda2'
+                        local minegenval=`ev'
+                        local eigen=`lambda1'
+                        local minj `j'
+                        local mink `k'
+                     }
+                     else if ("`method'"=="classical"|"`method'"=="polychoric")&`ev2'<`minegenval2' {
+                        local minegenval2=`ev2'
+                        local eigen2=`ev2'
+                        local minegenval=`ev'
+                        local eigen=`lambda1'
+                        local minj `j'
+                        local mink `k'
+                     }
+                  }
+               }
+            }
+         }
+      }
+      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',1]+`vp'[`k',1]-`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'
+      }
+      *set trace off
+      if `mink'<=`nbitems' {
+         local nomk=abbrev("``mink''",14)
+      }
+      else {
+         local nomk `mink'
+      }
+      forvalues j=1/`nbitems' {
+         matrix `matclus'[`j',`=`i'+1']=`matclus'[`j',`i']
+      }
+      if "`method'"!="centroid" {
+         matrix `vp'[`clus',1]=`eigen'                            /*FIRST EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',2]=`minegenval'                       /*VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',3]=`vp'[`=`clus'-1',3]-`vp'[`clus',2] /*T CRITERION*/
+         matrix `vp'[`clus',4]=`vp'[`clus',2]/`vp'[`=`clus'-1',3] /*RELATIVE VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',5]=`eigen2'                           /*SECOND EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',6]=`minj'                             /*CHILD 1*/
+         matrix `vp'[`clus',7]=`mink'                             /*CHILD 2*/
+         matrix `vp'[`clus',8]=`nbitems'+`i'                      /*NUMBER OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',9]=`vp'[`=`clus'-1',9]-`minegenval'   /*EXPLAINED VARIANCE*/
+         matrix `vp'[`clus',10]=`vp'[`clus',9]/`totvar'*100       /*% OF EXPLAINED VARIANCE*/
+      }
+      else {
+         matrix `vp'[`clus',1]=0                                  /*FIRST EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',2]=`minegenval'                       /*VARIATION OF THE G CRITERION*/
+         matrix `vp'[`clus',3]=`vp'[`=`clus'-1',3]-`vp'[`clus',2] /*G CRITERION*/
+         matrix `vp'[`clus',4]=`vp'[`clus',2]/`vp'[`=`clus'-1',3] /*RELATIVE VARIATION OF THE T CRITERION*/
+         matrix `vp'[`clus',5]=0                                  /*SECOND EIGEN VALUE OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',6]=`minj'                             /*CHILD 1*/
+         matrix `vp'[`clus',7]=`mink'                             /*CHILD 2*/
+         matrix `vp'[`clus',8]=`nbitems'+`i'                      /*NUMBER OF THE NEW CLUSTER*/
+         matrix `vp'[`clus',9]=`vp'[`=`clus'-1',9]-`minegenval'   /*EXPLAINED VARIANCE*/
+         matrix `vp'[`clus',10]=`vp'[`clus',9]/`totvar'*100       /*% OF EXPLAINED VARIANCE*/
+      }
+      foreach j of numlist `numlist`minj'' `numlist`mink''  {
+         matrix `matclus'[`j',`=`i'+1']=`clus'
+      }
+      if "`kernel'"!=""&`i'==`=`nbkerk'-`nbkerg'+1' {
+         local T=`vp'[`=`clus'-1',9]
+         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' {
+         if  `clus'==`nbitems'+`nbkerk'-`nbkerg'+1 {
+            local relv
+            local percent
+         }
+         else {
+            local relv=(`minegenval'-`vp'[`=`clus'-1',2])/`vp'[`=`clus'-1',3]*100
+            local percent %
+            matrix `threshold'[`=`nbitems'-`i'+1',1]=`relv'
+            matrix `threshold'[`=`nbitems'-`i'+1',3]=`minegenval'
+            if `i'>1 {
+               matrix `threshold'[`=`nbitems'-`i'+1',2]=`relv'-`threshold'[`=`nbitems'-`i'+2',1]
+            }
+         }
+         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',9] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',10] "%" _col(83) _col(84) %5.2f `relv' "`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',9] _col(62) %8.4f `minegenval' _col(72) %7.3f `vp'[`clus',10] "%" _col(83) %8.4f `vp'[`clus',5] _col(95) %5.2f `relv' "`percent'"
+         }
+      }
+   }
+   local i=2*`nbitems'-1
+   local relv=(`vp'[`i',3]-`vp'[`i',2])/`vp'[`i',3]*100
+   if "`method'"=="centroid" {
+      di in ye _col(84) %5.2f `relv' "`percent'"
+   }
+   else {
+      di in ye _col(95) %5.2f `relv' "`percent'"
+   }
+   matrix `threshold'[1,1]=`relv'
+   matrix `threshold'[1,2]=`relv'-`threshold'[2,1]
+   matrix `threshold'[1,3]=`vp'[`i',3]
+   *matrix list `threshold'
+   local best=0
+   local maxbest=0
+   local best2=0
+   local maxbest2=0
+   forvalues i=1/`nbitems' {
+      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 100}"
+   di 
+   di in green "{hline 24}"
+   di in green "PROPOSED BEST PARTITIONS"
+   di in green "{hline 24}"
+   di
+   di in yellow _col(4) "Based on the variation of the T criterion"
+   di in green _col(10) "1. Partitions in " in ye `best' in green " clusters"
+   di in green _col(10) "2. Partitions in " in ye `best2' in green " 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',2]>`var2'&`i'<`nbitems'-1 {
+           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"
+   di in green _col(10) "1. Partitions in " in ye `bestt' in green " clusters"
+   di in green _col(10) "2. Partitions in " in ye `bestt2' in green " clusters"
+   forvalues i=`=`nbitems'+1'/`=`nbitems'+`nbkerk'-`nbkerg'' {
+      matrix `vp'[`i',2]=`totvar'-`T'
+      matrix `vp'[`i',9]=`T'
+      matrix `vp'[`i',10]=`T'/`nbitems'*100
+   }
+   return local bestthresold `bestt' `bestt2'
+}
+
+if "`bar'"!="" {
+   drop _all
+   qui set obs `nbitems'
+   qui svmat `vp' ,names(v)
+   qui drop in 1/`nbitems'
+   qui gen id=`nbitems'-_n
+   qui drop if id>`nbitems'-`nbkerk'+`nbkerg'-1
+   label variable id "Number of clusters"
+   label variable v2 "T variation"
+   graph twoway bar v2 id, name(bar,replace) vert ,ylabel(0(0.5)2) xlabel(1(1)`=`nbitems'-`nbkerk'+`nbkerg'-1')
+}
+
+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',6]<=`nbitems') {
+      local first=`vp'[`ligne',6]
+      gsort +fait -v`i'  +clv_id
+   }
+   else {
+      local first=`vp'[`ligne',7]
+      gsort +fait -v`i'  +clv_id
+   }
+   if "`deltaT'"!="" {
+      qui replace clv_hgt=`vp'[`ligne',2] in 1
+   }
+   else {
+      qui replace clv_hgt=100-`vp'[`ligne',10] in 1
+   }
+   qui replace fait=1 in 1
+   qui replace clus=`vp'[`ligne',8] in 1
+}
+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 "`dendro'"=="" {
+   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 "`horizontal'"!="" {
+      cluster dendro clv,  name (dendrogram,replace) hor ytitle("`var'") `showcount' xtitle("`titleL'") title("`title'",span) xlabel(`yl') ylabel(,angle(0)) `cut'
+   }
+   else {
+      cluster dendro clv, name(dendrogram,replace)  xtitle("`var'") `showcount' ytitle("`titleL'") title("`title'",span) ylabel(`yl') `cut'
+   }
+}
+
+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
+}
+
+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')
+
+   *cluster generate cut = groups(2/9) , name(clv)
+   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=1
+      local max=0
+      local critT=0
+      forvalues g=1/`cons' {
+         di _col(`col') in green "GROUP " %2.0f `g' _c
+         local col=`col'+10
+         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'
+         }
+         else {
+            if `tmp`g''>1 {
+               if "`method'"=="classical"|"`method'"=="v2" {
+                  qui pca `list`g'' [`weight'`exp'] ,cov
+                  matrix `Ev'=e(Ev)
+                  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)
+                  rename `f1`g''1 `f1`g''
+               }
+               local lambda1=`Ev'[1,1]
+               local critT=`critT'+`lambda1'
+            }
+            else {
+               qui gen `f1`g''=`list`g''
+               if "`standardized'"=="" {
+                   local critT=`critT'+1
+               }
+               else  {
+                  qui su [`weight'`exp']
+                  local critT=`critT'+`r(Var)'
+               }
+            }
+         }
+      }
+      di
+      forvalues i=1/`max' {
+         local col=1
+         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'+10
+         }
+         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'"!="" {
+                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')
+            set matsize `max'
+            if "`addvar'"!="" {
+               local add `varlist'
+            }
+            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)
+         }
+         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''
+      }
+   }
+}
+set matsize `matsize'
+use `clvfile',replace
+capture cluster delete clv,zap
+return matrix vp=`vp'
+return matrix matclus=`matclus'
+return local varlist `varlist'
+return local method `method'
+return local kernel `kernel'
+end
diff --git a/Modules/ado/personal/c/compart.ado b/Modules/ado/personal/c/compart.ado
new file mode 100644
index 0000000..a74c88d
--- /dev/null
+++ b/Modules/ado/personal/c/compart.ado
@@ -0,0 +1,291 @@
+program define compart,rclass
+version 8
+syntax varlist [if] [in] [fweight iweight] [,part(numlist) Matrix(string) type(string) DETails noSTANDardized VARiables SQUare]
+
+preserve
+unab varlist:`varlist'
+di "`varlist'"
+tokenize `varlist'
+marksample touse
+local nbvar:word count `varlist'
+qui count
+local nbind=r(N)
+tempname p
+qui gen `p'=1
+qui su `p' [`weight'`exp'] if `touse'
+local nbind=r(N)
+if "`square'"=="" {
+   local quad=1
+}
+else {
+   local quad=2
+}
+
+if "`type'"!=""&"`type'"!="polychoric" {
+   di in red "The type of the matrix is not authorized. Please correct your {hi:type} option."
+   error 198
+}
+if "`type'"!=""&"`matrix'"!="" {
+   di in red "You cannot define in the same time the {hi:type} and the {hi:matrix} options"
+   error 198
+}
+
+/* DEFINITION OF THE PARTITION OF THE VARIABLES*/
+
+local newpart
+foreach i in `part' {
+    if `i'!=0 {
+       local newpart `newpart' `i'
+    }
+}
+local part `newpart'
+local meme=0
+local diff=0
+local nbpart:word count `part'
+forvalues i=1/`nbpart' {
+    local iti:word `i' of `part'
+    local meme=`meme'+`iti'*(`iti'-1)/2
+    forvalues j=`=`i'+1'/`nbpart' {
+       local itj:word `j' of `part'
+       local diff=`diff'+`iti'*`itj'
+    }
+}
+
+local perc=`meme'/(`meme'+`diff')
+di "meme: `meme' ; diff: `diff' ; perc: `perc'"
+
+
+local test=0
+local last0=0
+forvalues i=1/`nbpart' {
+   local first`i'=`last`=`i'-1''+1
+   local size`i':word `i' of `part'
+   local last`i'=`first`i''+`size`i''-1
+   local test=`test'+`size`i''
+   local list`i'
+   forvalues j=`first`i''/`last`i'' {
+      local list`i' `list`i'' ``j''
+   }
+}
+if `test'!=`nbvar' {
+   di in red "{p}The described partition of the variables is composed of a number of variables different of the number of variables of varlist.{p_end}"
+   exit 198
+}
+
+/* BY DEFAULT, STANDARDIZATION*/
+if "`standardized'"=="" {
+   forvalues i=1/`nbvar' {
+      qui su ``i'' [`weight'`exp']
+      qui replace ``i''=(``i''-r(mean))/r(sd)
+   }
+}
+
+tempname Cov W
+if "`matrix'"==""&"`type'"!="polychoric" {
+   /* COVARIANCE OR CORRELATION MATRIX*/
+   qui matrix accum `Cov'=`varlist' [`weight'`exp'],nocons dev
+   qui matrix `Cov'=`Cov'/(`nbind'-1)
+}
+else if "`type'"=="polychoric" {
+   qui polychoric `varlist'
+   qui matrix `Cov'=r(R)
+}
+else {
+   qui matrix `Cov'=`matrix'
+}
+ /* WE SAVE THE MATRIX AND WE COMPUTE THE AVERAGE COVARIANCE */
+qui matrix `W'=`Cov'
+local sum=0
+forvalues i=1/`nbvar' {
+   forvalues j=`=`i'+1'/`nbvar' {
+      local sum=`sum'+ `W'[`i',`j']^`quad'
+   }
+}
+
+/* WE SAVE THE DATA AND WE COMPUTES THE USED PERCENTILES OF THE COVARIANCE*/
+
+tempfile compartfile
+qui save `compartfile',replace
+
+drop _all
+set obs `=`nbvar'*(`nbvar'-1)'
+local n=1
+qui gen i=.
