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2240 lines
73 KiB
Plaintext
2240 lines
73 KiB
Plaintext
*! version 8.2 15july2009
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*! Jean-Benoit Hardouin
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*************************************************************************************************************
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* Raschtestv7: Rasch model, fit tests and graphical representations
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* Corresponds to the version 8 of Raschtest (http://freeirt.org) for Stata7
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*
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* Historic:
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* Version 2.1 (2003-07-10): Jean-Benoit Hardouin
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* Version 3.1 (2004-01-02) : Jean-Benoit Hardouin
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* Version 7.1 (2005-03-30) : Jean-Benoit Hardouin
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* Version 7.2.1 (2005-05-21) : Jean-Benoit Hardouin
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* Version 7.3.1 (2005-07-02) : Jean-Benoit Hardouin
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* Version 7.3.2 (2005-10-05) : Jean-Benoit Hardouin /*correction for the *genlt* option with *method(cml)**/
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* Version 7.3.3 (2005-12-21) : Jean-Benoit Hardouin /*correction if scores are not represented with method(mml) or method(gee)*/
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* Version 7.4.1 (2006-01-17) : Jean-Benoit Hardouin /*Standardized OUTFIT and INFIT, DIF option*/
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* Version 7.4.2 (2006-03-21) : Jean-Benoit Hardouin /*Avoids a bug when the temporary files are saved in a directory containing special characters*/
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* Version 7.4.3 (2006-11-15) : Jean-Benoit Hardouin /*Avoids a bug when the directory defined in the dirsave option contains special characters*/
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* Version 7.5.1 (2007-04-20) : Jean-Benoit Hardouin /*Return results of the comp option*/
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* Version 7.6.1 (2008-06-26) : Jean-Benoit Hardouin /*Nold option, corrects a bug with the genlt option*/
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* Version 7.6.2 (2009-03-01) : Jean-Benoit Hardouin /*Correction of the SE of the latent trait, graph option, correction of a bug with the if option, PSI in MML*/
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* Version 8.1 (2009-06-24) : Jean-Benoit Hardouin /*DIFFICULTIES option, COVARIATES option*/
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* Version 8.2 (2009-07-15) : Jean-Benoit Hardouin /*Correction of a bug with CML*/
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*
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* Needed modules :
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* gammasym version 2.2 (http://www.freeirt.org)
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* gausshermite version 1 (http://www.freeirt.org)
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* geekel2d version 4.3 (http://www.freeirt.org)
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* genscore version 1.4 (http://www.freeirt.org)
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* ghquadm (findit ghquadm)
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* gllamm version 2.3.14 (ssc describe gllamm)
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* gllapred version 2.3.7 (ssc describe gllapred)
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* elapse (ssc describe elapse)
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*
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* Jean-benoit Hardouin - Department of Biomathematics and Biostatistics - University of Nantes - France
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* EA 4275 "Biostatistics, Clinical Research and Subjective Measures in Health Sciences"
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* jean-benoit.hardouin@univ-nantes.fr
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*
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* News about this program : http://www.anaqol.org
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* FreeIRT Project : http://www.freeirt.org
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*
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* Copyright 2003-2009 Jean-Benoit Hardouin
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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************************************************************************************************************/
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/***********************************************************************************************************
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DEFINITION / SYNTAX
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***********************************************************************************************************/
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program define raschtestv7jf,rclass
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syntax varlist(min=1 numeric) [if] [in] , ID(varname) [ MEANdiff DIRsave(string) FILESsave nodraw PAUse REPlace ICC INFormation SPLITtests FITgraph Method(string) group(numlist >0 ascen) AUTOGroup Test(string) q2 GENLT(string) GENSCOre(string) GENFIT(string) GRAph v8 COMp(varname) dif(varlist) time TRace Details nold iterate(int 200) DIFFiculties(string) COVariates(string) ]
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local covariables `covariates'
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if "`if'"=="" {
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local if "if 1"
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}
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/***********************************************************************************************************
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INTRODUCTION
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***********************************************************************************************************/
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if "`trace'"!="" {
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di in green "*** Tests of conformity"
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}
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if "`v8'"=="" {
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version 7.0
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}
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else {
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version 8.0
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}
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local st = "$S_TIME"
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local nbitems : word count `varlist'
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marksample touse ,novarlist
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if "`covariables'"!="" {
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tokenize "`covariables'",parse(" ,")
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local i=1
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local tcovariables
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while "``i''"!=","&"``i''"!="" {
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local covariable`i' ``i''
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local tcovariables `tcovariables' ``i''
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local ++i
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}
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local nbcovariables=`i'-1
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local ss1
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local ss3
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if "``i''"=="," {
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forvalues j=`=`i'+1'/`=`i'+2' {
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if "``j''"=="ss1" {
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local ss1 ss1
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}
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else if "``j''"=="ss3" {
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local ss3 ss3
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}
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else if "``j''"=="" {
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* di in green "option `j' vide"
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}
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else {
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di in red "Invalid option in the {hi:covariables} option"
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error 198
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}
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}
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local i=`i'+3
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if "``i''"!="" {
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di in red "There is too much options in the {hi:covariates} option."
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error 198
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}
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}
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local covariables `tcovariables'
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}
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else {
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local nbcovariables=0
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}
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isid `id'
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tokenize `varlist'
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local bad
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forvalues i=1/`nbitems' {
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qui count if ``i''!=0&``i''!=1&``i''!=.
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local N=r(N)
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if `N'>0 {
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local bad `bad' ``i''
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}
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}
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if "`bad'"!="" {
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di in red "The item(s) {hi:`bad'} is(are) not binary item(s) (with responses 0 or 1)."
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exit
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}
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if "`method'"=="" {
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local method cml
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}
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if "`test'"=="" {
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local test R
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}
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local method=lower("`method'")
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local test=upper("`test'")
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if "`method'"!="mml"&"`method'"!="cml"&"`method'"!="gee" {
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di in red "Uncorrect method option."
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error 198
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exit
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}
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if "`test'"!="R"&"`test'"!="Q"&"`test'"!="WP"&"`test'"!="NONE" {
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di in red "Uncorrect test option."
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error 198
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exit
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}
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if "`genfit'"!="" {
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local nbwordgenfit:word count `genfit'
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if `nbwordgenfit'!=2 {
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di in red "Uncorrect genfit option."
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di in red "This option must contain exactly two words"
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error 198
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exit
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}
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}
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if "`genfit'"!=""|"`genlt'"!=""|"`genscore'"!="" {
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capture confirm new variable `genfit' `genscore'
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if "`genfit'"!="" {
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local o:word 1 of `genfit'
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forvalues i=1/`nbitems' {
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confirm new variable `o'``i''
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}
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}
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if "`genlt'"!="" {
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local 0 `genlt'
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gettoken left 0: 0,parse(",")
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gettoken right 0: 0,parse(",")
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local genlt `left'
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local replacegenlt `0'
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local length=length("`replacegenlt'")
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if `length'>=3&substr("`replacegenlt'",1,`length')==substr("replace",1,`length') {
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local replacegenlt replace
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}
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if "`replacegenlt'"!="replace"&"`replacegenlt'"!="" {
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di "Not allowed option in the {hi:genlt} option"
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error 198
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}
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else if "`replacegenlt'"=="" {
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confirm new variable `genlt'
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}
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}
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}
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preserve
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tempfile saveraschtest
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qui save `saveraschtest'
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qui keep if `touse'==1
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if "`autogroup'"!=""&"`group'"!="" {
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di in green "The autogroup and the group options cannot be defined in the same time"
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di in green "Only the group option is retained."
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local autogroup
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}
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if "`autogroup'"!="" {
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tempvar autoscore
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qui genscore `varlist',score(`autoscore')
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tempname matscore tmp
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matrix `matscore'=J(`=`nbitems'-1',3,0)
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forvalues i=1/`=`nbitems'-1' {
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matrix `matscore'[`i',1]=`i'
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matrix `matscore'[`i',2]=`i'
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qui count if `autoscore'==`i'
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matrix `matscore'[`i',3]=r(N)
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}
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local stop=0
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local j=0
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while `j'<=`=`nbitems'-3'&`stop'!=1 {
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local j=`j'+1
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local scoretogroup=99999999
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local rowtogroup1=0
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local rowtogroup2=0
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local stop=1
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forvalues i=1/`=`nbitems'-`j'' {
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if `matscore'[`i',1]>=0 & `matscore'[`i',3]<30 & `matscore'[`i',3]<`scoretogroup' {
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local scoretogroup=`matscore'[`i',3]
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local rowtogroup1=`i'
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local stop=0
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}
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}
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if `stop'!=1 {
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if `rowtogroup1'>1&`rowtogroup1'<`=`nbitems'-`j'' {
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if `matscore'[`=`rowtogroup1'-1',1]<`matscore'[`=`rowtogroup1'+1',1] {
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local rowtogroup2=`rowtogroup1'
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local rowtogroup1=`rowtogroup1'-1
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}
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else {
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local rowtogroup2=`rowtogroup1'+1
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}
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}
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else if `rowtogroup1'==1 {
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local rowtogroup2=2
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}
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else if `rowtogroup1'==`=`nbitems'-`j'' {
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local rowtogroup2=`nbitems'-`j'
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local rowtogroup1=`nbitems'-`j'-1
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}
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matrix `tmp'=`matscore'
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matrix `matscore'=J(`=`nbitems'-`j'',3,0)
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if `rowtogroup1'!=1 {
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matrix `matscore'[1,1]=`tmp'[1..`=`rowtogroup1'-1',1..3]
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}
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matrix `matscore'[`rowtogroup1',1]=`tmp'[`rowtogroup1',1]
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matrix `matscore'[`rowtogroup1',2]=`tmp'[`rowtogroup2',2]
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matrix `matscore'[`rowtogroup1',3]=`tmp'[`rowtogroup1',3]+`tmp'[`rowtogroup2',3]
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if `rowtogroup2'!=`=`nbitems'-`j'' {
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matrix `matscore'[`rowtogroup2',1]=`tmp'[`=`rowtogroup2'+1'..`=`nbitems'-`j'',1..3]
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}
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}
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}
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local nbrows=rowsof(`matscore')-1
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local thresholds
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forvalues i=1/`nbrows' {
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local tmp=`matscore'[`i',2]
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local thresholds `thresholds' `tmp'
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}
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local group `thresholds'
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}
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if "`group'"=="" {
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forvalues i=1/`=`nbitems'-1' {
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local group "`group' `i'"
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}
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}
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local nbgroups:word count `group'
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local groupmax:word `nbgroups' of `group'
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if `groupmax'>=`nbitems' {
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di in red "You cannot form a group with the higher possible score."
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di in red "The higher possible value of the group option is `=`nbitems'-1'."
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di in red "Please correct your group option."
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error 198
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exit
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}
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else {
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if `groupmax'!=`=`nbitems'-1' {
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local group "`group' `=`nbitems'-1'"
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local nbgroups=`nbgroups'+1
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}
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}
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local nbgroups=`nbgroups'+1
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if "`dirsave'"!=""&"`filessave'"=="" {
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di in ye "If you want to save yours graphs, use the filessave option"
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}
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if "`filessave'"!="" {
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if "`dirsave'"=="" {
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local dirsave "`c(pwd)'"
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}
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di in ye "The graphs files will be saved in `dirsave'"
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}
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if "`dirsave'"!="" {
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if "`filesave'"=="" {
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local filesave "filesave"
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}
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}
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if "`pause'"!=""&"`draw'"=="" {
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pause on
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}
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if "`difficulties'"!="" {
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capture confirm matrix `difficulties'
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if _rc!=0 {
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di in red "The vector `difficulties' defined in the {hi:difficulties} option does not exist."
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error 198
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exit
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}
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}
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if "`difficulties'"!=""&"`method'"!="mml" {
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di in red "The {hi:difficulties} option can be defined only for MML method."
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error 198
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exit
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}
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if "`covariables'"!=""&"`method'"!="mml" {
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di in red "The {hi:covariables} option can be defined only for MML method."
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error 198
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exit
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}
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if "`covariables'"!=""&"`comp'"!="" {
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di in red "The {hi:covariables} and {hi:comp} options can be defined jointly."
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error 198
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exit
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}
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/***********************************************************************************************************
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POSSIBLE TEST
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***********************************************************************************************************/
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if ("`icc'"!=""|"`fitgraph'"!=""|"`splittest'"!=""|"`details'"!="")&"`test'"=="NONE" {
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di in green "You cannot use the {hi:details}, {hi:icc}, {hi:fitgraph} or {hi:splittest} options if you use {hi:test(none)}."
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di in green "These options are ignored."
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local details
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local icc
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local fitgraph
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local splittest
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}
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if "`comp'"!=""&"`method'"=="cml" {
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di in green "You cannot compare two populations ({hi:comp} option) with the CML estimation method"
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di in green "This option is ignored."
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local comp
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}
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if "`method'"!="cml"&"`test'"=="WP" {
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di in green "The Wright-Panchapakesan test is not authorized with MML or GEE."
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di in green "The WP tests are replaced by Van den Wollenberg Q tests."
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local test Q
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}
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if "`method'"=="gee"&"`ld'"!="" {
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di in green "You cannot use the {hi:nold} option with the GEE method of estimation"
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di in green "This option is ignored."
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local ld
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}
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if "`test'"==""|"`test'"=="R" {
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local test "R"
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if "`method'"=="cml" {
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local namewp "R1c"
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local descwp "R1c test"
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}
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else {
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local namewp="R1m"
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local descwp "R1m test"
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}
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}
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if `nbitems'>999999|"`test'"=="WP" {
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local namewp " Y"
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local descwp "Wright-Panchapakesan Y test"
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local q2
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}
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else if "`test'"=="Q" {
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local namewp " Q1"
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local descwp "Van den Wollenberg Q1 test"
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}
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if "`method'"!="cml"&"`meandiff'"!="" {
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di in green "The {hi:meandiff} option is not available with MML or GEE."
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di in green "This option is ignored."
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local meandiff
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}
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if "`method'"!="cml"&"`splittests'"!="" {
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di in green "The {hi:splittests} option is not available with MML or GEE."
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di in green "This option is ignored."
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local splittests
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}
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if "`method'"!="cml"&"`dif'"!="" {
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di in green "The {hi:dif} option is not available with MML or GEE."
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di in green "This option is ignored."
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local dif
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}
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/***********************************************************************************************************
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SCORES AND GROUPS
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************************************************************************************************************/
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qui count if `touse'==1
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local N=r(N)
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qui keep `varlist' `comp' `covariables' `id' `touse'
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tempname rep item
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tempvar score realscore
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qui genscore `varlist',score(`score')
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qui count if `score'==.&`touse'==1
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local nbindmiss=r(N)
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if "`ld'"=="" {
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qui drop if `score'==.
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}
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forvalues i=1/`nbitems' {
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rename ``i'' `rep'`i'
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}
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local liminf0=0
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local limsup0=0
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local liminf`nbgroups'=`nbitems'
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local limsup`nbgroups'=`nbitems'
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local recode
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forvalues i=1/`=`nbgroups'-1' {
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if `i'!= 1{
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local liminf`i' : word `=`i'-1' of `group'
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}
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else {
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local liminf1=0
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}
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local liminf`i'=`liminf`i''+1
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local limsup`i':word `i' of `group'
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local recode "`recode' `liminf`i''/`limsup`i''=`i'"
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}
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qui gen `realscore'=`score'
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qui recode `score' `recode' `nbitems'=`nbgroups'
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local smallgroup=0
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forvalues i=0/`nbgroups' {
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qui count if `score'==`i'
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local effscore`i'=r(N)
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if `i'!=0&`i'!=`nbgroups'&`effscore`i''<30 {
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local smallgroup=1
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}
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}
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/***********************************************************************************************************
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ESTIMATION OF THE DIFFICULTY PARAMETERS
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************************************************************************************************************/
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if "`trace'"!="" {
|
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di in green "*** Estimation of the difficulty parameters"
|
|
}
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|
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if "`covariables'"!="" {
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forvalues i=1/`nbcovariables' {
|
|
qui su `covariable`i''
|
|
local mean`covariable`i''=r(mean)
|
|
qui replace `covariable`i''=`covariable`i''-`mean`covariable`i'''
|
|
}
|
|
}
|
|
|
|
tempname ll coef var beta Vbeta est
|
|
|
|
if "`method'"=="gee" {
|
|
qui geekel2d `rep'1-`rep'`nbitems',ll
|
|
if `r(error)'==1 {
|
|
di "The variance of the latent trait probably is too high to made the GEE a efficient method to estimate the parameters."
|
|
error 499
|
|
exit
|
|
}
|
|
scalar `ll'=r(ll)
|
|
local nbind=r(N)
|
|
matrix `coef'=r(b)
|
|
matrix `est'=`coef'
|
|
matrix `est'[1,`=`nbitems'+1']=sqrt(`est'[1,`=`nbitems'+1'])
|
|
matrix `var'=r(V)
|
|
matrix `beta'=`coef'[1,1..`nbitems']
|
|
matrix `Vbeta'=`var'[1..`nbitems',1..`nbitems']
|
|
local sig=sqrt(`coef'[1,`=`nbitems'+1'])
|
|
local sesig=sqrt(`var'[`=`nbitems'+1',`=`nbitems'+1'])/sqrt(`nbind')/(`sig'*2)
|
|
}
|
|
|
|
qui reshape long `rep', i(`id') j(`item')
|
|
|
|
tempvar diff tl
|
|
gen `diff'=0
|
|
|
|
forvalues i=1/`nbitems' {
|
|
qui gen `rep'`i'=`item'==`i'
|
|
qui replace `rep'`i'=-`rep'`i'
|
|
}
|
|
|
|
|
|
if "`method'"=="mml" {
|
|
if "`difficulties'"==""{
|
|
qui xtlogit `rep' `rep'1-`rep'`nbitems' `covariables', i(`id') nocons iterate(`iterate')
|
|
matrix `est'=e(b)
|
|
matrix `est'[1,`=`nbitems'+`nbcovariables'+1']=exp(`est'[1,`=`nbitems'+`nbcovariables'+1']/2)
|
|
}
|
|
else {
|
|
tempname offset b
|
|
qui gen `offset'=0
|
|
forvalues i=1/`nbitems' {
|
|
qui replace `offset'=-(`difficulties'[1,`i']) if `item'==`i'
|
|
}
|
|
qui gllamm `rep' `covariables' , offset(`offset') i(`id') nocons iterate(`iterate') link(logit) fam(bin)
|
|
matrix `b'=e(b)
|
|
matrix `est'=`difficulties',`b'
|
|
matrix `est'[1,`=`nbitems'+`nbcovariables'+1']=exp(`b'[1,`=`nbcovariables'+1']/2)
|
|
}
|
|
}
|
|
else if "`method'"=="cml" {
|
|
qui clogit `rep' `rep'1-`rep'`=`nbitems'-1' , group(`id')
|
|
}
|
|
|
|
|
|
if "`method'"!="gee" {
|
|
if "`difficulties'"=="" {
|
|
matrix `coef'=e(b)
|
|
matrix `var'=e(V)
|
|
}
|
|
else {
|
|
matrix `coef'=`difficulties'
|
|
matrix `var'=J(`=`nbitems'+`nbcovariables'+1',`=`nbitems'+`nbcovariables'+1',.)
|
|
matrix `var'[`=`nbitems'+1',`=`nbitems'+1']=e(V)
|
|
}
|
|
scalar `ll'=e(ll)
|
|
local nbind=e(N)/`nbitems'
|
|
|
|
|
|
if "`meandiff'"!="" {
|
|
matrix `var'=J(`nbitems',`nbitems',.)
|
|
matrix `coef'=J(1,`nbitems',.)