+qui gen j=.
+qui gen corr=.
+forvalues i=1/`nbvar' {
+   forvalues j=`=`i'+1'/`nbvar' {
+       qui replace i=`i' in `n'
+       qui replace j=`j' in `n'
+       qui replace corr=`W'[`i',`j']^`quad' in `n'
+       local ++n
+   }
+}
+matrix list `W'
+su corr
+sort corr
+centile corr,centile(`=100-`perc'*100')
+local centile=r(c_1)
+if `diff'!=0 {
+   local perc2=(`meme'+1)/(`meme'+`diff')
+   centile corr,centile(`=100-`perc2'*100')
+   local centile2=r(c_1)
+   local centile=(`centile'+`centile2')/2
+}
+qui use `compartfile',clear
+
+/***************************************************/
+
+if `nbpart'==1 {
+   local mean=0
+*   local mean=(2*`sum')/(`nbvar'*(`nbvar'-1))
+}
+else {
+   local mean=(2*`sum')/(`nbvar'*(`nbvar'-1))
+   local mean=0
+   *local mean=`centile'
+}
+
+/*THE MATRIX IS CENTERED*/
+
+forvalues i=1/`nbvar' {
+   matrix `W'[`i',`i']=0
+   forvalues j=`=`i'+1'/`nbvar' {
+      matrix `W'[`i',`j']=(`W'[`i',`j']^`quad'-`mean')
+      matrix `W'[`j',`i']=`W'[`i',`j']
+   }
+}
+
+/*WE COMPUTE THE INDEX D*/
+local C=0
+local C1=0
+local C2=0
+local minrho=2
+local summeme=0
+local sumdiff=0
+if "`square'"!="" {
+   local maxrho=0
+}
+else {
+   local maxrho=-2
+}
+*set trace on
+forvalues i=1/`nbpart' {
+   forvalues j=1/`nbpart' {
+       forvalues k=`first`i''/`last`i'' {
+           forvalues l=`first`j''/`last`j'' {
+               if `i'!=`j' {
+                  if `k'>`l' {
+                     local sumdiff=`sumdiff'+`Cov'[`k',`l']
+                  }
+                  if (`Cov'[`k',`l'])^`quad'>(`maxrho')^`quad' {
+                      local maxrho=(`Cov'[`k',`l'])
+                  }
+                  matrix `W'[`k',`l']=-(`W'[`k',`l'])
+               }
+               else if (`Cov'[`k',`l'])^`quad'<(`minrho')^`quad'&`k'!=`l' {
+                  local minrho=`Cov'[`k',`l']
+               }
+               if `i'==`j'&`k'>`l' {
+                  local summeme=`summeme'+`Cov'[`k',`l']
+               }
+               local C=`C'+`W'[`k',`l']
+               local C1=`C1'+abs(`W'[`k',`l'])
+               if `W'[`k',`l']>+0 {
+                  local ++C2
+               }
+          }
+      }
+   }
+}
+if `meme'!=0 {
+   local summeme=`summeme'/`meme'
+}
+if `diff'!=0 {
+    local sumdiff=`sumdiff'/`diff'
+}
+local diffsum=`summeme'-`sumdiff'
+set trace off
+di "Summeme: `summeme' ; Sumdiff: `sumdiff'"
+
+local minrho=(`minrho')^(`quad')
+local maxrho=(`maxrho')^(`quad')
+
+local C=sign(`C')*(abs(`C'))^(1/`quad')/(`nbvar'*(`nbvar'-1))+`mean'
+local C1=(`C1')^(1/`quad')/(`nbvar'*(`nbvar'-1))
+local C2=(`C2')/(`nbvar'*(`nbvar'-1))*100
+return local Pcov=`=`C'/`C1''
+return local Pel=`=`C2'/100'
+
+if `nbpart'==1 {
+    local C=`C'*(`nbvar'+1)/`nbvar'
+}
+
+*di "C:" `C' " C1: " `C1' " C2: " `C2'
+if `nbpart'==1 {
+   local maxrho=0
+}
+if `nbpart'==`nbvar' {
+   local minrho=0
+}
+di " C=(`meme'*`minrho'-`diff'*`maxrho')/(`meme'+`diff')"
+local C=(`meme'*`minrho'-`diff'*`maxrho')/(`meme'+`diff')
+di in green "{hline 80}"
+di in green "Number of individuals: " _col(71) in ye %8.0f `nbind'
+di in green "Number of variables: " _col(71) in ye %8.0f `nbvar'
+di in green "COMPART index: " _col(71) in ye %8.6f `C'
+di in green "Proportion of the covariances explained by the COMPART index: " _col(73) in ye %6.2f `=abs(`C')/`C1'*100' "%"
+di in green "Proportion of positive elements in the matrix: " _col(73) in ye %6.2f `C2' "%"
+di in green "Minimum correlation coefficient for 2 variables of the same group: " _col(74) in ye %5.2f `minrho'
+di in green "Maximum correlation coefficient for 2 variables of two different groups: " _col(74) in ye %5.2f `maxrho'
+di in green "Average correlation coefficient: " _col(74) in ye %5.2f `mean'
+di in green "{hline 80}"
+di
+if "`details'"!="" {
+   di in green "Matrix of the coefficients"
+   di in green "{hline 26}"
+   matrix list `W' ,noheader format(%7.4f)
+   di
+}
+if "`variables'"!="" {
+   di in green "Details for each variable"
+   di in green "{hline 26}"
+   di
+   di in green "{hline 80}"
+   di in green "Items" _col(17) "COMPART" _col(26) "Problematic items"
+   di in green "{hline 80}"
+   tempname Cvar
+   matrix `Cvar'=J(1,`nbvar',0)
+   forvalues i=1/`nbvar' {
+      local C`i'=0
+      local pourri`i'
+      forvalues j=1/`nbvar' {
+         local C`i'=`C`i''+`W'[`i',`j']
+         if `W'[`i',`j']<0 {
+            local pourri`i' `pourri`i'' ``j''
+         }
+      }
+      local C`i'=`C`i''/(`nbvar'-1)
+      matrix `Cvar'[1,`i']=`C`i''
+      di in ye abbrev("``i''",14) _col(15) %9.6f `C`i'' _c
+      if "`pourri`i''"!="" {
+         di in ye _col(26) "`pourri`i''"
+      }
+      else {
+         di
+      }
+   }
+   di in green "{hline 80}"
+   matrix colnames `Cvar'=`varlist'
+   matrix rownames `Cvar'=Compart
+   return matrix Cvar=`Cvar'
+}
+local test=`maxrho'-`minrho'
+local diffsum=(`summeme'*`meme'-`diff'*`sumdiff')/(`meme'+`diff')
+local diffsum=`summeme'/*-`sumdiff'*/
+return local compart `C'
+return local mean `mean'
+return local list `varlist'
+return local part `part'
+restore
+end
diff --git a/Modules/ado/personal/c/compart2.ado b/Modules/ado/personal/c/compart2.ado
new file mode 100644
index 0000000..725b35e
--- /dev/null
+++ b/Modules/ado/personal/c/compart2.ado
@@ -0,0 +1,120 @@
+program define compart2,rclass
+version 9
+syntax varlist [if] [in] [fweight iweight] [,part(numlist) type(string)]
+
+preserve
+unab varlist:`varlist'
+di "`varlist'"
+tokenize `varlist'
+marksample touse
+local nbvar:word count `varlist'
+qui count
+local nbind=r(N)
+tempname p
+qui gen `p'=1
+qui su `p' [`weight'`exp'] if `touse'
+local nbind=r(N)
+if "`square'"=="" {
+   local quad=1
+}
+else {
+   local quad=2
+}
+
+if "`type'"=="" {
+   local type classical
+}
+if "`type'"!="classical"&"`type'"!="centroid"&"`type'"!="polychoric" {
+   di in red "The type of the matrix is not authorized. Please correct your {hi:type} option."
+   error 198
+}
+
+/* DEFINITION OF THE PARTITION OF THE VARIABLES*/
+
+local newpart
+foreach i in `part' {
+    if `i'!=0 {
+       local newpart `newpart' `i'
+    }
+}
+local part `newpart'
+local meme=0
+local diff=0
+local nbpart:word count `part'
+forvalues i=1/`nbpart' {
+    local iti:word `i' of `part'
+    local meme=`meme'+`iti'*(`iti'-1)/2
+    forvalues j=`=`i'+1'/`nbpart' {
+       local itj:word `j' of `part'
+       local diff=`diff'+`iti'*`itj'
+    }
+}
+
+local perc=`meme'/(`meme'+`diff')
+di "meme: `meme' ; diff: `diff' ; perc: `perc'"
+
+
+local test=0
+local last0=0
+forvalues i=1/`nbpart' {
+   local first`i'=`last`=`i'-1''+1
+   local size`i':word `i' of `part'
+   local last`i'=`first`i''+`size`i''-1
+   local test=`test'+`size`i''
+   local list`i'
+   forvalues j=`first`i''/`last`i'' {
+      local list`i' `list`i'' ``j''
+   }
+}
+if `test'!=`nbvar' {
+   di in red "{p}The described partition of the variables is composed of a number of variables different of the number of variables of varlist.{p_end}"
+   exit 198
+}
+
+forvalues g=1/`nbpart' {
+   tempname f1`g'
+   if `size`g''>1 {
+      if "`type'"=="classical" {
+          qui pca `list`g'',cov
+          qui predict `f1`g''
+      }
+      else if "`type'"=="polychoric" {
+         qui polychoricpca `list`g'',score(`f1`g'') nscore(1)
+         rename `f1`g''1 `f1`g''
+      }
+      else if "`type'"=="centroid" {
+         qui genscore `list`g'', score(`f1`g'') mean
+      }
+   }
+   else if `size`g''==1 {
+      qui gen `f1`g''=`list`g''
+   }
+}
+local minrho=2
+local maxrho=-2
+forvalue i=1/`nbvar' {
+   forvalues g=1/`nbpart' {
+      qui corr ``i'' `f1`g''
+      if `i'>=`first`g''&`i'<=`last`g'' {
+         if r(rho)<`minrho' {
+            local minrho=r(rho)
+         }
+      }
+      else if r(rho)>`maxrho' {
+         local maxrho=r(rho)
+      }
+   }
+}
+
+di "C=(`meme'*`minrho'-`diff'*`maxrho')/(`meme'+`diff')"
+local C=(`meme'*`minrho'-`diff'*`maxrho')/(`meme'+`diff')
+local C=(`meme'*`minrho'-`diff'*`maxrho')/(`meme'+`diff')
+*local C=`minrho'
+di "C= `C' min=`minrho' max=`maxrho'"
+
+return local compart `C'
+*return local mean `mean'
+*return local list `varlist'
+return local part `part'
+restore
+end
diff --git a/Modules/ado/personal/c/conc.ado b/Modules/ado/personal/c/conc.ado
new file mode 100644
index 0000000..7c5c377
--- /dev/null
+++ b/Modules/ado/personal/c/conc.ado
@@ -0,0 +1,81 @@
+capture program drop conc
+program conc,rclass
+syntax varlist, comp(varlist) [tconc(real 0.4)]
+
+di as result "{hline}"
+di "{bf:Concurrent validity}"
+di as result "{hline}"
+di
+
+local n : word count `varlist'
+local p : word count `comp'
+
+matrix m = J(`n',`p',.)
+matrix rownames m = `varlist'
+matrix colnames m = `comp'
+local r = 1
+
+foreach i in `varlist' {
+	local c = 1
+	foreach j in `comp' {
+		qui corr `i' `j'
+		mat e = r(C)
+		local f = e[2,1]
+		mat m[`r',`c'] = `f' 
+		local `++c'
+	}
+	local `++r'
+}
+
+*mat li m, format(%3.2f) noheader
+
+tokenize `varlist'
+local maxv = length("`1'")
+forvalues i=1/`n' {
+	local lenv = length("``i''")
+	if `lenv' > `maxv' local maxv = `lenv'
+}
+
+local decv = `maxv'+6
+
+tokenize `comp'
+local maxc = length("`1'")
+forvalues i=1/`p' {
+	local lenc = length("``i''")
+	if `lenc' > `maxc' local maxc = `lenc'
+}
+
+local decc = `maxc'+4
+
+local col = `decv'
+foreach c in `comp' {
+	di as result _col(`col') "`c'" _c
+	local col = `col'+`decc'
+}
+di
+
+local i = 1 
+foreach x in `varlist' {
+	local var`i' = "`x'"
+	local `++i'
+}
+
+
+forvalues i=1/`n' {
+	di as result "`var`i''" _c
+	local col = `decv'
+	forvalues j=1/`p' {
+		local t = m[`i',`j']
+		if `t' > `tconc' | `t' < -`tconc' {
+			di as result _col(`=`col'-1') %5.2f `t' _c
+		}
+		else di as text _col(`=`col'-1') %5.2f `t' _c
+		local col = `col'+`decc'
+	}
+	di
+}
+
+end
+
+*conc HA-MOC, comp(ioc1 ioc2) tconc(0.4)
+*conc x1-x40, comp(x1 x2) tconc(0.4)
diff --git a/Modules/ado/personal/c/convdiv.ado b/Modules/ado/personal/c/convdiv.ado
new file mode 100644
index 0000000..1e71a7c
--- /dev/null
+++ b/Modules/ado/personal/c/convdiv.ado
@@ -0,0 +1,298 @@
+capture program drop convdiv
+program convdiv
+syntax varlist, PARTition(numlist integer >0) [SCOrename(string) TCONVdiv(real 0.4) convdivboxplots]
+preserve
+
+qui set autotabgraphs on
+
+local C = 0
+foreach z in `partition' {
+	local C = `C' + `z'
+}
+	
+local nbvars : word count `varlist'
+if `C' != `nbvars' {
+	di in red "The sum of the numbers in the partition option is different from the number of variables precised in varlist"
+	exit 119
+}
+
+local P:word count `partition'
+if "`scorename'" !="" {
+	local S:word count `scorename'
+	if `P'!=`S' {
+		di in red "The number of score names given is different from the number of dimensions in the partition option" 
+		exit 119
+	}
+}
+
+qui detect `varlist', partition(`partition') 
+
+matrix A = r(Corrrestscores)
+matrix B = r(Corrscores)
+
+local i = 1
+local y = 1
+
+foreach x in `partition' {
+	if `i' == 1 local s = `x'
+	else local s = `s' +`x'
+	
+	forvalues z = `y'/`s' {
+		matrix B[`z',`i'] = A[`z',`i']
+	}
+	local `i++' 
+	local y = `s'+1
+} 
+
+if "`scorename'"!="" {
+	matrix colnames B = `scorename'
+}
+else {
+	local name
+	local nname
+	forvalues i = 1/`P' {
+		local name "Dim`i'"
+		local nname `nname' `name'
+	}
+local scorename = "`nname'"
+matrix colnames B = `scorename'
+}
+
+/* coupure noms des scores */
+/*
+local i = 1
+foreach s in `scorename' {
+	local len = length("`s'")
+	if `len' > 5 {
+		local c = substr("`s'",1,4)
+		local d = substr("`s'",-1,1)
+		local s`i' "`c'" "~" "`d'" 
+	}
+	else local s`i' = "`s'"
+	local sc `sc' `s`i''
+	local `++i'
+}
+*/
+
+/* coupure noms des items */
+/*
+local i = 1
+foreach s in `varlist' {
+	local len = length("`s'")
+	if `len' > 10 {
+		local c = substr("`s'",1,9)
+		local d = substr("`s'",-1,1)
+		local var`i' "`c'" "~" "`d'" 
+	}
+	else local var`i' = "`s'"
+	local `++i'
+}
+*/
+
+local i = 1
+foreach v in `varlist' {
+	local var`i' = abbrev("`v'",10)
+	local `++i'
+}
+
+local i = 1
+foreach s in `scorename' {
+	local s`i' = abbrev("`s'",7)
+	local sc `sc' `s`i''
+	local `++i'
+}
+
+di as result "{hline}"
+di "{bf:Correlation matrix}"
+di "{hline}"
+di
+
+/*
+local i = 1 
+foreach x in `varlist' {
+	local var`i' = "`x'"
+	local `++i'
+}
+*/
+ /*
+tokenize `sc'
+local max = 3
+
+forvalues j=1/`P' {
+	local len`j' = length("`s`j''")
+	if `len`j'' > `max' local max = `len`j''
+}
+
+
+local maxit = 1
+
+forvalues i=1/`nbvars' {
+	local len = length("`var`i''")
+	if `len' > `maxit' local maxit = `len'
+}
+*/
+local dec = 10
+local col = `dec'
+
+local decit = 14
+local colit = `decit'
+
+
+local col1 = `decit'
+forvalues i=1/`P' {
+	di _col(`col1') "{bf:`s`i''}" _c
+	local col1 = `col1' + `dec'
+}
+
+di 
+local h = (`P'-1)*`dec'+`decit'+4
+di "{hline `h'}"
+
+local i = 1
+local j = 1
+local y = 1
+
+foreach p in `partition' {
+	if `j' == 1 local s = `p'
+	else local s = `s' +`p'
+	
+	forvalues z = `y'/`s' { 
+		di as text "{bf:`var`z''}" _c
+		local col = `decit'-1	
+		
+		local dd = `z' // [counting cptdiv (one per item)] 
+		
+		forvalues k = 1/`P' { 
+				
+			local t = B[`z',`k']
+			local t : di %6.3f `t'
+			if `k' == `i' {
+				if `t' < `tconvdiv' {
+					di in red _col(`col') "{bf:`t'}" _c
+					local cptconv = `cptconv'+1
+					local col = `col' + `dec'
+				}
+				else {
+					di _col(`col') "{bf:`t'}" _c
+					local col = `col' + `dec'
+				}
+			}
+			else {
+				
+				if B[`z',`k'] > B[`z',`i'] {
+					di in red _col(`col') "`t'" _c
+					if `dd' == `z' local cptdiv = `cptdiv'+1 // [one per item]
+					local dd = 0
+					local col = `col' + `dec'
+				}
+				else {
+					di as text _col(`col') "{text:`t'}"_c
+					local col = `col' + `dec'
+				}
+			}
+		}
+	di 
+	}
+	di "{dup `h':-}"
+	local `i++' 
+	local `j++' 
+	local y = `s'+1
+}
+
+local y = 1
+local h = 1
+local np : word count `partition'
+
+foreach p in `partition' {
+	if `h' == 1 local s = `p'
+	else local s = `s' +`p'
+	
+	forvalues j = 1/`np' {
+		mat C_`h'_`j' = B[`y'..`s',`j']
+		
+		tempvar tp_`h'_`j'
+        mat colnames C_`h'_`j' = `tp_`h'_`j''
+		
+		svmat C_`h'_`j', names(col)
+		*rename C_`h'_`j' 
+		*mat li C_`h'_`j' 
+	}
+	
+	local `++h' 
+	local y = `s'+1
+}
+
+if "`convdivboxplots'" != "" {
+	forvalues h = 1/`np' {
+		tokenize `scorename'
+		local call = ""
+		local callbox = ""
+		local callleg = ""
+		
+		forvalues j = 1/`np' {
+			*rename C_`h'_`j' _``j'' 
+			*di "`tp_`h'_`j''"
+			local call `call' /*_``j''*/ `tp_`h'_`j''
+			*if `h' == `j' local color = "blue"
+			*else local color = ""
+			local callbox `callbox' box(`j',fcolor(`color') lcolor(`color')) marker(`j', mcolor(`color')) 
+			
+			local lab = "``j''"
+			local lab = `"`lab'"'
+			local callleg `callleg' `j' "`lab'"
+			*di `"`callleg'"'
+			*di "`call'"
+			
+		}
+		graph box `call', name("Conv_div_``h''",replace) `callbox' legend(order(`"`callleg'"') stack rows(1) size(small)) title(Correlations between items of ``h'' and dimensions) yline(`tconvdiv', lpattern(dot) lcolor(black))
+		qui set autotabgraphs on
+		*drop `call'
+	}
+}
+
+/*
+foreach var of varlist ioc1 ioc2 ioc3 {
+    loc varlab `""`:var l `var''""'
+    loc varlabs `"`varlabs'`varlab'"'
+	di "`varlab'"
+}
+*/
+
+/*
+svmat B
+
+local y = 1
+local h = 1
+qui gen d = 0
+foreach p in `partition' {
+	if `h' == 1 local s = `p'
+	else local s = `s' +`p'
+	
+	replace d = `h' in `y'/`s'
+	
+	local `++h' 
+	local y = `s'+1
+}
+
+twoway (scatter B1 d)
+*/
+local t : di %5.3f `tconvdiv'
+local p1 = (`nbvars'-`cptconv')/`nbvars'*100
+local p1 : di %4.1f `p1'
+local p2 = (`nbvars'-`cptdiv')/`nbvars'*100
+local p2 : di %4.1f `p2'
+
+di
+di as result "Convergent validity:" _c
+di as text " `=`nbvars'-`cptconv''/`nbvars' items (`p1'%) have a correlation coefficient with the score of "
+di _col(22) "their own dimension greater than `t'"
+di
+di as result "Divergent validity:" _c
+di as text "  `=`nbvars'-`cptdiv''/`nbvars' items (`p2'%) have a correlation coefficient with the score"
+di _col(22) "of their own dimension greater than those computed with other scores." 
+
+
+end
+
+*convdiv ioc1-ioc37, partition(4 4 7 3 3 4 7 5) scorename(Hddfdfdffda PSE W BCC Afdfdfererdfc AE LI MOC) tconvdiv(0.4) // convdivboxplots
+*convdiv x1-x40, partition(5 5 5 5 5 5 5 5) scorename(Hdfda PSE W BCC Afdfdfererdfc AE LI MOC) tconvdiv(0.4) // convdivboxplots
diff --git a/Modules/ado/personal/d/dege.ado b/Modules/ado/personal/d/dege.ado
new file mode 100644
index 0000000..c21030b
--- /dev/null
+++ b/Modules/ado/personal/d/dege.ado
@@ -0,0 +1,16 @@
+program define dege
+syntax anything [, i(string)]
+preserve
+if "`i'"=="" {
+   local i ind
+}
+qui reshape long `anything',i(`i') j(item)
+qui rename  `anything' response
+qui inspect item
+local nbvalues=r(N_unique)
+forvalues j=1/`nbvalues' {
+   qui gen `anything'`j'=item==`j'
+   qui replace `anything'`j'=-`anything'`j'
+}
+restore,not
+end
diff --git a/Modules/ado/personal/d/delta.ado b/Modules/ado/personal/d/delta.ado
new file mode 100644
index 0000000..5f431bf
--- /dev/null
+++ b/Modules/ado/personal/d/delta.ado
@@ -0,0 +1,108 @@
+*! Delta version 1.5 - 5 March 2008
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* DELTA: delta coefficient
+* Version 1.5: March 5, 2008
+*
+* Historic
+* Version 1 (2007-05-21): Jean-Benoit Hardouin
+* Version 1.1 (2007-05-22): Jean-Benoit Hardouin /* if in and possibility to use the score*/
+* Version 1.2 (2007-05-22): Jean-Benoit Hardouin /*bug when a score is missing*/
+* Version 1.3 (2007-06-16): Jean-Benoit Hardouin /*change in the options*/
+* Version 1.4 (2007-07-03): Jean-Benoit Hardouin /*correct a bug in the options*/
+* Version 1.5 (2008-03-05): Jean-Benoit Hardouin /*correct a bug in the ci option*/
+*
+* Jean-benoit Hardouin, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* jean-benoit.hardouin@univ-nantes.fr
+*
+* News about this program : http://www.anaqol.org
+* FreeIRT Project : http://www.freeirt.org
+*
+* Copyright 2007-2008 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 delta , rclass
+version 7.0
+syntax varlist(min=1 numeric) [if] [in] [,ci(integer 0) noDots  MINscore(int 0) MAXscore(int 0)]
+
+preserve
+tempfile deltafile
+qui save `deltafile'
+if "`if'"!=""|"`in'"!="" {
+   qui keep `if' `in'
+}
+
+local nbitems:word count `varlist'
+tokenize `varlist'
+
+local scoremin=`minscore'
+local scoremax=`maxscore'
+
+
+
+tempvar score
+if `nbitems'==1&`scoremax'==0 {
+   di in red "If you indicate only the score variable, you must define the {cmd:scoremax} option"
+   error 198
+}
+else if `nbitems'==1&`scoremax'!=0 {
+   qui gen `score'=`varlist'
+}
+else {
+   qui genscore `varlist',score(`score')
+}
+qui drop if `score'==.