|
|
local param
|
|
local lin `rep'1
|
|
forvalues j=2/`=`nbitems'-1' {
|
|
local lin `lin'+`rep'`j'
|
|
}
|
|
local lin (`lin')/`nbitems'
|
|
|
|
forvalues j=1/`=`nbitems'-1' {
|
|
qui lincom `rep'`j'-`lin'
|
|
matrix `coef'[1,`j']=`r(estimate)'
|
|
matrix `var'[`j',`j']=`r(se)'^2
|
|
}
|
|
qui lincom -`lin'
|
|
matrix `coef'[1,`nbitems']=`r(estimate)'
|
|
matrix `var'[`nbitems',`nbitems']=`r(se)'^2
|
|
}
|
|
if "`method'"=="mml" {
|
|
local sig=e(sigma_u)
|
|
local sesig=sqrt(`var'[`=`nbitems'+`nbcovariables'+1',`=`nbitems'+`nbcovariables'+1'])*`sig'/2
|
|
tempname betacov Vbetacov
|
|
if "`difficulties'"=="" {
|
|
matrix `beta'=`coef'[1,1..`nbitems']
|
|
matrix `Vbeta'=`var'[1..`nbitems',1..`nbitems']
|
|
if "`covariables'"!="" {
|
|
matrix `betacov'=`coef'[1,`=`nbitems'+1'..`=`nbitems'+`nbcovariables'']
|
|
matrix `Vbetacov'=`var'[`=`nbitems'+1'..`=`nbitems'+`nbcovariables'',`=`nbitems'+1'..`=`nbitems'+`nbcovariables'']
|
|
}
|
|
local sig=e(sigma_u)
|
|
local sesig=sqrt(`var'[`=`nbitems'+`nbcovariables'+1',`=`nbitems'+`nbcovariables'+1'])*`sig'/2
|
|
}
|
|
else {
|
|
tempname tmp
|
|
matrix `tmp'=e(b)
|
|
matrix `beta'=`difficulties'
|
|
if "`covariables'"!="" {
|
|
matrix `betacov'=`tmp'[1,1..`nbcovariables']
|
|
}
|
|
local sig=`tmp'[1,`=`nbcovariables'+1']
|
|
matrix `tmp'=e(V)
|
|
local sesig=sqrt(`tmp'[`=`nbcovariables'+1',`=`nbcovariables'+1'])
|
|
matrix `Vbeta'=J(`nbitems',`nbitems',.)
|
|
if "`covariables'"!="" {
|
|
matrix `Vbetacov'=`tmp'[1..`nbcovariables',1..`nbcovariables']
|
|
}
|
|
}
|
|
}
|
|
else if "`method'"=="cml"&"`meandiff'"==""{
|
|
matrix `beta'=`coef'[1,1..`=`nbitems'-1']
|
|
matrix `Vbeta'=`var'[1..`=`nbitems'-1',1..`=`nbitems'-1']
|
|
}
|
|
else if "`method'"=="cml"&"`meandiff'"!=""{
|
|
matrix `beta'=`coef'
|
|
matrix `Vbeta'=`var'
|
|
}
|
|
}
|
|
|
|
if ("`method'"=="mml"|"`method'"=="gee") {
|
|
local colnames
|
|
forvalues i=1/`nbitems' {
|
|
local colnames "`colnames' `rep':`rep'`i'"
|
|
}
|
|
forvalues i=1/`nbcovariables'{
|
|
local tmp:word `i' of `covariables'
|
|
local colnames "`colnames' `rep':`tmp'"
|
|
}
|
|
local id2=substr("`id'",1,4)
|
|
local colnames "`colnames' `id2'1:_cons"
|
|
matrix colnames `est'=`colnames'
|
|
|
|
qui gllamm `rep' `rep'1-`rep'`nbitems' `covariables', i(`id') allc nocons family(binom) link(logit) trace from(`est') eval
|
|
tempname u
|
|
qui gllapred `u',u fsample
|
|
|
|
forvalues i=1/`nbcovariables' {
|
|
local tmp:word `i' of `covariables'
|
|
qui replace `u'm1=`u'm1+`betacov'[1,`i']*(`tmp')/*+`mean`covariable`i''')*/
|
|
}
|
|
tempname theta sdtheta
|
|
matrix `theta'=J(1,`=`nbitems'+1',0)
|
|
matrix `sdtheta'=J(`=`nbitems'+1',`=`nbitems'+1',0)
|
|
qui su `u'm1 if `rep'1==-1
|
|
local vartheta=r(Var)
|
|
qui gen `u's2=`u's1^2
|
|
qui su `u's2 if `rep'1==-1
|
|
local meanse2=r(mean)
|
|
local psi=1-`meanse2'/(`meanse2'+`vartheta')
|
|
forvalues s=0/`nbitems' {
|
|
qui su `u'm1 if `realscore'==`s'
|
|
local theta`s'=r(mean)
|
|
qui su `u's1 if `realscore'==`s'
|
|
local sdtheta`s'=r(mean)
|
|
matrix `theta'[1,`=`s'+1']=`theta`s''
|
|
matrix `sdtheta'[`=`s'+1',`=`s'+1']=`sdtheta`s''
|
|
}
|
|
if "`genlt'"!="" {
|
|
tempfile ltsave ltsavetmp
|
|
qui save `ltsavetmp', replace
|
|
qui keep if `rep'1==-1
|
|
qui keep `u'm1 `u's1 `id'
|
|
qui sort `id'
|
|
qui save `ltsave'
|
|
qui use `ltsavetmp'
|
|
}
|
|
}
|
|
|
|
forvalues i=1/`nbitems' {
|
|
if `i'==`nbitems'&"`method'"=="cml"&"`meandiff'"=="" {
|
|
local beta`i'=0
|
|
local sd`i' .
|
|
local fixed`i' "*"
|
|
}
|
|
else {
|
|
local beta`i'=`coef'[1,`i']
|
|
local sd`i'=sqrt(`var'[`i',`i'])
|
|
}
|
|
qui replace `diff'=-`beta`i'' if `item'==`i'
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
COMPARISON OF TWO POPULATIONS
|
|
***********************************************************************************************************/
|
|
|
|
if "`comp'"!=""&"`method'"!="cml" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Test of comparison of two populations"
|
|
}
|
|
qui inspect `comp'
|
|
local unique=r(N_unique)
|
|
if `unique'== 2 {
|
|
qui su `comp'
|
|
local mincomp=r(min)
|
|
local maxcomp=r(max)
|
|
tempname bmin bmax
|
|
qui xtlogit `rep' if `comp'==`mincomp',offset(`diff') i(`id')
|
|
matrix `bmin'=e(b)
|
|
local meanmin=`bmin'[1,1]
|
|
local varmin=e(sigma_u)^2
|
|
local llmin=e(ll)
|
|
local Nmin=e(N_g)
|
|
qui xtlogit `rep' if `comp'==`maxcomp',offset(`diff') i(`id')
|
|
matrix `bmax'=e(b)
|
|
local meanmax=`bmax'[1,1]
|
|
local varmax=e(sigma_u)^2
|
|
local llmax=e(ll)
|
|
local Nmax=e(N_g)
|
|
local Zcomp=(`meanmin'-`meanmax')/sqrt(`varmin'/`Nmin'+`varmax'/`Nmax')
|
|
local pvalue=1-norm(abs(`Zcomp'))
|
|
}
|
|
else {
|
|
di "It is impossible to compare more than two populations"
|
|
di "The comparison process is not run"
|
|
local comp
|
|
}
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
ESTIMATION OF THE ABILITY PARAMETERS / CML
|
|
************************************************************************************************************/
|
|
if `nbitems'>=2 {
|
|
if "`trace'"!="" {
|
|
di in green "*** Estimation of the ability parameters"
|
|
}
|
|
if "`method'"=="cml" {
|
|
tempfile verytmp verytmp2
|
|
qui save `verytmp',replace
|
|
clear
|
|
qui use `saveraschtest'
|
|
|
|
tempvar itembis score2 a aa b c z
|
|
forvalues i=1/`nbitems'{
|
|
gen `itembis'`i'=``i''
|
|
replace `itembis'`i'=0 if ``i''==.
|
|
}
|
|
genscore `itembis'*, score(`score2')
|
|
drop `itembis'*
|
|
|
|
local exp "string(`1'!=.)"
|
|
noi di
|
|
local exp2 "`1'==. "
|
|
forvalues i=2/`nbitems'{
|
|
local exp "`exp'+string(``i''!=.)"
|
|
local exp2 "`exp2' & ``i''==."
|
|
}
|
|
gen `z'=0
|
|
replace `z'=1 if `exp2'
|
|
* save "~/Bureau/baseRasch.dta", replace
|
|
set trace on
|
|
set tracedepth 1
|
|
su
|
|
gen `aa'=`exp' if `z'==0
|
|
|
|
encode `aa',gen(`b')
|
|
bysort `b': gen `c'=_n if `z'==0
|
|
su `b'
|
|
local nbpos=r(max)
|
|
|
|
forvalues i=1/`nbpos'{
|
|
su `score2' if `b'==`i'
|
|
local scorem`i'=r(max)
|
|
local expp`i' ""
|
|
local expi`i' ""
|
|
local expj`i' ""
|
|
forvalues j=1/`nbitems'{
|
|
su ``j'' if `b'==`i'
|
|
if r(N)!=0{
|
|
local expp`i' "`expp`i'' p`j'"
|
|
local expi`i' "`expi`i'' i`j'"
|
|
local expj`i' "`expj`i'' j`j'"
|
|
}
|
|
}
|
|
}
|
|
drop `aa' `c'
|
|
qui save `verytmp2',replace
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
drop _all
|
|
qui set obs 20001
|
|
qui gen theta=(_n-10001)/1000
|
|
qui gen A=1
|
|
forvalues j=1/`nbitems' {
|
|
qui gen u`j'=exp(theta-`beta`j'')
|
|
qui gen p`j'=u`j'/(1+u`j')
|
|
qui gen a`j'=1/u`j'
|
|
qui replace A=A*a`j'
|
|
qui gen i`j'=u`j'/(1+u`j')^2
|
|
qui gen j`j'=(u`j'*(1-u`j'))/(1+u`j')^3
|
|
}
|
|
|
|
|
|
|
|
forvalues i=1/`nbpos'{
|
|
egen P`i'=rsum(`expp`i'')
|
|
egen I`i'=rsum(`expi`i'')
|
|
egen J`i'=rsum(`expj`i'')
|
|
tempname theta sdtheta
|
|
matrix `theta'`i'=J(1,`=`scorem`i''+1',0)
|
|
matrix `sdtheta'`i'=J(`=`scorem`i''+1',`=`scorem`i''+1',0)
|
|
forvalues s=0/`scorem`i'' {
|
|
capture drop f`s'
|
|
gen f`s'=abs(`s'-P`i'+.5*J`i'/I`i'^2)
|
|
sort f`s'
|
|
matrix `theta'`i'[1,`=`s'+1']=theta[1]
|
|
matrix `sdtheta'`i'[`=`s'+1',`=`s'+1']=sqrt(1/I`i'[1])
|
|
}
|
|
drop P`i' I`i' J`i'
|
|
}
|
|
matrix `theta'=`theta'`nbpos'
|
|
matrix `sdtheta'=`sdtheta'`nbpos'
|
|
|
|
use "`verytmp2'",replace
|
|
|
|
tempvar gentl3 sdgentl3
|
|
gen `gentl3'=.