+qui count
+local nbind=r(N)
+
+if `scoremax'==0 {
+   qui su `score'
+   local scoremax=r(max)
+}
+
+tempname error
+gen `error'=`score'<`scoremin'|`score'>`scoremax'
+qui count if `error'==1
+local err=r(N)
+if `err'!=0 {
+   di in red "`err' individuals has(have) a score inferior to `scoremin' or superior to `scoremax'"
+   error 198
+}
+
+local sumsqscore=0
+forvalues i=`scoremin'/`scoremax' {
+   qui count if `score'==`i'
+   local score`i'=r(N)
+   local sumsqscore=`sumsqscore'+`score`i''^2
+}
+local delta=(1+`scoremax')*(`nbind'^2-`sumsqscore')/(`nbind'^2*`scoremax')
+
+di in green "Range of the scores : " in ye `scoremin' in gr "/" in ye `scoremax'
+di in green "Number of used individuals : " in ye `nbind'
+
+if `ci'!=0 {
+   bootstrap delta=r(delta), reps(`ci') nowarn noheader nolegend `dots': delta `varlist' ,minscore(`scoremin') maxscore(`scoremax')
+}
+else {
+   display in green "Delta=  " in yellow %8.6f `delta'
+}
+return scalar delta=`delta'
+qui use `deltafile',clear
+restore,not
+
+end
diff --git a/Modules/ado/personal/d/descitems.ado b/Modules/ado/personal/d/descitems.ado
new file mode 100644
index 0000000..283b29e
--- /dev/null
+++ b/Modules/ado/personal/d/descitems.ado
@@ -0,0 +1,250 @@
+capture program drop descitems
+program descitems
+syntax varlist, PARTition(numlist integer >0)
+local i = 1
+
+local C = 0
+foreach z in `partition' {
+	local C = `C' + `z'
+}
+
+local nbvars : word count `varlist'
+
+if `C' != `nbvars' {
+	di in red "The sum of the numbers in the partition option is different from the number of variables precised in varlist"
+	exit 119
+}
+
+
+local i = 1 
+foreach x in `varlist' {
+	local var`i' = "`x'"
+	local `++i'
+}
+
+/*
+qui su `var1'
+local min = r(min)
+local max = r(max)
+
+forvalue i=2/`nbvars' {
+	qui su `var`i''
+	local minloc = r(min)
+	local maxloc = r(max)
+	if `minloc'<`min' local min = `minloc'
+	if `maxloc'>`max' local max = `maxloc'
+}
+*/
+
+foreach var in `varlist' {
+	qui replace `var' = round(`var')
+}
+
+local lev = ""
+foreach var in `varlist' {
+	qui levelsof `var', local(levels)
+	foreach l in `levels' {
+		if strpos("`lev'","`l'") == 0 {
+		local lev `lev' `l'	
+		}
+	}
+}
+
+_qsort_index `lev'
+local lev = r(slist1)
+
+local i = 1
+matrix d = J(`nbvars',4,.)
+
+foreach var in `varlist'{
+	qui count if missing(`var')
+	local ct=r(N)
+	
+	local tx`i'=`ct'/_N
+	matrix d[`i',1] = `tx`i''
+	local `i++'
+}
+
+matrix rownames d = `varlist'
+matrix colnames d = "missing" "alpha" "Hj" 
+
+local i = 1
+local y = 1
+foreach x in `partition' {
+	
+	if `i' == 1 local s = `x'
+	else local s = `s' +`x'
+
+	local liste = ""
+	forvalues w = `y'/`s' {
+		local liste `liste' `var`w'' 
+	}
+	
+	qui capture alpha `liste', asi item std
+	
+	mat a = r(Alpha)
+	mat at = a'
+	
+	qui capture loevh `liste', pairwise
+	matrix e = r(loevHj)	
+	matrix et = e'
+	matrix ns = r(nbHjkNS)	
+	matrix nst = ns'
+		
+	local k = 0
+	forvalues j = `y'/`s' {
+		local k = `k'+1
+		matrix d[`j',2] = at[`k',1]
+		matrix d[`j',3] = et[`k',1]
+		matrix d[`j',4] = nst[`k',1]
+		
+	}
+	local `i++' 
+	local y = `s'+1	
+}
+
+/* coupure noms des items */
+/*
+local i = 1
+foreach s in `varlist' {
+	local len = length("`s'")
+	if `len' > 10 {
+		local c = substr("`s'",1,9)
+		local d = substr("`s'",-1,1)
+		local var`i' "`c'" "~" "`d'" 
+	}
+	else local var`i' = "`s'"
+	*local v `v' `var`i''
+	local `++i'
+}
+
+
+local max = 3
+forvalues i=1/`nbvars' {
+	local len = length("`var`i''")
+	if `len' > `max' local max = `len'
+}
+*/
+
+local i = 1
+foreach v in `varlist' {
+	local var`i' = abbrev("`v'",8)
+	local `++i'
+}
+
+local dec = 10
+local col = `dec'
+local b : word count `lev'
+
+local i = 1
+local j = 1
+local y = 1
+di in blue _col(`dec') "{bf:Missing}" _c
+local col = `col'+11
+di in blue _col(`=`col'+2') "{bf:N}" _c
+
+local col = `col'+9
+di _col(`col') "{bf:Response categories}" _c
+local col = `dec'+18+8*`b'
+di _col(`col') "{bf:Alpha}" _c
+local col = `col'+9
+di _col(`col') "{bf:Loevinger}" _c  
+local col = `col'+12
+di _col(`col') "{bf:Number of}" 
+
+local col = `dec'-1
+di _col(`col') "{bf:data rate}" _c
+local col = `dec'+18
+foreach m in `lev' {
+	di _col(`=`col'+2') "`m'" _c
+	local col = `col'+8
+}
+local col = `dec'+17+8*`b'
+di as result _col(`col') "- item" _c
+local col = `col'+10
+di as result _col(`col') "Hj coeff" _c 
+local col = `col'+12
+di as result _col(`col') "NS Hjk"  
+
+
+
+local ch = `dec'+18+8*`b'+29
+di "{hline `ch'}"
+
+local i = 1 
+foreach x in `varlist' {
+	local varo`i' = "`x'"
+	local `++i'
+}
+
+
+local y = 1
+foreach p in `partition' {
+	
+	if `j' == 1 local s = `p'
+	else local s = `s' +`p'
+	
+	forvalues z = `y'/`s' { 
+		local col = `dec'
+		di "{bf:`var`z''}" _c
+		local t = d[`z',1]
+		local t : di %8.2f `t' 
+		di _col(`col') "{text:`t'}" _c
+		qui count if missing(`varo`z'')
+		local m = r(N)
+		local N = _N-`m'
+		local N : di %4.0f `N'
+		local col = `col'+10
+		di _col(`col') "{text:`N'}" _c
+		
+		local col = `col'+8
+		foreach m in `lev' {
+			
+			local f = 0
+			qui levelsof `varo`z'', local(levels)
+			foreach l in `levels' {
+				if strpos("`levels'","`m'") == 0 {
+					local f = 1
+				}
+			}
+			/*if `f' == 1 {
+				di _col(`=`col'+2') "_" _c
+			}*/
+			*else {
+				qui count if round(`varo`z'') == `m'
+				local n = r(N)
+				qui count if `varo`z'' != . 
+				local d = r(N)
+				local e = `n'/`d'
+				local e : di %4.2f `e'
+				if `e' != 0 di _col(`=`col'-1')"{text:`e'}" _c
+				else di _col(`=`col'-1')"{text:   -}" _c
+				
+			*}
+			local col = `col'+8
+		}
+		local col = `dec'+18+8*`b'
+		local a = d[`z',2]
+		local a : di %4.2f `a'
+		di _col(`=`col'+1') "{text:`a'}" _c
+		local h = d[`z',3]
+		local h : di %5.2f `h'
+		local col = `col'+10
+		di _col(`=`col'+3') "{text:`h'}" _c
+		local ns = d[`z',4]
+		local ns : di %1.0f `ns'
+		local col = `col'+12
+		di _col(`=`col'+7') "{text:`ns'}" 
+		
+		
+	}
+	local `i++' 
+	local `j++' 
+	local y = `s'+1
+	di "{dup `ch':-}" 
+}
+end
+
+*descitems iociociociociocicocio1-ioc37, part(4 4 7 3 3 4 7 5)
+*descitems ptgi1-peur16, part(4 4 7 3 3 4 7 5)
+*descitems x1-x30, part(5 5 5 5 5 4 1)
diff --git a/Modules/ado/personal/d/descscale.ado b/Modules/ado/personal/d/descscale.ado
new file mode 100644
index 0000000..a3f1829
--- /dev/null
+++ b/Modules/ado/personal/d/descscale.ado
@@ -0,0 +1,293 @@
+*! Version 1.2 29 August 2019
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* Stata program : descscale
+* Description of a scale and covariates
+* Release 1.1 : June 4, 2019  
+*
+*
+* Historic :
+* Version 1 (April 12, 2019) [Jean-Benoit Hardouin]
+* Version 1.1 (June 4, 2019) [Jean-Benoit Hardouin]
+* Version 1.2 (August 29, 2019) [Jean-Benoit Hardouin] /*correction of bugs*/
+*
+* Jean-benoit Hardouin, PhD, Assistant Professor
+* Team of Methods in Patient Centered Outcomes and Health Research  (INSERM U1246-SPHERE)
+* University of Nantes - Faculty of Pharmaceutical Sciences
+* France
+* jean-benoit.hardouin@anaqol.org
+*
+*
+* News about this program :http://www.anaqol.org
+*
+* Copyright 2019 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 descscale , rclass
+version 8.2
+syntax varlist [if] [in] [,MIN(int 0) CONTinuous(varlist) CATegorical(varlist) All PARTition(numlist) Large MEAN MAXlevels(int 20)]
+
+preserve
+marksample touse,novarlist
+label variable `touse' "selection"
+
+tokenize `varlist'
+local nbitems: word count `varlist'
+if "`partition'"=="" {
+    local partition `nbitems'
+}
+local nbdim:word count `partition'
+forvalues i=1/`nbdim' {
+   local nbitemsdim`i': word `i' of `partition'
+}
+qui count if `touse'
+local N=r(N)
+local maxr=0
+forvalues i=1/`nbitems' {
+   qui levelsof ``i'' if `touse'
+   local r`i'=r(r)
+   qui su ``i'' if `touse'
+   local max`i'=r(max)
+   local mean`i'=r(mean)
+   qui count if ``i''!=0&``i''!=1&``i''!=2&``i''!=3&``i''!=4&``i''!=5&``i''!=6&``i''!=7&``i''!=8&``i''!=9&``i''!=10&``i''!=.
+   local ninc=r(N)
+   local ok`i'=1
+   if `r`i''>11&"`large'"=="" {
+       di in red "The number of answer categories to the items ``i'' is large (>11). Use the -large- option or correct the list of items." 
+       local ok`i'=0
+	   exit
+   }
+   if `ninc'>0 {
+       di in red "The variable ``i'' has incompatible values with an items (integers between 0 and 10)." 