|
|
gen `sdgentl3'=.
|
|
forvalues i=1/`nbpos'{
|
|
forvalues j=0/`scorem`i''{
|
|
replace `gentl3'=`theta'`i'[1,`=`j'+1'] if `score2'==`j'& `b'==`i'
|
|
replace `sdgentl3'=`sdtheta'`i'[1,`=`j'+1'] if `score2'==`j'& `b'==`i'
|
|
}
|
|
}
|
|
|
|
keep `id' `gentl3' `sdgentl3'
|
|
qui save `verytmp2',replace
|
|
|
|
|
|
|
|
|
|
|
|
use "`verytmp'",replace
|
|
}
|
|
qui gen `tl'=0
|
|
forvalues s=0/`nbitems' {
|
|
local theta`s'=`theta'[1,`=`s'+1']
|
|
qui replace `tl'=`theta`s'' if `realscore'==`s'
|
|
local sdtheta`s'=`sdtheta'[`=`s'+1',`= `s'+1']
|
|
}
|
|
tempname pred
|
|
qui gen `pred'=log(exp(`rep'*(`tl'+`diff'))/(1+exp(`tl'+`diff')))
|
|
|
|
qui su `pred'
|
|
local globalll=r(sum)
|
|
|
|
local nulscore=0
|
|
forvalues i=0/`nbgroups' {
|
|
qui count if `score'==`i'&`item'==1
|
|
local nbscore`i'=r(N)
|
|
if `nbscore`i''==0 {
|
|
local nulscore=1
|
|
}
|
|
}
|
|
if `nulscore' {
|
|
di in green "{p}At least one group of scores concerns none individuals. Tests will be not computed.{p_end}"
|
|
di in green "{p}Use the {hi:group} or the {hi:autogroup} options.{p_end}"
|
|
local test "NONE"
|
|
local details
|
|
local icc
|
|
local fitgraph
|
|
local splittest
|
|
}
|
|
forvalues i=0/`nbitems' {
|
|
qui count if `realscore'==`i'&`item'==1
|
|
local nbrealscore`i'=r(N)
|
|
}
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
TESTS OF THE FIRST ORDER
|
|
************************************************************************************************************/
|
|
|
|
tempname Pi
|
|
matrix define `Pi'=J(`nbitems',`=`nbitems'-1',0)
|
|
if "`test'"!="NONE" {
|
|
qui drop if `score'==.
|
|
if "`trace'"!="" {
|
|
di in green "*** Tests of the first order"
|
|
}
|
|
tempname Obs Obs2 Th Th2
|
|
|
|
matrix define `Obs'=J(`nbitems',`=`nbitems'-1',0)
|
|
matrix define `Obs2'=J(`nbitems',`=`nbgroups'-1',0)
|
|
matrix define `Th'=J(`nbitems',`=`nbitems'-1',0)
|
|
matrix define `Th2'=J(`nbitems',`=`nbgroups'-1',0)
|
|
local listofitemsc
|
|
|
|
/* Estimation of the gamma symetrical functions*/
|
|
local c0 "1"
|
|
local c`nbitems' "`nbitems'*x"
|
|
forvalues j=1/`nbitems' {
|
|
local listini`j'
|
|
local listofitemsc "`listofitemsc' `beta`j''"
|
|
local c0 `c0'*(1+exp(x-`beta`j''))
|
|
local c`nbitems' `c`nbitems''-`beta`j''
|
|
forvalues k=1/`nbitems' {
|
|
local listini`j'k`k'
|
|
if `k'!=`j' {
|
|
local listini`j' "`listini`j'' `beta`k''"
|
|
}
|
|
forvalues l=1/`nbitems' {
|
|
if `l'!=`j'&`l'!=`k' {
|
|
local listini`j'k`k' "`listini`j'k`k'' `beta`l''"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
gammasym `listofitemsc'
|
|
|
|
/*Estimation, for each value of the score s of the probability to respond to each item (Pi) and of the theorical number of positive responses (Ths) and of theorical number of positive respond per item pair (Ws)*/
|
|
forvalues s=1/`nbitems' {
|
|
local denom`s'=r(gamma`s')
|
|
tempname W`s'
|
|
matrix define `W`s''=J(`nbitems',`nbitems',0)
|
|
}
|
|
tempvar prob prob2 z y v z2 y2 v2 c r q e
|
|
qui gen `prob'=.
|
|
qui gen `prob2'=.
|
|
forvalues j=1/`nbitems' {
|
|
forvalues s=1/`=`nbitems'-1' {
|
|
qui count if `rep'==1&`item'==`j'&`realscore'==`s'
|
|
matrix `Obs'[`j',`s']=r(N)
|
|
if "`test'"!="WP" {
|
|
gammasym `listini`j''
|
|
local num`j'=r(gamma`=`s'-1')
|
|
if "`method'"=="cml"|"`test'"=="Q" {
|
|
matrix `Pi'[`j',`s']=exp(-`beta`j'')*`num`j''/`denom`s''
|
|
}
|
|
else {
|
|
gausshermite exp(`s'*x)/(`c0'), sigma(`sig')
|
|
local int`s'=r(int)
|
|
if "`test'"=="R"&`nbrealscore`s''!=0{
|
|
local tmp=exp(-`beta`j'')*`num`j''*`int`s''*`nbind'/`nbrealscore`s''
|
|
matrix `Pi'[`j',`s']=`tmp'
|
|
}
|
|
else if `nbrealscore`s''==0 {
|
|
matrix `Pi'[`j',`s']=0
|
|
}
|
|
}
|
|
if "`test'"=="R" {
|
|
forvalues k=`=`j'+1'/`nbitems' {
|
|
if `s'>=2 {
|
|
gammasym `listini`j'k`k''
|
|
local num`j'k`k'=r(gamma`=`s'-2')
|
|
if "`method'"=="cml" {
|
|
matrix `W`s''[`j',`k']=`nbrealscore`s''*exp(-`beta`j'')*exp(-`beta`k'')*`num`j'k`k''/`denom`s''
|
|
}
|
|
else {
|
|
matrix `W`s''[`j',`k']=exp(-`beta`j'')*exp(-`beta`k'')*`num`j'k`k''*`int`s''*`nbind'
|
|
}
|
|
matrix `W`s''[`k',`j']=`W`s''[`j',`k']
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if "`test'"=="WP" {
|
|
matrix `Pi'[`j',`s']=exp(`theta`s''-`beta`j'')/(1+exp(`theta`s''-`beta`j''))
|
|
}
|
|
matrix `Th'[`j',`s']=`Pi'[`j',`s']*`nbrealscore`s''
|
|
qui replace `prob'=`Pi'[`j',`s'] if `realscore'==`s'&`item'==`j'
|
|
qui replace `prob2'=exp(`theta`s''-`beta`j'')/(1+exp(`theta`s''-`beta`j'')) if `realscore'==`s'&`item'==`j'
|
|
matrix `W`s''[`j',`j']=`nbrealscore`s''*`Pi'[`j',`s']
|
|
if "`test'"!="R" {
|
|
matrix `W`s''[`j',`j']=`W`s''[`j',`j']*abs(1-`Pi'[`j',`s'])
|
|
}
|
|
}
|
|
}
|
|
qui gen `v2'=abs(`prob'*(1-`prob'))
|
|
qui gen `z2'=(`rep'-`prob')^2
|
|
qui gen `y2'=`z2'/`v2'
|
|
qui gen `c'=abs(`prob'*(1-`prob')*(`prob'^3+(1-`prob')^3))
|
|
qui gen `r'=`c'/(`v2')^2
|
|
qui gen `e'=`c'-(`v2')^2
|
|
|
|
forvalues j=1/`nbitems' {
|
|
qui su `y2' if `item'==`j'
|
|
local outfit`j'=r(mean)
|
|
qui su `r' if `item'==`j'
|
|
local Voutfit`j'=r(sum)
|
|
local Voutfit`j'=`Voutfit`j''/(`nbind')^2-1/`nbind'
|
|
local outfitstd`j'=(`outfit`j''^(1/3)-1)*(3/sqrt(`Voutfit`j''))-sqrt(`Voutfit`j'')/3
|
|
|
|
qui su `z2' if `item'==`j'
|
|
local n=r(sum)
|
|
qui su `v2' if `item'==`j'
|
|
local d=r(sum)
|
|
local infit`j'=`n'/`d'
|
|
qui su `e' if `item'==`j'
|
|
local sume=r(sum)
|
|
local Vinfit`j'=`sume'/(`d')^2
|
|
local infitstd`j'=(`infit`j''^(1/3)-1)*(3/sqrt(`Vinfit`j''))-sqrt(`Vinfit`j'')/3
|
|
}
|
|
tempname tmp stattest testitems
|
|
|
|
/*Estimation, by scores group g, of the theorical number of positive response (Th2g) and of the positive respond to each items pair (W2g)*/
|
|
scalar `stattest'=0
|
|
forvalues g=1/`=`nbgroups'-1' {
|
|
tempname W2`g' d2`g'
|
|
matrix define `W2`g''=J(`nbitems',`nbitems',0)
|
|
forvalues s=`liminf`g''/`limsup`g'' {
|
|
forvalues j=1/`nbitems' {
|
|
matrix `Obs2'[`j',`g']=`Obs2'[`j',`g']+`Obs'[`j',`s']
|
|