+       local ok`i'=0
+	   exit
+   }
+   if `max`i''>`maxr' {
+      local maxr `max`i''
+   }	  
+}
+di "maxr=`maxr'"
+di
+di in green "Number of individuals : " as result `N'
+
+di
+di in green "Description of the items"
+local long=(`maxr'+1-`min')*8+40
+di in green "{hline `long'}"
+
+di in green "Items" _col(23) "Obs" _c 
+local col=33
+forvalues j=`min'/`maxr' {
+    di _col(`col') "`j'" _c
+	local col=`col'+8
+}
+di _col(`col') "." _col(`=`col'+4') "Mean"
+di in green "{hline `long'}"
+
+local deb=1
+forvalues d=1/`nbdim' {
+    local loi`d' 
+    *di "local fin=`deb'+`nbitemsdim`d''-1"
+    local fin=`deb'+`nbitemsdim`d''-1
+	forvalues i=`deb'/`fin' {
+        if `ok`i''==1 {
+			local loi`d' `loi`d'' ``i'' 
+			qui count if `touse'&``i''!=.
+			local k=abbrev("``i''",20)
+			di in green "`k'" _col(22) as result %4.0f `r(N)' _c
+			local col=28
+			forvalues j=`min'/`maxr' {
+				qui count if `touse'&``i''==`j'
+				local per=round(`r(N)'/`N'*100, 0.1)
+				di _col(`col') %5.1f `per' "%" _c
+				local col=`col'+8
+			}
+			qui count  if `touse'&``i''==.
+			local per=round(`r(N)'/`N'*100,0.1)
+			di _col(`col') %5.1f `per' "%" _col(`=`col'+9') %4.2f `mean`i''
+		}
+	}
+	if `d'!=`nbdim' {
+	   di in green "{dup `long':-}"
+	}
+	local deb=`fin'+1
+}
+di in green "{hline `long'}"
+
+di
+di in green "Description of the scores"
+local long2=72
+di in green "{hline `long2'}"
+di in green "Scores" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+di in green "{hline `long2'}"
+
+forvalues d=1/`nbdim' {
+   tempname score`d'
+   genscore `loi`d'' if `touse', score(`score`d'') `mean' `standardized'
+   qui su `score`d'' if `touse'
+   di in green "score`d'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+}
+di in green "{hline `long2'}"
+
+
+if "`continuous'"!="" {
+   local continuous2
+   foreach i of varlist `continuous' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local continuous2 `continuous2' `i'
+	  }
+   }
+   local continuous `continuous2'
+}
+if "`categorical'"!="" {
+   local categorical2
+   foreach i of varlist `categorical' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local categorical2 `categorical2' `i'
+	  }
+   }
+   local categorical `categorical2'
+}
+
+if "`all'"!="" {
+   *local continuous
+   *local categorial
+   foreach i of varlist * {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+      qui levelsof `i' if `touse'
+ 	  local r=r(r)
+      if `r'>7&`candidate'==1 {
+		 local continuous `continuous' `i'
+	  }
+	  else {
+         local categorical `categorical' `i'
+	  }
+   }
+}
+
+*di "CONTINUOUS : `continuous'"
+*di "CATEGORICAL : `categorical'"
+
+if "`continuous'"!="" {
+    di
+    di "Descriptive analysis of continuous covariates"
+	local long2=72
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+	di in green "{hline `long2'}"
+	foreach i of varlist `continuous' {
+	   qui su `i' if `touse'
+	   local k=abbrev("`i'",18)
+	   *di in green "`k'" _c
+	   di in green "`k'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+	}
+	di in green "{hline `long2'}"
+}
+if "`categorical'"!="" {
+    di
+	*set trace on
+    di "Descriptive analysis of categorical covariates"
+	local long2=55
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(25)  "Levels" _col(37) "Freq." _col(49) "Percent" 
+	di in green "{hline `long2'}"
+	local nbc: word count `categorical'
+	local m=1
+    local nonret
+	foreach i of varlist `categorical' {
+	   local type : type `i'
+	   local type=substr("`type'",1,3)
+	   qui levelsof `i'
+	   local lev "`r(levels)'"
+	   local nblev=r(r)
+	   if (`maxlevels'>=`nblev') {
+		   local k=abbrev("`i'",20)
+		   di in green "`k'" _c
+		   if "`type'"=="str" {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'=="`j'"
+				 local k=abbrev("`j'",10)
+				 di _col(21) %10s in green "`k'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==""
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }  
+		   else {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'==`j'
+				 di _col(21) %10s in green "`j'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==.
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }
+		   if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+		   }
+		   else {
+			  di in green "{dup `long2':-}"
+		   }
+	   }
+	   else {
+	      local nonret `nonret' `i'
+	      *di "local nonret `nonret' `i'"
+		  *di "non retenu `i'"
+		  if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+          }
+	   }
+	   local ++m
+	}
+	if "`nonret'"!="" {
+	   di in green "Not described variables (too more levels) : " as result "`nonret'"
+	}
+}
+end
diff --git a/Modules/ado/personal/d/descscalev1.1.ado b/Modules/ado/personal/d/descscalev1.1.ado
new file mode 100644
index 0000000..6508346
--- /dev/null
+++ b/Modules/ado/personal/d/descscalev1.1.ado
@@ -0,0 +1,290 @@
+*! Version 1.1 4 June 2019
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* Stata program : descscale
+* Description of a scale and covariates
+* Release 1.1 : June 4, 2019  
+*
+*
+* Historic :
+* Version 1 (April 12, 2019) [Jean-Benoit Hardouin]
+* Version 1.1 (June 4, 2019) [Jean-Benoit Hardouin]
+*
+* Jean-benoit Hardouin, PhD, Assistant Professor
+* Team of Methods in Patient Centered Outcomes and Health Research  (INSERM U1246-SPHERE)
+* University of Nantes - Faculty of Pharmaceutical Sciences
+* France
+* jean-benoit.hardouin@anaqol.org
+*
+*
+* News about this program :http://www.anaqol.org
+*
+* Copyright 2019 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 descscale , rclass
+version 8.2
+syntax varlist [if] [in] [,MIN(int 0) CONTinuous(varlist) CATegorical(varlist) All PARTition(numlist) Large MEAN MAXlevels(int 20)]
+
+preserve
+marksample touse,novarlist
+label variable `touse' "selection"
+
+tokenize `varlist'
+local nbitems: word count `varlist'
+if "`partition'"=="" {
+    local partition `nbitems'
+}
+local nbdim:word count `partition'
+forvalues i=1/`nbdim' {
+   local nbitemsdim`i': word `i' of `partition'
+}
+qui count if `touse'
+local N=r(N)
+local maxr=0
+forvalues i=1/`nbitems' {
+   qui levelsof ``i'' if `touse'
+   local r`i'=r(r)
+   qui su ``i'' if `touse'
+   local max`i'=r(max)
+   local mean`i'=r(mean)
+   qui count if ``i''!=0&``i''!=1&``i''!=2&``i''!=3&``i''!=4&``i''!=5&``i''!=6&``i''!=7&``i''!=8&``i''!=9&``i''!=10&``i''!=.
+   local ninc=r(N)
+   local ok`i'=1
+   if `r`i''>11&"`large'"=="" {
+       di in red "The number of answer categories to the items ``i'' is large (>11). Use the -large- option or correct the list of items." 
+       local ok`i'=0
+	   exit
+   }
+   if `ninc'>0 {
+       di in red "The variable ``i'' has incompatible values with an items (integers between 0 and 10)." 
+       local ok`i'=0
+	   exit
+   }
+   if `r`i''>`maxr' {
+      local maxr `r`i''
+   }	  
+}
+*di "maxr=`maxr'"
+di
+di in green "Number of individuals : " as result `N'
+
+di
+di in green "Decription of the items"
+local long=(`maxr'+1-`min')*8+40
+di in green "{hline `long'}"
+
+di in green "Items" _col(23) "Obs" _c 
+local col=33
+forvalues j=`min'/`maxr' {
+    di _col(`col') "`j'" _c
+	local col=`col'+8
+}
+di _col(`col') "." _col(`=`col'+4') "Mean"
+di in green "{hline `long'}"
+
+local deb=1
+forvalues d=1/`nbdim' {
+    local loi`d' 
+    *di "local fin=`deb'+`nbitemsdim`d''-1"
+    local fin=`deb'+`nbitemsdim`d''-1
+	forvalues i=`deb'/`fin' {
+        if `ok`i''==1 {
+			local loi`d' `loi`d'' ``i'' 
+			qui count if `touse'&``i''!=.
+			local k=abbrev("``i''",20)
+			di in green "`k'" _col(22) as result %4.0f `r(N)' _c
+			local col=28
+			forvalues j=`min'/`maxr' {
+				qui count if `touse'&``i''==`j'
+				local per=round(`r(N)'/`N'*100, 0.1)
+				di _col(`col') %5.1f `per' "%" _c
+				local col=`col'+8
+			}
+			qui count  if `touse'&``i''==.
+			local per=round(`r(N)'/`N'*100,0.1)
+			di _col(`col') %5.1f `per' "%" _col(`=`col'+9') %4.2f `mean`i''
+		}
+	}
+	if `d'!=`nbdim' {
+	   di in green "{dup `long':-}"
+	}
+	local deb=`fin'+1
+}
+di in green "{hline `long'}"
+
+di
+di in green "Decription of the scores"
+local long2=72
+di in green "{hline `long2'}"
+di in green "Scores" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+di in green "{hline `long2'}"
+
+forvalues d=1/`nbdim' {
+   tempname score`d'
+   genscore `loi`d'' if `touse', score(`score`d'') `mean' `standardized'
+   qui su `score`d'' if `touse'
+   di in green "score`d'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+}
+di in green "{hline `long2'}"
+
+
+if "`continuous'"!=""|"`categorical'"!="" {
+   local continuous2
+   foreach i of varlist `continuous' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local continuous2 `continuous2' `i'
+	  }
+   }
+   local continuous `continuous2'
+   local categorical2
+   foreach i of varlist `categorical' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local categorical2 `categorical2' `i'
+	  }
+   }
+   local categorical `categorical2'
+}
+
+if "`all'"!="" {
+   *local continuous
+   *local categorial
+   foreach i of varlist * {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+      qui levelsof `i' if `touse'
+ 	  local r=r(r)
+      if `r'>7&`candidate'==1 {
+		 local continuous `continuous' `i'
+	  }
+	  else {
+         local categorical `categorical' `i'
+	  }
+   }
+}
+
+*di "CONTINUOUS : `continuous'"
+*di "CATEGORICAL : `categorical'"
+
+if "`continuous'"!="" {
+    di
+    di "Descriptive analysis of continuous covariates"
+	local long2=72
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+	di in green "{hline `long2'}"
+	foreach i of varlist `continuous' {
+	   qui su `i' if `touse'
+	   local k=abbrev("`i'",18)
+	   *di in green "`k'" _c
+	   di in green "`k'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+	}
+	di in green "{hline `long2'}"
+}
+if "`categorical'"!="" {
+    di
+	*set trace on
+    di "Descriptive analysis of categorical covariates"
+	local long2=55
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(25)  "Levels" _col(37) "Freq." _col(49) "Percent" 
+	di in green "{hline `long2'}"
+	local nbc: word count `categorical'
+	local m=1
+    local nonret
+	foreach i of varlist `categorical' {
+	   local type : type `i'
+	   local type=substr("`type'",1,3)
+	   qui levelsof `i'
+	   local lev "`r(levels)'"
+	   local nblev=r(r)
+	   if (`maxlevels'>=`nblev') {
+		   local k=abbrev("`i'",20)
+		   di in green "`k'" _c
+		   if "`type'"=="str" {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'=="`j'"
+				 local k=abbrev("`j'",10)
+				 di _col(21) %10s in green "`k'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==""
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }  
+		   else {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'==`j'
+				 di _col(21) %10s in green "`j'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==.
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }
+		   if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+		   }
+		   else {
+			  di in green "{dup `long2':-}"
+		   }
+	   }
+	   else {
+	      local nonret `nonret' `i'
+	      *di "local nonret `nonret' `i'"
+		  *di "non retenu `i'"
+		  if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+          }
+	   }
+	   local ++m
+	}
+	if "`nonret'"!="" {
+	   di in green "Not described variables (too more levels) : " as result "`nonret'"
+	}
+}
+end
diff --git a/Modules/ado/personal/d/descscalev1.ado b/Modules/ado/personal/d/descscalev1.ado
new file mode 100644
index 0000000..8c842a1
--- /dev/null
+++ b/Modules/ado/personal/d/descscalev1.ado
@@ -0,0 +1,279 @@
+*! Version 1 11 April 2019
+*! Jean-Benoit Hardouin
+************************************************************************************************************
+* Stata program : descscale
+* Description of a scale and covariates
+* Release 1 : April 12, 2019  
+*
+*
+* Historic :
+* Version 1 (April 12, 2019) [Jean-Benoit Hardouin]
+*
+* Jean-benoit Hardouin, PhD, Assistant Professor
+* Team of Methods in Patient Centered Outcomes and Health Research  (INSERM U1246-SPHERE)
+* University of Nantes - Faculty of Pharmaceutical Sciences
+* France
+* jean-benoit.hardouin@anaqol.org
+*
+*
+* News about this program :http://www.anaqol.org
+*
+* Copyright 2019 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 descscale , rclass
+version 8.2
+syntax varlist [if] [in] [,MIN(int 0) CONTinuous(varlist) CATegorical(varlist) All PARTition(numlist) Large MEAN MAXlevels(int 20)]
+
+preserve
+marksample touse,novarlist
+label variable `touse' "selection"
+
+tokenize `varlist'
+local nbitems: word count `varlist'
+if "`partition'"=="" {
+    local partition `nbitems'
+}
+local nbdim:word count `partition'
+forvalues i=1/`nbdim' {
+   local nbitemsdim`i': word `i' of `partition'
+}
+qui count if `touse'
+local N=r(N)
+local maxr=0
+forvalues i=1/`nbitems' {
+   qui levelsof ``i'' if `touse'
+   local r`i'=r(r)
+   qui su ``i'' if `touse'
+   local max`i'=r(max)
+   local mean`i'=r(mean)
+   if `r`i''>11&"`large'"=="" {
+       di in red "The number of answer categories to the items is large (>11). Use the -large- option or correct the list of items." 
+	   exit
+   }
+   if `r`i''>`maxr' {
+      local maxr `r`i''
+   }	  
+}
+*di "maxr=`maxr'"
+di
+di in green "Number of individuals : " as result `N'
+
+di
+di in green "Decription of the items"
+local long=(`maxr'+1-`min')*8+40
+di in green "{hline `long'}"
+
+di in green "Items" _col(23) "Obs" _c 
+local col=33
+forvalues j=`min'/`maxr' {
+    di _col(`col') "`j'" _c
+	local col=`col'+8
+}
+di _col(`col') "." _col(`=`col'+4') "Mean"
+di in green "{hline `long'}"
+
+local deb=1
+forvalues d=1/`nbdim' {
+    local loi`d' 
+    *di "local fin=`deb'+`nbitemsdim`d''-1"
+    local fin=`deb'+`nbitemsdim`d''-1
+	forvalues i=`deb'/`fin' {
+        local loi`d' `loi`d'' ``i'' 
+     	qui count if `touse'&``i''!=.
+	    local k=abbrev("``i''",20)
+	    di in green "`k'" _c
+		di in green "`k'" _col(22) as result %4.0f `r(N)' _c
+		local col=28
+		forvalues j=`min'/`maxr' {
+     		qui count if `touse'&``i''==`j'
+			local per=round(`r(N)'/`N'*100, 0.1)
+			di _col(`col') %5.1f `per' "%" _c
+			local col=`col'+8
+		}
+		qui count  if `touse'&``i''==.
+		local per=round(`r(N)'/`N'*100,0.1)
+		di _col(`col') %5.1f `per' "%" _col(`=`col'+9') %4.2f `mean`i'' 
+	}
+	if `d'!=`nbdim' {
+	   di in green "{dup `long':-}"
+	}
+	local deb=`fin'+1
+}
+di in green "{hline `long'}"
+
+di
+di in green "Decription of the scores"
+local long2=72
+di in green "{hline `long2'}"
+di in green "Scores" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+di in green "{hline `long2'}"
+
+forvalues d=1/`nbdim' {
+   tempname score`d'
+   genscore `loi`d'' if `touse', score(`score`d'') `mean' `standardized'
+   qui su `score`d'' if `touse'
+   di in green "score`d'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+}
+di in green "{hline `long2'}"
+
+
+if "`continuous'"!=""|"`categorical'"!="" {
+   local continuous2
+   foreach i of varlist `continuous' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local continuous2 `continuous2' `i'
+	  }
+   }
+   local continuous `continuous2'
+   local categorical2
+   foreach i of varlist `categorical' {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  if `candidate'==1 {
+		 qui levelsof `i' if `touse'
+		 local r=r(r)
+         local categorical2 `categorical2' `i'
+	  }
+   }
+   local categorical `categorical2'
+}
+
+if "`all'"!="" {
+   *local continuous
+   *local categorial
+   foreach i of varlist * {
+      local candidate=1
+	  forvalues j=1/`nbitems' {
+	     if "`i'"=="``j''" {
+		    local candidate=0
+		 }
+      }
+	  local type : type `i'
+	  local type=substr("`type'",1,3)
+	  if "`type'"=="str" {
+		 local candidate=0
+	  }
+      qui levelsof `i' if `touse'
+ 	  local r=r(r)
+      if `r'>7&`candidate'==1 {
+		 local continuous `continuous' `i'
+	  }
+	  else {
+         local categorical `categorical' `i'
+	  }
+   }
+}
+
+*di "CONTINUOUS : `continuous'"
+*di "CATEGORICAL : `categorical'"
+
+if "`continuous'"!="" {
+    di
+    di "Descriptive analysis of continuous covariates"
+	local long2=72
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(22)  "Obs" _col(33)  "Mean" _col(40) "Std. Dev." _col(58) "Min" _col(70) "Max"
+	di in green "{hline `long2'}"
+	foreach i of varlist `continuous' {
+	   qui su `i' if `touse'
+	   local k=abbrev("`i'",18)
+	   *di in green "`k'" _c
+	   di in green "`k'" _col(20) as result %5.0f `r(N)' _col(30) %7.2f `r(mean)' _col(42) %7.2f `r(sd)' _col(54) %7.2f `r(min)' _col(66) %7.2f `r(max)' 
+	}
+	di in green "{hline `long2'}"
+}
+if "`categorical'"!="" {
+    di
+	*set trace on
+    di "Descriptive analysis of continuous covariates"
+	local long2=55
+	di in green "{hline `long2'}"
+	di in green "Variables" _col(25)  "Levels" _col(37) "Freq." _col(49) "Percent" 
+	di in green "{hline `long2'}"
+	local nbc: word count `categorical'
+	local m=1
+    local nonret
+	foreach i of varlist `categorical' {
+	   local type : type `i'
+	   local type=substr("`type'",1,3)
+	   qui levelsof `i'
+	   local lev "`r(levels)'"
+	   local nblev=r(r)
+	   if (`maxlevels'>=`nblev') {
+		   local k=abbrev("`i'",20)
+		   di in green "`k'" _c
+		   if "`type'"=="str" {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'=="`j'"
+				 local k=abbrev("`j'",10)
+				 di _col(21) %10s in green "`k'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==""
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }  
+		   else {
+			  foreach j in `lev' {
+				 qui count if `touse'&`i'==`j'
+				 di _col(21) %10s in green "`j'" as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+			  qui count if `touse'&`i'==.
+			  if `r(N)'!=0 {
+			     di as result %5.0f _col(37) `r(N)' %6.2f _col(49) `=`r(N)'/`N'*100' "%"
+			  }
+		   }
+		   if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+		   }
+		   else {
+			  di in green "{dup `long2':-}"
+		   }
+	   }
+	   else {
+	      local nonret `nonret' `i'
+	      *di "local nonret `nonret' `i'"
+		  *di "non retenu `i'"
+		  if `m'==`nbc' {
+			  di in green "{hline `long2'}"
+          }
+	   }
+	   local ++m
+	}
+	if "`nonret'"!="" {
+	   di in green "Not described variables (too more levels) : " as result "`nonret'"
+	}
+}
+end
diff --git a/Modules/ado/personal/d/detect.ado b/Modules/ado/personal/d/detect.ado
new file mode 100644
index 0000000..6162a4a
--- /dev/null
+++ b/Modules/ado/personal/d/detect.ado
@@ -0,0 +1,302 @@
+************************************************************************************************************
+* DETECT: detect, Iss and R indexes
+* Version 3.1: May 13, 2004
+*
+* Historic
+* Version 1 (2003-06-20): Jean-Benoit Hardouin
+* Version 2 (2004-01-18): Jean-Benoit Hardouin
+* Version 3 (2004-01-26): Jean-Benoit Hardouin
+* Version 3.1 (2004-05-13): Jean-Benoit Hardouin
+* Version 4 (2009-11-24): Jean-Benoit Hardouin /*corrections*/
+*
+* Jean-benoit Hardouin, Regional Health Observatory of Orléans - France
+* jean-benoit.hardouin@neuf.fr
+*
+* News about this program : http://anaqol.free.fr
+* FreeIRT Project : http://freeirt.free.fr
+*
+* Copyright 2003, 2004 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 detect , rclass
+version 7.0
+syntax varlist(min=2 numeric), PARTition(numlist integer >0) [noSCOres noRESTscores]
+
+local nbitemstest=0
+tokenize `partition'
+local Q:word count `partition'
+local firstitem=0
+local dim0=1
+forvalues i=1/`Q' {
+	local dim`i'=``i''
+	local firstitem`i'=`firstitem`=`i'-1''+`dim`=`i'-1''
+	local nbitemstest=`nbitemstest'+`dim`i''
+	tempvar score`i'
+	qui gen `score`i''=0
+	forvalues j=`firstitem`i''/`=`firstitem`i''+`dim`i''-1' {
+		local item`j': word `j' of `varlist'
+		qui replace `score`i''=`score`i''+`item`j''
+	}
+}
+
+
+
+local nbitems:word count `varlist'
+tokenize `varlist'
+
+if `nbitems'!=`nbitemstest' {
+	di in red "The sum of the numbers of items in all the dimensions is different of the total number of items precised in varlist"
+exit
+}
+
+tempname Corrscores Corrrestscores
+
+matrix define `Corrscores'=J(`nbitems',`Q',0)
+matrix define `Corrrestscores'=J(`nbitems',`Q',0)
+
+
+forvalues i=1/`nbitems' {
+	forvalues j=1/`Q' {
+		tempvar restscore`i's`j'
+		qui gen `restscore`i's`j''=`score`j''-``i''
+		qui corr ``i'' `score`j''
+		local corr`i's`j'=r(rho)
+		qui corr ``i'' `restscore`i's`j''
+		local corr`i'rs`j'=r(rho)
+		matrix `Corrscores'[`i',`j']=`corr`i's`j''
+		matrix `Corrrestscores'[`i',`j']=`corr`i'rs`j''
+	}
+}
+
+
+qui count
+local nbind=r(N)
+
+tempvar score
+qui gen `score'=0