matrix `Th2'[`j',`g']=`Th2'[`j',`g']+`Th'[`j',`s']
|
|
if "`test'"=="R" {
|
|
forvalues k=`=`j'+1'/`nbitems' {
|
|
matrix `W2`g''[`j',`k']=`W2`g''[`j',`k']+`W`s''[`j',`k']
|
|
matrix `W2`g''[`k',`j']=`W2`g''[`j',`k']
|
|
}
|
|
}
|
|
matrix `W2`g''[`j',`j']=`W2`g''[`j',`j']+`W`s''[`j',`j']
|
|
}
|
|
}
|
|
|
|
/*Estimation of the d2g vectors*/
|
|
matrix `d2`g''=`Obs2'[1..`nbitems',`g']-`Th2'[1..`nbitems',`g']
|
|
|
|
/*Estimation of the influences on the test of each item (testitemsg matrix) and of the test statistic (stattest)*/
|
|
tempname test`g' testitems`g'
|
|
matrix `test`g''=`d2`g'''*syminv(`W2`g'')*`d2`g''
|
|
capture matrix `testitems`g''=cholesky(syminv(`W2`g''))*`d2`g''
|
|
if _rc!=0 {
|
|
matrix `tmp'=J(`nbitems',`nbitems',0)
|
|
forvalues j=1/`nbitems' {
|
|
matrix `tmp'[`j',`j']=`W2`g''[`j',`j']
|
|
}
|
|
di in green "Group `g' (`liminf`g''/`limsup`g''): The weight matrix is not positive-definite, the diagonal matrix is used to estimate the contribution of the items on the `wpname' statistic"
|
|
matrix list `tmp'
|
|
matrix `testitems`g''=cholesky(syminv(`tmp'))*`d2`g''
|
|
}
|
|
else {
|
|
matrix `testitems`g''=cholesky(syminv(`W2`g''))*`d2`g''
|
|
}
|
|
scalar `stattest'=`stattest'+`test`g''[1,1]
|
|
}
|
|
matrix `testitems'=J(`nbitems',1,0)
|
|
forvalues j=1/`nbitems' {
|
|
forvalues g=1/`=`nbgroups'-1' {
|
|
matrix `testitems'[`j',1]=`testitems'[`j',1]+`testitems`g''[`j',1]^2
|
|
}
|
|
}
|
|
|
|
/*Adaptation for the Q1 statistic*/
|
|
if "`test'"=="Q" {
|
|
scalar `stattest'=`stattest'*`=`nbitems'-1'/`nbitems'
|
|
}
|
|
|
|
/*Correction for R1m and Q1m*/
|
|
if ("`test'"=="R"|"`test'"=="Q")&("`method'"=="mml"|"`method'"=="gee") {
|
|
local c`nbitems' exp(`c`nbitems'')/(`c0')
|
|
local c0 1/(`c0')
|
|
gausshermite `c0', sigma(`sig')
|
|
local ci0=r(int)*`nbind'
|
|
gausshermite `c`nbitems'',sigma(`sig')
|
|
local ci`nbitems'=`nbind'*r(int)
|
|
scalar `stattest'=`stattest'+(`nbrealscore0'-`ci0')^2/`ci0'+(`nbrealscore`nbitems''-`ci`nbitems'')^2/`ci`nbitems''
|
|
}
|
|
|
|
|
|
|
|
/***********************************************************************************************************
|
|
TESTS U
|
|
************************************************************************************************************/
|
|
|
|
if "`method'"=="cml" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Tests U"
|
|
}
|
|
|
|
local quartile=`nbind'/4
|
|
local c1=0
|
|
local n1=0
|
|
while `n1'<`quartile' {
|
|
local c1=`c1'+1
|
|
local n1=`n1'+`nbrealscore`c1''
|
|
}
|
|
local c2=`nbitems'
|
|
local n2=0
|
|
while `n2'<`quartile' {
|
|
local c2=`c2'-1
|
|
local n2=`n2'+`nbrealscore`c2''
|
|
}
|
|
forvalues j=1/`nbitems' {
|
|
local zu1=0
|
|
local zu2=0
|
|
forvalues s=1/`c1' {
|
|
local zu1=`zu1'+`nbrealscore`s''*(`Obs'[`j',`s']/`nbrealscore`s''-`Pi'[`j',`s'])/sqrt(`nbrealscore`s''*`Pi'[`j',`s']*(1-`Pi'[`j',`s']))
|
|
}
|
|
forvalues s=`c2'/`=`nbitems'-1' {
|
|
local zu2=`zu2'+`nbrealscore`s''*(`Obs'[`j',`s']/`nbrealscore`s''-`Pi'[`j',`s'])/sqrt(`nbrealscore`s''*`Pi'[`j',`s']*(1-`Pi'[`j',`s']))
|
|
}
|
|
local U`j'=(`zu1'-`zu2')/sqrt(`c1'+`nbitems'-`c2')
|
|
}
|
|
}
|
|
|
|
|
|
|
|
/***********************************************************************************************************
|
|
TESTS OF THE SECOND ORDER /*undocumented in beta test*/
|
|
************************************************************************************************************/
|
|
|
|
if "`q2'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Tests of the second order"
|
|
}
|
|
|
|
tempfile Q2file
|
|
qui save "`Q2file'",replace
|
|
qui use "`saveraschtest'",replace
|
|
|
|
qui keep if `touse'==1
|
|
gen `score'=0
|
|
|
|
forvalues i=1/`nbitems' {
|
|
local Q2i`i'
|
|
rename ``i'' `rep'`i'
|
|
qui replace `score'=`score'+`rep'`i'
|
|
}
|
|
qui recode `score' `recode'
|
|
|
|
forvalues i=1/`nbitems' {
|
|
local Q2i`i'=0
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
|
local listinci`i'j`j'
|
|
forvalues k=1/`nbitems' {
|
|
if `k'!=`i'&`k'!=`j' {
|
|
local listinci`i'j`j' "`listinci`i'j`j'' `beta`k''"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
local Q2tot=0
|
|
forvalues k=2/`=`nbitems'-1' {
|
|
forvalues i=1/`=`nbitems'-1' {
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
|
if `k'==1 {
|
|
local num=0
|
|
}
|
|
else {
|
|
gammasym `listinci`i'j`j''
|
|
local num=r(gamma`=`k'-2')
|
|
}
|
|
local athi`i'j`j'k`k'=exp(-`beta`i'')*exp(-`beta`j'')*`num'/`denom`k''
|
|
local bthi`i'j`j'k`k'=`nbth`i's`k''-`athi`i'j`j'k`k''
|
|
local cthi`i'j`j'k`k'=`nbth`j's`k''-`athi`i'j`j'k`k''
|
|
local dthi`i'j`j'k`k'=1-`athi`i'j`j'k`k''-`bthi`i'j`j'k`k''-`cthi`i'j`j'k`k''
|
|
}
|
|
}
|
|
}
|
|
forvalues k=1/`=`nbgroups'-1' {
|
|
local Q2`k'=0
|
|
forvalues i=1/`=`nbitems'-1' {
|
|
forvalues j=`=`j'+1'/`nbitems' {
|
|
qui count if `rep'`i'==1&`rep'`j'==1&`score'==`k'
|
|
local aempi`i'j`j'k`k'=r(N)
|
|
local ath2i`i'j`j'k`k'=0
|
|
local bth2i`i'j`j'k`k'=0
|
|
local cth2i`i'j`j'k`k'=0
|
|
local dth2i`i'j`j'k`k'=0
|
|
}
|
|
}
|
|
if `limsup`k''!=1 {
|
|
forvalues l=`liminf`k''/`limsup`k'' {
|
|
forvalues i=1/`=`nbitems'-1' {
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
|
if `l'!=1 {
|
|
local ath2i`i'j`j'k`k'=`ath2i`i'j`j'k`k''+`athi`i'j`j'k`l''*`nbrealscore`l''
|
|
local bth2i`i'j`j'k`k'=`bth2i`i'j`j'k`k''+`bthi`i'j`j'k`l''*`nbrealscore`l''
|
|
local cth2i`i'j`j'k`k'=`cth2i`i'j`j'k`k''+`cthi`i'j`j'k`l''*`nbrealscore`l''
|
|
local dth2i`i'j`j'k`k'=`dth2i`i'j`j'k`k''+`dthi`i'j`j'k`l''*`nbrealscore`l''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
forvalues i=1/`=`nbitems'-1' {
|
|
forvalues j=`=`i'+1'/`nbitems' {
|
|
local d2i`i'j`j'k`k'=(`aempi`i'j`j'k`k''-`ath2i`i'j`j'k`k'')^2
|
|
local Q2i`i'j`j'k`k'=`d2i`i'j`j'k`k''/`ath2i`i'j`j'k`k''+`d2i`i'j`j'k`k''/`bth2i`i'j`j'k`k''+`d2i`i'j`j'k`k''/`cth2i`i'j`j'k`k''+`d2i`i'j`j'k`k''/`dth2i`i'j`j'k`k''
|
|
local Q2i`i'=`Q2i`i''+`Q2i`i'j`j'k`k''
|
|
local Q2i`j'=`Q2i`j''+`Q2i`i'j`j'k`k''
|
|