+forvalues i=1/`nbitems' {
+	qui replace `score'=`score'+``i''
+}
+
+forvalues i=1/`nbitems' {
+	local tmp=`i'+1
+	forvalues j=`tmp'/`nbitems' {
+		tempvar restscorei`i'j`j'
+		qui gen `restscorei`i'j`j''=`score'-``i''-``j''
+	}
+}
+
+forvalues k=0/`nbitems'{
+	tempname Tcov`k'
+	qui count if `score'==`k'
+	local n`k'=r(N)
+	if `n`k''>1 {
+	   qui matrix accum `Tcov`k''=`varlist' if `score'==`k',nocons dev
+	}
+	else {
+           matrix `Tcov`k''=J(`nbitems',`nbitems',0)
+        }
+	if `n`k''!=0 {
+		matrix `Tcov`k''=`Tcov`k''/`n`k''
+	}
+}
+
+
+forvalues i=1/`nbitems'{
+	local tmp=`i'+1
+	forvalues j=`tmp'/`nbitems' {
+		local tmp=`nbitems'-2
+		forvalues k=0/`tmp' {
+			tempname Rcovi`i'j`j'k`k'
+			qui count if `restscorei`i'j`j''==`k'
+			local ni`i'j`j'k`k'=r(N)
+                        if `ni`i'j`j'k`k''>1 {
+			   qui matrix accum `Rcovi`i'j`j'k`k''=`varlist' if `restscorei`i'j`j''==`k',nocons dev
+			}
+			else {
+                           matrix  `Rcovi`i'j`j'k`k''=J(`nbitems',`nbitems',0)
+                        }
+                        if `ni`i'j`j'k`k''!=0 {
+				matrix `Rcovi`i'j`j'k`k''=`Rcovi`i'j`j'k`k''/`ni`i'j`j'k`k''
+			}
+		}
+	}
+}
+
+
+tempname delta
+matrix `delta'=J(`nbitems',`nbitems',-1)
+
+local debut=1
+local fin=0
+forvalues i=1/`Q' {
+	local fin=`fin'+`dim`i''
+	forvalues j=`debut'/`fin' {
+		forvalues k=`debut'/`fin' {
+			matrix `delta'[`j',`k']=1
+		}
+	}
+	local debut=`debut'+`dim`i''
+}
+
+tempname Tcov Rcov Covfin Issm Abscov
+
+matrix  `Tcov'=J(`nbitems',`nbitems',0)
+matrix  `Rcov'=J(`nbitems',`nbitems',0)
+forvalues k=0/`nbitems' {
+	matrix `Tcov'=`Tcov'+`Tcov`k''*`n`k''
+}
+forvalues i=1/`nbitems'{
+	local tmp=`i'+1
+	forvalues j=`tmp'/`nbitems' {
+		local tmp=`nbitems'-2
+		forvalues k=0/`tmp' {
+			matrix `Rcov'[`i',`j']=`Rcov'[`i',`j']+`Rcovi`i'j`j'k`k''[`i',`j']*`ni`i'j`j'k`k''
+			matrix `Rcov'[`j',`i']=`Rcov'[`i',`j']
+		}
+	}
+}
+
+
+matrix  `Covfin'=J(`nbitems',`nbitems',0)
+matrix  `Issm'=J(`nbitems',`nbitems',0)
+matrix  `Abscov'=J(`nbitems',`nbitems',0)
+forvalues i=1/`nbitems' {
+	forvalues j=1/`nbitems' {
+		matrix `Covfin'[`i',`j']=(`Tcov'[`i',`j']+`Rcov'[`i',`j'])/2*`delta'[`i',`j']
+		matrix `Issm'[`i',`j']=sign(`Tcov'[`i',`j']+`Rcov'[`i',`j'])*`delta'[`i',`j']
+		matrix `Abscov'[`i',`j']=abs(`Tcov'[`i',`j']+`Rcov'[`i',`j'])/2
+	}
+}
+
+local somme=0
+local Iss=0
+local R=0
+forvalues i=1/`nbitems' {
+	local tmp=`i'+1
+	forvalues j=`tmp'/`nbitems' {
+		local somme=`somme'+`Covfin'[`i',`j']
+		local Iss=`Iss'+`Issm'[`i',`j']
+		local R=`R'+`Abscov'[`i',`j']
+	}
+}
+local DETECT=`somme'/(`nbind'*`nbitems'*(`nbitems'-1))
+local DETECT=`somme'/(`nbitems'*(`nbitems'-1))
+local Iss=`Iss'*2/(`nbitems'*(`nbitems'-1))
+*local R=`DETECT'/(`R'/(`nbind'*`nbitems'*(`nbitems'-1)))
+local R=`DETECT'/(`R'/(`nbitems'*(`nbitems'-1)))
+
+
+di 
+di in green _col(20)  "DETECT : " as result %5.4f `DETECT'
+di in green _col(23)  "Iss : " as result %5.4f `Iss'
+di in green _col(25)  "R : " as result %5.4f `R'
+
+di 
+
+if "`scores'"=="" {
+	di _col(5) in green "Correlations Items-Scores"
+	di in green _col(5) "{hline 25}"
+	di
+	di in green _col(5) "Items"  _continue
+	local col=10
+	forvalues q=1/`Q' {
+		local col=`col'+10
+		di in green _col(`col') "dim `q'" _continue
+	}
+	di
+	local length=`Q'*10+10
+	di in green _col(5) "{hline `length'}"
+	forvalues i=1/`nbitems' {
+		forvalues q=2/`Q' {
+			if `i'==`firstitem`q'' {
+				di _col(5) in green _dup(`length') "-" 
+			}
+		}
+		di in green _col(5) "``i''" _continue
+		local col=5
+		forvalues q=1/`Q' {
+			local col=`col'+10
+			di in yellow _col(`col') %10.4f `corr`i's`q'' _continue
+		}
+		di
+	}
+	di in green _col(5) "{hline `length'}"
+	di
+}
+
+
+if "`restscore'"=="" {
+	di _col(5) in green "Correlations Items-Rest-Scores"
+	di in green _col(5) "{hline 30}"
+	di
+	di in green _col(5) "Items"  _continue
+	local col=10
+	forvalues q=1/`Q' {
+		local col=`col'+10
+		di in green _col(`col') "dim `q'" _continue
+	}
+	di
+	local length=`Q'*10+10
+	di in green _col(5) "{hline `length'}"
+	forvalues i=1/`nbitems' {
+		forvalues q=2/`Q' {
+			if `i'==`firstitem`q'' {
+				di _col(5) in green _dup(`length') "-" 
+			}
+		}
+		di in green _col(5) "``i''" _continue
+		local col=5
+		forvalues q=1/`Q' {
+			local col=`col'+10
+			di in yellow _col(`col') %10.4f `corr`i'rs`q'' _continue
+		}
+		di
+	}
+	di in green _col(5) "{hline `length'}"
+	di
+}
+	
+local namesdim
+forvalues q=1/`Q' {
+	local namesdim "`namesdim' dim`q'"
+}
+
+matrix rownames `Tcov'=`varlist'
+matrix rownames `Rcov'= `varlist'
+matrix rownames `Covfin'= `varlist'
+matrix rownames `Corrscores'= `varlist'
+matrix rownames `Corrrestscores'= `varlist'
+matrix colnames `Tcov'= `varlist'
+matrix colnames `Rcov' =`varlist'
+matrix colnames `Covfin'= `varlist'
+matrix colnames `Corrscores'= `namesdim'
+matrix colnames `Corrrestscores'= `namesdim'
+
+			
+return scalar DETECT=`DETECT'
+return scalar Iss=`Iss'
+return scalar R=`R'
+return matrix Tcov `Tcov'
+return matrix Rcov `Rcov'
+return matrix Covfin `Covfin'
+return matrix Corrscores `Corrscores'
+return matrix Corrrestscores `Corrrestscores'
+
+
+end
diff --git a/Modules/ado/personal/d/detect.hlp b/Modules/ado/personal/d/detect.hlp
new file mode 100644
index 0000000..b94f0df
--- /dev/null
+++ b/Modules/ado/personal/d/detect.hlp
@@ -0,0 +1,48 @@
+{smcl}
+{* 26jan2004}{...}
+{hline}
+help for {hi:detect}
+{hline}
+
+{title:DETECT Index}
+
+{p 8 14 2}{cmd:detect} {it:varlist} {cmd:,} {cmdab:part:ition}({it:numlist}) [{cmdab:nosco:res} {cmdab:norest:scores}]
+
+
+{p 4 4 2}{it:varlist} is a list of two existing dichotomous variables (items) or more. The first items of this list compose the first dimension, the following items define the second dimension, as so on.
+
+{p 4 4 2}{cmd:partition} permit to define the number of items in each dimension. The user must precise in the {it:numlist} the number of items in each dimension.
+
+{Options}
+
+{p 4 4 2}{cmd:noscores} permit to remove the table of the correlations between the items and the scores
+
+{p 4 4 2}{cmd:norestscores} permit to remove the table of the correlations between the items and the rest-scores
+
+
+{title:Description}
+
+{p 4 4 2}{cmd:detect} permit to compute the DETECT index (Dimensionality Evaluation to Enumerate Contributing Traits), as the Iss (Approximate Simple Structure Index) and the R indexes defined by Zhang and Stout (1999).
+
+
+{title:Remarks} 
+
+{p 4 4 2}For detailed information on the DETECT, Iss and R indexes, see Zhang and Stout (1999).
+
+{p 4 4 2}{cmd:detect} don't permit the use of polytomous items.
+
+{title:Example}
+
+	{cmd:. detect item1 item2 item3 item4 , partition(2 2) nosco norest}
+
+	{cmd:. detect item1 item2 item3 item4 item 5 item6 item7 , partition(3 2 2)}
+
+
+{title:References}
+
+{p 4 4 2}Zhang J. and Stout W., The theorical DETECT index of dimensionality and its application to approximate simple structure, {it: Psychometrika}, vol.64(2), 1999, pp. 213-249.
+
+{title:Author}
+
+{p 4 8 2}Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France.
+You can contact the author at {browse "mailto:jean-benoit.hardouin@neuf.fr":jean-benoit.hardouin@neuf.fr} and visit the websites {browse "http://anaqol.free.fr":AnaQol} and {browse "http://freeirt.free.fr":FreeIRT}
diff --git a/Modules/ado/personal/d/detect2.ado b/Modules/ado/personal/d/detect2.ado
new file mode 100644
index 0000000..d2510d4
--- /dev/null
+++ b/Modules/ado/personal/d/detect2.ado
@@ -0,0 +1,68 @@
+************************************************************************************************************
+* based on the command "detect" by Jean-Benoit Hardouin 
+************************************************************************************************************
+program define detect2 , rclass
+version 7.0
+syntax varlist(min=2 numeric), PARTition(numlist integer >0) [noSCOres noRESTscores]
+
+local nbitemstest=0
+tokenize `partition'
+local Q:word count `partition'
+local firstitem=0
+local dim0=1
+forvalues i=1/`Q' {
+	local dim`i'=``i''
+	local firstitem`i'=`firstitem`=`i'-1''+`dim`=`i'-1''
+	local nbitemstest=`nbitemstest'+`dim`i''
+	tempvar score`i'
+	qui gen `score`i''=0
+	forvalues j=`firstitem`i''/`=`firstitem`i''+`dim`i''-1' {
+		local item`j': word `j' of `varlist'
+		qui replace `score`i''=`score`i''+`item`j''
+	}
+}
+
+
+
+local nbitems:word count `varlist'
+tokenize `varlist'
+
+if `nbitems'!=`nbitemstest' {
+	di in red "The sum of the numbers of items in all the dimensions is different of the total number of items precised in varlist"
+exit
+}
+
+
+tempname Corrscores Corrrestscores
+
+matrix define `Corrscores'=J(`nbitems',`Q',0)
+matrix define `Corrrestscores'=J(`nbitems',`Q',0)
+
+
+forvalues i=1/`nbitems' {
+	forvalues j=1/`Q' {
+		tempvar restscore`i's`j'
+		qui gen `restscore`i's`j''=`score`j''-``i''
+		qui corr ``i'' `score`j''
+		local corr`i's`j'=r(rho)
+		qui corr ``i'' `restscore`i's`j''
+		local corr`i'rs`j'=r(rho)
+		matrix `Corrscores'[`i',`j']=`corr`i's`j''
+		matrix `Corrrestscores'[`i',`j']=`corr`i'rs`j''
+	}
+}
+
+
+
+local namesdim
+forvalues q=1/`Q' {
+	local namesdim "`namesdim' dim`q'"
+}
+
+matrix colnames `Corrscores'= `namesdim'
+matrix colnames `Corrrestscores'= `namesdim'
+return matrix Corrscores `Corrscores'
+return matrix Corrrestscores `Corrrestscores'
+
+
+end
diff --git a/Modules/ado/personal/d/dropmissing.ado b/Modules/ado/personal/d/dropmissing.ado
new file mode 100644
index 0000000..15f3a77
--- /dev/null
+++ b/Modules/ado/personal/d/dropmissing.ado
@@ -0,0 +1,23 @@
+program define dropmissing
+syntax varlist [, missing(string) delete]
+
+tokenize `varlist'
+local nbitems:word count `varlist'
+if "`missing'"=="" {
+	local missing="."
+}
+
+local nbmissing:word count `missing'
+
+forvalues i=1/`nbitems' {
+	forvalues j=1/`nbmissing' {
+		local miss:word `j' of `missing'
+		if "`delete'"!="" {
+                        drop if ``i''==`miss'
+                }
+                else {
+                        replace ``i'=. if ``i''==`miss'
+                }
+	}
+}
+end
diff --git a/Modules/ado/personal/e/estpop.ado b/Modules/ado/personal/e/estpop.ado
new file mode 100644
index 0000000..9cf9453
--- /dev/null
+++ b/Modules/ado/personal/e/estpop.ado
@@ -0,0 +1,344 @@
+************************************************************************************************************
+* Estpop : Estimation de la population d'une commune, d'un canton, d'un département de la région Centre
+* (ou de la région Centre toute entière)
+*
+* Version 2.1: 7 décembre 2004
+* Version 2: 2 décembre 2004
+* Version 1.5: 2 décembre 2004
+* Version 1.4: 21 octobre 2004
+* Version 1.3: 16 aout 2004
+* Version 1.2: 20 juillet 2004
+* Version 1.1: 16 juillet 2004
+* Version 1: 16 juillet 2004
+*
+* Jean-benoit Hardouin, Observatoire Régional de la Santé du Centre - Orléans - France
+* jean-benoit.hardouin@neuf.fr
+*
+* Copyright 2004 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 estpop,rclass
+version 7.0
+syntax ,  [annee(int 1999) codegeo(int 1) sexe(int 0) quinq juillet]
+capture su *
+if _rc==0 {
+   tempfile saveestpop
+   qui save `saveestpop',replace
+}
+preserve
+
+if `annee'>=1900&`annee'<2000 {
+   local annee=`annee'-1900
+}
+local sannee=1900+`annee'
+if `sannee'<1982 {
+  local `sannee'=`sannee'+100
+}
+
+
+if `annee'<82|`annee'>99 {
+   di in red "Vous devez indiquer une annee entre 1982 et 1999 (ou entre 82 et 99)"
+   exit
+}
+
+if `sexe'==0 {
+   local sexel hf
+}
+else if `sexe'==1 {
+   local sexel h
+}
+else if `sexe'==2 {
+   local sexel f
+}
+else {
+   di in red "Vous devez indiquer un sexe parmi 0 (2 sexes confondus), 1 (hommes) et 2 (femmes)"
+   exit
+}
+
+if `codegeo'>100 {/*SI COMMUNE OU ZE OU CANTON */
+  use "C:\ado\personal\files\rp`sexel'829099.dta", clear
+  sort codecom
+  merge codecom using "C:\ado\personal\files\nais8002.dta"
+
+  if `sexe'!=0 {
+    foreach i of numlist 80/99 00/02 {
+        if `sexe'==1 {
+           local ratio=1.05/2.05
+        }
+        else if `sexe'==2 {
+           local ratio=1/2.05
+        }
+        if `i'<10 {
+           qui replace naiss0`i'=naiss0`i'*`ratio'
+        }
+        else if `i'>=10 {
+           qui replace naiss`i'=naiss`i'*`ratio'
+        }
+     }
+  }
+
+  tempvar flag
+  gen `flag'=0
+
+
+  if `codegeo'==0 {
+    qui replace `flag'=1 if codecom==0
+  }
+  if `codegeo'==1 {
+    qui replace `flag'=1 if codecom!=0
+  }
+  if `codegeo'<100 {
+    qui replace `flag'=1 if dep==`codegeo'
+  }
+  if `codegeo'<1000&`codegeo'>100 {
+    qui replace `flag'=1 if arr==`codegeo'
+  }
+  if `codegeo'<10000&`codegeo'>1000&int(`codegeo'/100)!=24 {
+    qui replace `flag'=1 if pct==`codegeo'
+  }
+  if int(`codegeo'/100)==24 {
+    qui replace `flag'=1 if ze94==`codegeo'
+  }
+  if `codegeo'<100000&`codegeo'>10000 {
+    qui replace `flag'=1 if codecom==`codegeo'
+  }
+  qui keep if `flag'==1
+
+
+  if `annee'==82|`annee'==90|`annee'==99 {
+    forvalues i=1/95 {
+        qui su rp`annee'`sexel'`i'
+        local popr`i'=r(sum)
+        return scalar popr`i'=`popr`i''
+    }
+  }
+  if (`annee'>90&`annee'<99)|(`annee'>82&`annee'<90) {
+    if `annee'>90&`annee'<99 {
+             local first=`annee'-90
+             local last=98-`annee'
+             local anc=90
+             local suiv=99
+             local nbans=9
+    }
+    if `annee'>82&`annee'<90 {
+             local first=`annee'-82
+             local last=89-`annee'
+             local anc=82
+             local suiv=90
+             local nbans=8
+    }
+
+    forvalues i=1/`first' {
+             local tmp=`suiv'-`annee'+`i'
+             local tmp2=`suiv'-`annee'+1
+             tempvar rp`annee'`sexel'`i'
+             qui gen `rp`annee'`sexel'`i''=naiss`=`annee'-`i''+`i'*(rp`suiv'`sexel'`tmp'-naiss`=`annee'-`i'')/`tmp'
+             qui su `rp`annee'`sexel'`i''
+             local popr`i'=r(sum)
+             return scalar popr`i'=`popr`i''
+    }
+    local rp`annee'`sexel'94 rp`annee'`sexel'95
+    forvalues i=`=`anc'+1'/`=`suiv'-1' {
+            tempname rp`i'`sexel'94 rp`i'`sexel'95
+            qui gen `rp`i'`sexel'94'=rp`anc'`sexel'94+(rp`suiv'`sexel'94-rp`anc'`sexel'94)/`nbans'*(`i'-`anc')
+            qui gen `rp`i'`sexel'95'=rp`anc'`sexel'95+(rp`suiv'`sexel'95-rp`anc'`sexel'95)/`nbans'*(`i'-`anc')
+    }
+    qui su `rp`annee'`sexel'94'
+    local popr94=r(sum)
+    return scalar popr94=`popr94'
+    qui su `rp`annee'`sexel'95'
+    local popr95=r(sum)
+    return scalar popr95=`popr95'
+    forvalues i=`=`first'+1'/`=95-`nbans'' {
+             tempvar rp`annee'`sexel'`i'
+             local tmp=`anc'+`i'-`annee'
+             local tmp2=`suiv'+`i'-`annee'
+             qui gen `rp`annee'`sexel'`i''=rp`anc'`sexel'`tmp'+(`annee'-`anc')*(rp`suiv'`sexel'`tmp2'-rp`anc'`sexel'`tmp')/`nbans'
+             qui su `rp`annee'`sexel'`i''
+             local popr`i'=r(sum)
+             return scalar popr`i'=`popr`i''
+    }
+    forvalues i=`=95-`nbans'+1'/93 {
+             tempvar rp`annee'`sexel'`i'
+             local tmp=`anc'+`i'-`annee'
+             local tmp2=94-`i'+`annee'
+             qui gen `rp`annee'`sexel'`i''=rp`anc'`sexel'`tmp'+`=`annee'-`anc''*(`rp`tmp2'`sexel'94'-rp`anc'`sexel'`tmp')/`=`tmp2'-`anc''
+             qui su `rp`annee'`sexel'`i''
+             local popr`i'=r(sum)
+             return scalar popr`i'=`popr`i''
+    }
+    /*qui su naiss`annee'
+    local popr0=r(sum)
+    return scalar popr0=`popr0'*/
+
+  }
+  qui su naiss`annee'
+  local popr0=r(sum)
+
+  return scalar popr0=`popr0'
+}/*FIN SI COMMUNE ZE OU CANTON OU ANNEE<1990*/
+
+
+else if `codegeo'<100 { /*FRANCE, REGION OU DEPT*/
+  use "C:\ado\personal\files\popfrregdept8201.dta", clear
+  if `sexe'==0 {
+    replace sexe=0
+  }
+  if "`juillet'"=="" {
+    forvalues i=0/95 {
+      qui su age`i' if annee==`sannee'&codegeo==`codegeo'&sexe==`sexe'
+      local popr`i'=r(sum)
+      return scalar popr`i'=`popr`i''
+    }
+  }
+  else if "`juillet'"!="" {
+    local pannee=`sannee'+1
+    forvalues i=0/95 {
+      qui su age`i' if annee==`pannee'&codegeo==`codegeo'&sexe==`sexe'
+      local poppr`i'=r(sum)
+      qui su age`i' if annee==`sannee'&codegeo==`codegeo'&sexe==`sexe'
+      local popsr`i'=r(sum)
+      local popr`i'=(`poppr`i''+`popsr`i'')/2
+      return scalar popr`i'=`popr`i''
+    }
+  }
+}/*FIN FRANCE, REGION OU DEPT ET ANNEE>=1990*/
+
+
+local popr=0
+forvalues i=0/95 {
+   local popr=`popr'+`popr`i''
+}
+
+return scalar popr=`popr'
+local poprq1=`popr1'+`popr2'+`popr3'+`popr4'
+return scalar poprq1=`poprq1'
+
+foreach i of numlist 5(5)90 {
+        local poprq`i'=`popr`i''+`popr`=`i'+1''+`popr`=`i'+2''+`popr`=`i'+3''+`popr`=`i'+4''
+        return scalar poprq`i'=`poprq`i''
+}
+return scalar poprq0=`popr0'
+return scalar poprq95=`popr95'
+
+
+if `codegeo'==0 {
+        local libgeo France metropolitaine
+}
+else if `codegeo'==1 {
+        local libgeo Region Centre
+}
+else if `codegeo'==18 {
+        local libgeo Cher
+}
+else if `codegeo'==28 {
+        local libgeo Eure-et-Loir
+}
+else if `codegeo'==36 {
+        local libgeo Indre
+}
+else if `codegeo'==37 {
+        local libgeo Indre-et-Loire
+}
+else if `codegeo'==41 {
+        local libgeo Loir-et-Cher
+}
+else if `codegeo'==45 {
+        local libgeo Loiret
+}
+else {
+        qui trouve `codegeo'
+        local libgeo=r(trouve`codegeo')
+}
+di
+if `codegeo'>1000&`codegeo'<10000 {
+        di in green  "Niveau geographique : Canton de " in yellow "`libgeo'"
+}
+else if `codegeo'>10000&`codegeo'<100000 {
+        di in green  "Niveau geographique : Commune de " in yellow "`libgeo'"
+}
+else {
+        di in green  "Niveau geographique : " in yellow "`libgeo'"
+}
+if `sexe'==0 {
+        local libsexe Deux sexes
+}
+else if `sexe'==1 {
+        local libsexe Hommes
+}
+else if `sexe'==2 {
+        local libsexe Femmes
+}
+if "`juillet'"==""&`codegeo'<100{
+  local comm="Estimation au 1er janvier"
+}
+else if "`juillet'"!=""&`codegeo'<100{
+  local comm="Estimation au 1er juillet"
+}
+else if `codegeo'>100 {
+  local comm="Retropolation entre deux recensements pour"
+}
+
+di in green "Sexe : " in yellow "`libsexe'"
+di in green "Annee : " in yellow "`comm' `=`annee'+1900'"
+
+
+di
+di in green _col(4) "{hline 22}"
+di in green _col(4) "Age" _col(16) "Population"
+di in green _col(4) "{hline 22}"
+if "`quinq'"=="" {
+     forvalues i=0/95 {
+             if `i'<2 {
+                  di in green _col(4) "`i' an" _col(18) in yellow %8.0f `popr`i''
+             }
+             else if `i'==95 {
+                  di in green _col(4) "95 ans et plus" _col(18) in yellow %8.0f `popr`i''
+             }
+             else {
+                  di in green _col(4) "`i' ans" _col(18) in yellow %8.0f `popr`i''
+             }
+     }
+}
+else {
+     di in green _col(4) "0 an" _col(18) in yellow %8.0f `popr0'
+     di in green _col(4) "1-4 ans" _col(18) in yellow %8.0f `poprq1'
+
+     foreach i of numlist 5(5)90 {
+                  di in green _col(4) "`i'-`=`i'+4' ans" _col(18) in yellow %8.0f `poprq`i''
+     }
+     di in green _col(4) "95 ans et plus" _col(18) in yellow %8.0f `popr95'
+}
+
+di in green _col(4) "{hline 22}"
+di in green _col(4) "Total" _col(18) in yellow %8.0f `popr'
+
+restore , not
+qui drop _all
+if "`saveestpop'"!="" {
+   qui use `saveestpop'
+}
+
+end
+
+
+
+
+
+
+
diff --git a/Modules/ado/personal/files/Pop8204Dep.dta b/Modules/ado/personal/files/Pop8204Dep.dta
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