local Q2`k'=`Q2`k''+`Q2i`i'j`j'k`k''
|
|
}
|
|
}
|
|
}
|
|
local Q2tot=`Q2tot'+`Q2`k''
|
|
}
|
|
forvalues i=1/`nbitems' {
|
|
di in green "Item ``i'' : Q2 = `Q2i`i''"
|
|
}
|
|
local Q2=`Q2tot'*(`nbitems'-3)/(`nbitems'-1)
|
|
di in green "Q2 = `Q2tot'"
|
|
qui use "`Q2file'",replace
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
TEST LR Z
|
|
************************************************************************************************************/
|
|
|
|
if "`method'"=="cml" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Tests LR of Andersen"
|
|
}
|
|
local ssll=0
|
|
tempname Zfile
|
|
qui save "`Zfile'", replace
|
|
qui use "`saveraschtest'",replace
|
|
qui keep if `touse'==1
|
|
gen `score'=0
|
|
forvalues j=1/`nbitems' {
|
|
qui replace `score'=`score'+``j''
|
|
}
|
|
qui recode `score' `recode' `nbitems'=`nbgroups'
|
|
forvalues i=1/`=`nbgroups'-1' {
|
|
if `effscore`i''>0 {
|
|
qui raschtestv7jf `varlist' if `score'==`i', test(NONE) `ld' id(`id')
|
|
local ll`i'=r(cll)
|
|
local ssll=`ssll'+(`ll`i'')
|
|
}
|
|
}
|
|
local Z=2*`ssll'-2*`ll'
|
|
use "`Zfile'",replace
|
|
tempname AndersenZ
|
|
matrix define `AndersenZ'=(`Z',`=(`nbgroups'-2)*(`nbitems'-1)',1-chi2(`=(`nbitems'-1)*(`nbgroups'-2)',`Z'))
|
|
}
|
|
}
|
|
|
|
|
|
/***********************************************************************************************************
|
|
DISPLAYING RESULTS WITH TESTS
|
|
************************************************************************************************************/
|
|
|
|
if "`test'"!="NONE" {
|
|
local conttest= "_c"
|
|
}
|
|
di
|
|
tempname itemfit globalfit
|
|
matrix `globalfit'=J(1,3,0)
|
|
if "`method'"=="cml" {
|
|
di in green "Estimation method: " in yellow "Conditional maximum likelihood (CML)"
|
|
local nbtest=`nbgroups'-1
|
|
local line=77
|
|
}
|
|
else if "`method'"=="mml"{
|
|
di in green "Estimation method: " in yellow "Marginal maximum likelihood (MML)"
|
|
local nbtest=`nbgroups'+1
|
|
local line=70
|
|
}
|
|
else if "`method'"=="gee" {
|
|
di in green "Estimation method: " in yellow "Generalized Estimating Equations (GEE)"
|
|
local nbtest=`nbgroups'+1
|
|
local line=70
|
|
}
|
|
if "`test'"=="NONE" {
|
|
local line=35
|
|
}
|
|
|
|
di in green "Number of items: " in yellow `nbitems'
|
|
|
|
di in green "Number of groups: " in yellow `=`nbgroups'+1' `conttest'
|
|
if "`test'"!="NONE" {
|
|
di in green " (" in yellow "`nbtest'" in green " of them are used to compute the statistics of test)"
|
|
}
|
|
if "`method'"=="cml" {
|
|
local nbind=`nbind'+`effscore0'+`effscore`nbgroups''
|
|
local cont "_c"
|
|
matrix `itemfit'=J(`nbitems',6,0)
|
|
}
|
|
else {
|
|
local cont
|
|
matrix `itemfit'=J(`nbitems',5,0)
|
|
}
|
|
local missing=`N'-`nbind'
|
|
di in green "Number of individuals: " in yellow `N'
|
|
di in green "Number of individuals with missing values: " in yellow `nbindmiss' _c
|
|
if "`ld'"=="" {
|
|
di in green " (removed)"
|
|
}
|
|
else {
|
|
di
|
|
}
|
|
di in green "Number of individuals with nul or perfect score: " in yellow `=`effscore0'+`effscore`nbgroups'''
|
|
if "`method'"=="cml" {
|
|
di in green "Conditional log-likelihood: " in yellow %-13.4f `ll'
|
|
di in green "Log-likelihood: " in yellow %-13.4f `globalll'
|
|
}
|
|
else {
|
|
di in green "Marginal log-likelihood: " in yellow %-13.4f `ll'
|
|
di in green "Log-likelihood: " in yellow %-13.4f `globalll'
|
|
}
|
|
di
|
|
noi di in green _col(16) "Difficulty" `conttest'
|
|
if "`test'"!="NONE" {
|
|
di in green _col(58) "Standardized"
|
|
}
|
|
noi di in green _col(9) "Items" _col(16) "parameters" _col(28) "std Err." `conttest'
|
|
if "`test'"!="NONE" {
|
|
local varin=int(2/sqrt(`nbind')*100)/100
|
|
local varout=int(6/sqrt(`nbind')*100)/100
|
|
di _col(41) "`namewp'" _col(47) "df" _col(50) "p-value" _col(58) "Outfit" _col(66) "Infit" `cont'
|
|
if "`method'"=="cml" {
|
|
di in green _col(77) "U"
|
|
}
|
|
}
|
|
di in green "{hline `line'}"
|
|
forvalues i=1/`nbitems' {
|
|
noi di in yellow _col(3) %12s abbrev("``i''`fixed`i''" ,12) in yellow _col(18) %8.5f `beta`i'' _col(29) %6.5f `sd`i'' `conttest'
|
|
if "`test'"!="NONE" {
|
|
di _col(36) %8.3f `testitems'[`i',1] _col(46) %3.0f `=`nbgroups'-2' _col(51) %6.4f 1-chi2(`=`nbgroups'-2',`testitems'[`i',1]) _col(58) %6.3f `outfitstd`i'' _col(65) %6.3f `infitstd`i'' /*_col(72) %6.3f `outfit`i'' _col(79) %6.3f `infit`i''*/ `cont'
|
|
matrix `itemfit'[`i',1]=`testitems'[`i',1]
|
|
matrix `itemfit'[`i',2]=`=`nbgroups'-2'
|
|
matrix `itemfit'[`i',3]=1-chi2(`=`nbgroups'-2',`testitems'[`i',1])
|
|
matrix `itemfit'[`i',4]=`outfitstd`i''
|
|
matrix `itemfit'[`i',5]=`infitstd`i''
|
|
if "`method'"=="cml" {
|
|
di in ye _col(72) %6.3f `U`i''
|
|
matrix `itemfit'[`i',6]=`U`i''
|
|
}
|
|
}
|
|
}
|
|
di in green "{hline `line'}"
|
|
if "`test'"!="NONE" {
|
|
if "`method'"=="cml" {
|
|
local df=(`nbgroups'-2)*(`nbitems'-1)
|
|
}
|
|
else {
|
|
local df=(`nbgroups'-1)*(`nbitems'-1)-1
|
|
}
|
|
matrix `globalfit'=(`stattest',`df',1-chi2(`df',`stattest'))
|
|
noi di in green _col(6) "`descwp'" _col(32) in gr "`namewp'=" _col(36) in ye %8.3f `globalfit'[1,1] _col(46) %3.0f `globalfit'[1,2] _col(51) %6.4f `globalfit'[1,3]
|
|
if "`method'"=="cml" {
|
|
noi di in green _col(6) "Andersen LR test" _col(34) "Z=" in yellow _col(36) %8.3f `AndersenZ'[1,1] _col(46) %3.0f `AndersenZ'[1,2] _col(51) %6.4f `AndersenZ'[1,3]
|
|
}
|
|
di in green "{hline `line'}"
|
|
}
|
|
|
|
if "`method'"=="cml"&"`meandiff'"==""{
|
|
di in green "*: The difficulty parameter of this item had been fixed to 0"
|
|
}
|
|
if "`method'"=="cml"&"`meandiff'"!=""{
|
|
di in green "The mean of the difficulty parameters is fixed to 0"
|
|
}
|
|
if `smallgroup'==1&"`test'"!="NONE" {
|
|
di in green "You have groups of scores with less than 30 individuals. The tests can be invalid."
|
|
}
|
|
if "`method'"!="cml"&"`test'"=="Q" {
|
|
di in green "The Q statistics is approximated in the `method' approach. It is preferable to use the R1m test."
|
|
}
|
|
|
|
|
|
|
|
di
|
|
if "`method'"!="cml" {
|
|
di in green "{hline 56}"
|
|
noi di in green _col(5) "Parameters" _col(21) "Coef." _col(28) "std Err." _col(43) "z" _col(52) "P>|z|"
|
|
di in green "{hline 56}"
|
|
local zsig=`sig'/`sesig'
|
|
local pzsig=2*(1-norm(abs(`zsig')))
|
|
di in green _col(10) in yellow "Sigma" _col(18) %8.5f `sig' _col(29) %6.5f `sesig' _col(38) %6.3f `zsig' _col(50) %7.4f `pzsig'
|
|
di in green "{hline 56}"
|
|
}
|
|
*set trace on
|
|
if "`covariables'"!="" {
|
|
tempname zcovariates pcovariates
|
|
matrix `zcovariates'=J(1,`nbcovariables',0)
|
|
matrix `pcovariates'=J(1,`nbcovariables',0)
|
|
forvalues i=1/`nbcovariables' {
|
|
local tmp :word `i' of `covariables'
|
|
local zcov=`betacov'[1,`i']/`Vbetacov'[`i',`i']^.5
|
|
local pzcov=2*(1-norm(abs(`zcov')))
|
|
matrix `zcovariates'[1,`i']=`zcov'
|
|
matrix `pcovariates'[1,`i']=`pzcov'
|
|
di in green _col(3) %12s in yellow abbrev("`tmp'",12) _col(18) %8.5f `betacov'[1,`i'] _col(29) %6.5f `Vbetacov'[`i',`i']^.5 _col(38) %6.3f `zcov' _col(50) %7.4f `pzcov'
|
|
}
|
|
di in green "{hline 56}"
|
|
local difficulties2
|
|
if "`difficulties'"!="" {
|
|
local difficulties2 diff(`difficulties')
|
|
}
|
|
if "`ss1'"!="" {
|
|
tempfile ss1save
|
|
qui save `ss1save'
|
|
qui use `saveraschtest'
|
|
qui keep if `touse'==1
|
|
|
|
qui raschtestv7jf `varlist' ,id(`id') meth(`method') test(none) `ld' `difficulties2'
|
|
local sigma0=r(sigma)
|
|
local var0=`sigma0'^2
|
|
local ss10=`var0'*`=`N'-1'
|
|
local ss1c0=0
|
|
di
|
|
di in green "Type 1 Sum of Squares (sequential)"
|
|
di "{hline 56}"
|
|
di in green _col(27) "Variance of the" _col(48) "Explained"
|
|
di in green _col(4) "Covariates" _col(25) "SS" _col(30) "latent trait" _col(49) "Variance"
|
|
di "{hline 56}"
|
|
di _col(10) in green "None" _col(15) in ye %12.3f `ss10' _col(34) %8.3f `var0'
|
|
di in green "{dup 56:-}"
|
|
local covariablesss1
|
|
forvalues i=1/`nbcovariables' {
|
|
local covariablesss1 `covariablesss1' `covariable`i''
|
|
qui raschtestv7jf `varlist' ,id(`id') meth(`method') covariates(`covariablesss1') test(none) `ld' `difficulties2'
|
|
local sigma`i'=r(sigma)
|
|
local var`i'=`sigma`i''^2
|
|
local ss1`i'=`var`i''*`=`N'-1'
|
|
di _col(2) %12s in yellow abbrev("`covariable`i''",12) _col(15) %12.3f in ye `=`ss1`=`i'-1''-`ss1`i''' _col(34) %8.3f `var`i'' _col(52) %5.3f (`=`ss1`=`i'-1''-`ss1`i''')/`ss10'
|
|
}
|
|
di in green "{hline 56}"
|
|
qui use `ss1save'
|
|
}
|
|
if "`ss3'"!="" {
|
|
tempfile ss3save
|
|
qui save `ss3save'
|
|
qui use `saveraschtest'
|
|
qui keep if `touse'==1
|
|
if "`ss1'"=="" {
|
|
qui raschtestv7jf `varlist' ,id(`id') meth(`method') test(none) `ld' `difficulties2'
|
|
local sigma0=r(sigma)
|
|
local var0=`sigma0'^2
|
|
local ss30=`var0'*`=`N'-1'
|
|
}
|
|
else {
|
|
local ss30=`ss10'
|
|
}
|
|
di
|
|
di in green "Type 3 Sum of Squares "
|
|
di "{hline 56}"
|
|
di in green _col(27) "Variance of the" _col(48) "Explained"
|
|
di in green _col(4) "Covariates" _col(25) "SS" _col(30) "latent trait" _col(49) "Variance"
|
|
di "{hline 56}"
|
|
di _col(10) in green "None" _col(15) in ye %12.3f `ss10' _col(34) %8.3f `var0'
|
|
di _col(6) in green "Complete" _col(15) in ye %12.3f `sig'^2*(`N'-1) _col(34) %8.3f `sig'^2
|
|
di in green "{dup 56:-}"
|
|
forvalues i=1/`nbcovariables' {
|
|
local covariablesss3
|
|
forvalues j=1/`nbcovariables' {
|
|
if `i'!=`j' {
|
|
local covariablesss3 `covariablesss3' `covariable`j''
|
|
}
|
|
}
|
|
qui raschtestv7jf `varlist' ,id(`id') meth(`method') covariates(`covariablesss3') test(none) `ld' `difficulties2'
|
|
local sigmas`i'=r(sigma)
|
|
local vars`i'=`sigmas`i''^2
|
|
local ss3s`i'=`vars`i''*`=`N'-1'
|
|
local vara`i'=`sig'^2
|
|
local ss3a`i'=`vara`i''*`=`N'-1'
|
|
di _col(2) %12s in yellow abbrev("`covariable`i''",12) _col(15) %12.3f in ye `=`ss3s`i''-`ss3a`i''' _col(34) %8.3f `vars`i'' _col(52) %5.3f (`=`ss3s`i''-`ss3a`i''')/`ss30'
|
|
}
|
|
di in green "{hline 56}"
|
|
qui use `ss3save'
|
|
}
|
|
}
|
|
|
|
|
|
|
|
/*Tabular of the estimated values of the latent trait*/
|
|
|
|
if "`covariables'"=="" {
|
|
di
|
|
di
|
|
noi di in green _col(33) "Ability" _col(60) "Expected"
|
|
noi di in green _col(17) "Group" _col(23) "Score" _col(30) "parameters" _col(42) "std Err." _col(54) "Freq." _col(63) "Score"_c
|
|
if "`method'"=="cml"&"`test'"!="NONE" {
|
|
noi di in green _col(75) "ll"
|
|
}
|
|
else {
|
|
noi di ""
|
|
}
|
|
if "`method'"=="cml"&"`test'"!="NONE" {
|
|
local line=62
|
|
}
|
|
else {
|
|
local line=51
|
|
}
|
|
di in green _col(17) "{hline `line'}"
|
|
local nonul=0
|
|
forvalues g=0/`nbgroups' {
|
|
if `g'!=0 {
|
|
di in green _col(17) "{dup `line':-}"
|
|
}
|
|
forvalues s=`liminf`g''/`limsup`g'' {
|
|
if `s'==`liminf`g'' {
|
|
local tmp `ll`g''
|
|
local gr `g'
|
|
}
|
|
else {
|
|
local tmp
|
|
local gr
|
|
}
|
|
local expscore`nonul'=0
|
|
forvalues i=1/`nbitems' {
|
|
local expscore`nonul'=`expscore`nonul''+1/(1+exp(-`theta`nonul''+`beta`i''))
|
|
}
|
|
if "`method'"=="cml" {
|
|
local format1 %8.3f
|
|
}
|
|
else {
|
|
local format1 %8.5f
|
|
}
|
|
noi di in yellow _col(17) %5s "`gr'"_col(23) %5s "`s'" _col(32) `format1' `theta`nonul'' _col(42) `format1' `sdtheta`nonul'' _col(55) %4.0f `nbrealscore`s'' _col(64) % 4.2f `expscore`nonul'' _col(68) %11.4f `tmp'
|
|
local nonul=`nonul'+1
|
|
}
|
|
}
|
|
di in green _col(17) "{hline `line'}"
|
|
}
|
|
if "`method'"=="mml"|"`method'"=="gee" {
|
|
di
|
|
*di in green "Variance of the estimated latent trait" _col(45) in ye %10.4f `vartheta'
|
|
di in green "Mean variance of the error" _col(45) in ye %10.4f `meanse2'
|
|
local sig2=`sig'^2
|
|
di in green "Estimated variance of the latent trait" _col(45) in ye %10.4f `sig2'
|
|
local psi2=1-`meanse2'/`sig2'
|
|
di in green "Personal Separation Index (PSI)" _col(45) in ye %10.4f `psi2'
|
|
di in green "Adjusted PSI on covariates (PSIadj)" _col(45) in ye %10.4f `psi'
|
|
}
|
|
|
|
|
|
/***********************************************************************************************************
|
|
DETAILS OPTION
|
|
************************************************************************************************************/
|
|
|
|
if "`details'"!="" {
|
|
forvalues g=0/`nbgroups' {
|
|
if (`g'!=0&`g'!=`nbgroups')|"`method'"!="cml" {
|
|
di
|
|
di in green "{hline 44}"
|
|
di in green "Group: " in ye "`g'" in green " from " in ye "`liminf`g''" in green " to " in ye "`limsup`g''" in green " (n=" in ye "`nbscore`g''" in green")"
|
|
di
|
|
di _col(3) "Item" _col(10) "Observed" _col(23) "Expected" _col(37) "Scaled"
|
|
di in green "{dup 44:-}"
|
|
}
|
|
if `g'!=0&`g'!=`nbgroups' {
|
|
forvalues j=1/`nbitems' {
|
|
local tmp=`d2`g''[`j',1]/sqrt(`W2`g''[`j',`j'])
|
|
di _col(3) in ye "``j''" _col(14) %4.0f `Obs2'[`j',`g'] _col(25) %6.2f `Th2'[`j',`g'] _col(36) %7.4f `tmp'
|
|
}
|
|
di in green "{dup 44:-}"
|
|
di in green "Contribution to the `namewp' statistics: " %8.4f in ye `test`g''[1,1]
|
|
}
|
|
else if "`method'"!="cml" {
|
|
if `g'==0 {
|
|
local h=0
|
|
}
|
|
else {
|
|
local h=`nbitems'
|
|
}
|
|
local tmp=abs(`nbrealscore`h''-`ci`h'')/sqrt(`ci`h'')
|
|
di in ye _col(14) %4.0f `nbrealscore`h'' _col(25) %6.2f `ci`h'' _col(36) %7.4f `tmp'
|
|
di in green "{dup 44:-}"
|
|
local tmp=`tmp'^2
|
|
di in green "Contribution to the `namewp' statistics: " %8.4f in ye `tmp'
|
|
}
|
|
}
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
OPTION ICC
|
|
************************************************************************************************************/
|
|
*set trace on
|
|
if "`icc'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Items Characteristic Curves"
|
|
}
|
|
tempvar proba propemp propth propthb
|
|
qui replace `tl'=. if `realscore'==0|`realscore'==`nbitems'|`realscore'==.
|
|
qui gen `propemp'=.
|
|
qui gen `propth'=.
|
|
qui gen `propthb'=.
|
|
label variable `propth' "Expected ICC"
|
|
label variable `propemp' "Observed ICC"
|
|
label variable `propthb' "Expected ICC"
|
|
label variable `tl' "Latent trait"
|
|
global iccs
|
|
forvalues i=1/`nbitems' {
|
|
forvalues s=1/`=`nbitems'-1' {
|
|
qui replace `propthb'=exp(`theta`s''-`beta`i'')/(1+exp(`theta`s''-`beta`i'')) if `item'==`i'&`realscore'==`s'
|
|
}
|
|
qui replace `propemp'=.
|
|
qui replace `propth'=.
|
|
tempvar propemp`i' propth`i'
|
|
qui bysort `realscore' `item' : egen `propth`i''=mean(`propthb') if `item'==`i'
|
|
qui bysort `realscore' `item' : egen `propemp`i''=mean(`rep') if `item'==`i'
|
|
qui replace `propth'=`propth`i'' if `item'==`i'
|
|
qui replace `propemp'=`propemp`i'' if `item'==`i'
|
|
local mintl=floor(`theta1')
|
|
local maxtl=floor(`theta`=`nbitems'-1'')+1
|
|
if "`filessave'"!=""{
|
|
local saving "`dirsave'\\icc``i''"
|
|
}
|
|
if "`v8'"!="" {
|
|
graph twoway (line `propemp' `propth' `tl') if `item'==`i' , name(icc``i'',replace) ytitle("") ylabel(0(.25)1) title("Observed and Expected ICC for the item ``i''") xlabel(`mintl'(1)`maxtl') `draw'
|
|
pause
|
|
if "`filessave'"!="" {
|
|
graph save icc``i'' "`saving'" , `replace'
|
|
}
|
|
}
|
|
else {
|
|
if "`filessave'"!="" {
|
|
local saving "saving(`saving'"
|
|
if "`replace'"=="" {
|
|
local saving "`saving')"
|
|
}
|
|
else {
|
|
local saving "`saving',replace)"
|
|
}
|
|
}
|
|
qui graph `propemp' `propth' `tl' if `item'==`i' , twoway `saving' c(ll) ylabel(0(.25)1) xlabel(`mintl'(1)`maxtl') title("Observed and Expected ICC for the item ``i''")
|
|
pause
|
|
}
|
|
}
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
OPTION INFORMATION
|
|
************************************************************************************************************/
|
|
|
|
if "`information'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Information graph"
|
|
}
|
|
|
|
tempfile saveinfo
|
|
qui save "`saveinfo'",replace
|
|
tempvar info latent
|
|
drop _all
|
|
qui set obs 2001
|
|
gen `latent'=((_n-1)/1001-1)*3
|
|
label variable `latent' "latent trait"
|
|
gen `info'=0
|
|
forvalues i=1/`nbitems' {
|
|
qui replace `info'=`info'+exp(`latent'-`beta`i'')/(1+exp(`latent'-`beta`i''))^2
|
|
}
|
|
local saving "`dirsave'\\information"
|
|
if "`v8'"!="" {
|
|
graph twoway (line `info' `latent') , name(information,replace) ytitle("Information") xlabel(-3(1)3) title("Information graph of the scale") `draw'
|
|
if "`filessave'"!="" {
|
|
graph save information "`saving'" , `replace'
|
|
}
|
|
pause
|
|
}
|
|
else {
|
|
if "`filessave'"!="" {
|
|
local saving "saving(`saving'"
|
|
if "`replace'"=="" {
|
|
local saving "`saving')"
|
|
}
|
|
else {
|
|
local saving "`saving',replace)"
|
|
}
|
|
}
|
|
if "`filessave'"=="" {
|
|
local saving
|
|
}
|
|
qui graph `info' `latent' , twoway `saving' c(l) xlabel(-3(1)3) title("Information graph of the scale")
|
|
pause
|
|
}
|
|
qui use "`saveinfo'",replace
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
OPTION FITGRAPH
|
|
************************************************************************************************************/
|
|
|
|
if "`fitgraph'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Graphical validation of the fit"
|
|
}
|
|
*set trace on
|
|
tempname outfit meanz2 meanv2 sumd infit Voutfit outfitstd Vinfit infitstd
|
|
qui egen `outfit'=mean(`y2'),by(`id')
|
|
qui egen `Voutfit'=sum(`r'),by(`id')
|
|
qui replace `Voutfit'=`Voutfit'/(`nbitems')^2-1/`nbitems'
|
|
qui gen `outfitstd'=(`outfit'^(1/3)-1)*(3/sqrt(`Voutfit'))-sqrt(`Voutfit')/3
|
|
qui egen `meanz2'=sum(`z2'),by(`id')
|
|
qui egen `meanv2'=sum(`v2'),by(`id')
|
|
qui gen `infit'=`meanz2'/`meanv2'
|
|
qui egen `Vinfit'=sum(`e'),by(`id')
|
|
qui replace `Vinfit'=`Vinfit'/(`meanv2')^2
|
|
qui gen `infitstd'=(`infit'^(1/3)-1)*(3/sqrt(`Vinfit'))-sqrt(`Vinfit')/3
|
|
|
|
qui su `outfitstd'
|
|
local mino=floor(2*min(`r(min)',-2))/2
|
|
local maxo=ceil(2*max(`r(max)',2))/2
|
|
qui su `infitstd'
|
|
local mini=floor(2*min(`r(min)',-2))/2
|
|
local maxi=ceil(2*max(`r(max)',2))/2
|
|
if "`filessave'"!=""{
|
|
local savingo "`dirsave'\\outfitind"
|
|
local savingi "`dirsave'\\infitind"
|
|
}
|
|
if "`v8'"!="" {
|
|
graph twoway (scatter `outfitstd' `id'), name(outfit,replace) yline(-2 2) title("Outfit indexes") xtitle("Individual indexes") ytitle("Outfit") ylabel(`mino'(.5)`maxo') `draw'
|
|
pause
|
|
graph twoway (scatter `infitstd' `id'), name(infit,replace) yline(-2 2) title("Infit indexes") xtitle("Individual indexes") ytitle("Infit") ylabel(`mini'(.5)`maxi') `draw'
|
|
pause
|
|
if "`filessave'"!="" {
|
|
graph save outfit "`savingo'" , `replace'
|
|
graph save infit "`savingi'" , `replace'
|
|
}
|
|
}
|
|
else {
|
|
if "`filessave'"!="" {
|
|
local savingo "saving(`savingo'"
|
|
local savingi "saving(`savingi'"
|
|
if "`replace'"=="" {
|
|
local savingo "`savingo')"
|
|
local savingi "`savingi')"
|
|
}
|
|
else {
|
|
local savingo "`savingo',replace)"
|
|
local savingi "`savingi',replace)"
|
|
}
|
|
}
|
|
graph `outfitstd' `id', `savingo' twoway sy(.) c(.) yline(-2 2) title("Outfit indexes") b2title("Individual indexes") l1("") l2title("Outfit") ylabel(`mino'(.5)`maxo')
|
|
pause
|
|
graph `infitstd' `id', `savingi' twoway sy(.) c(.) yline(-2 2) title("Infit indexes") b2title("Individual indexes") l1("") l2title("Infit") ylabel(`mini'(.5)`maxi')
|
|
pause
|
|
}
|
|
drop _all
|
|
qui set obs `nbitems'
|
|
tempvar name betap outfitstdj infitstdj
|
|
qui gen str9 `name'=""
|
|
qui gen `betap'=.
|
|
qui gen `outfitstdj'=.
|
|
qui gen `infitstdj'=.
|
|
local mino=-2
|
|
local maxo=2
|
|
local mini=-2
|
|
local maxi=2
|
|
forvalues j=1/`nbitems' {
|
|
qui replace `name'="``j''" in `j'
|
|
qui replace `betap'=`beta`j'' in `j'
|
|
qui replace `outfitstdj'=`outfitstd`j'' in `j'
|
|
qui replace `infitstdj'=`infitstd`j'' in `j'
|
|
local mino=floor(min(`mino',`outfitstd`j'')*2)/2
|
|
local mini=floor(min(`mini',`infitstd`j'')*2)/2
|
|
local maxo=ceil(max(`maxo',`outfitstd`j'')*2)/2
|
|
local maxi=ceil(max(`maxi',`infitstd`j'')*2)/2
|
|
}
|
|
if "`filessave'"!=""{
|
|
local savingo "`dirsave'\\outfititems"
|
|
local savingi "`dirsave'\\infititems"
|
|
}
|
|
if "`v8'"!="" {
|
|
graph twoway (scatter `outfitstdj' `betap',name(outfititem,replace) mlabel(`name')), yline(-2 2) title("Outfit indexes") xtitle("Difficulty") ytitle("Outfit") ylabel(`mino'(.5)`maxo') `draw'
|
|
pause
|
|
graph twoway (scatter `infitstdj' `betap',name(infititem,replace) mlabel(`name')), yline(-2 2) title("Infit indexes") xtitle("Difficulty") ytitle("Infit") ylabel(`mini'(.5)`maxi') `draw'
|
|
pause
|
|
if "`filessave'"!="" {
|
|
graph save outfititem "`savingo'" , `replace'
|
|
graph save infititem "`savingi'" , `replace'
|
|
}
|
|
}
|
|
else {
|
|
if "`filessave'"!="" {
|
|
local savingo "saving(`savingo'"
|
|
local savingi "saving(`savingi'"
|
|
if "`replace'"=="" {
|
|
local savingo "`savingo')"
|
|
local savingi "`savingi')"
|
|
}
|
|
else {
|
|
local savingo "`savingo',replace)"
|
|
local savingi "`savingi',replace)"
|
|
}
|
|
}
|
|
graph `outfitstdj' `betap', `savingo' twoway c(.) yline(-2 2) title("Outfit indexes") b2title("Difficulty") l1title("") l2title("Outfit") ylabel(`mino'(.5)`maxo') sy([`name'])
|
|
pause
|
|
graph `infitstdj' `betap', `savingi' twoway c(.) yline(-2 2) title("Infit indexes") b2title("Difficulty") l1title("") l2title("Infit") ylabel(`mini'(.5)`maxi') sy([`name'])
|
|
pause
|
|
}
|
|
}
|
|
|
|
|
|
/***********************************************************************************************************
|
|
OPTION SPLITTESTS
|
|
************************************************************************************************************/
|
|
|
|
if "`splittests'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Splitting tests"
|
|
}
|
|
forvalues j=1/`nbitems' {
|
|
tempname estneg`j' estpos`j'
|
|
local listitems
|
|
forvalues k=1/`nbitems' {
|
|
if `j'!=`k' {
|
|
local listitems `listitems' ``k''
|
|
}
|
|
}
|
|
qui use "`saveraschtest'",replace
|
|
qui keep if `touse'==1
|
|
qui raschtestv7jf `listitems' if ``j''==0,test(NONE) meth(cml) `ld' id(`id')
|
|
matrix `estneg`j''=r(beta)
|
|
local llneg=r(cll)
|
|
qui raschtestv7jf `listitems' if ``j''==1,test(NONE) meth(cml) `ld' id(`id')
|
|
matrix `estpos`j''=r(beta)
|
|
local llpos=r(cll)
|
|
qui raschtestv7jf `listitems',test(NONE) meth(cml) `ld' id(`id')
|
|
local llnegpos=r(cll)
|
|
local nbcol=colsof(`estneg`j'')
|
|
local meanneg=0
|
|
local meanpos=0
|
|
forvalues k=1/`nbcol' {
|
|
local meanneg=`meanneg'+`estneg`j''[1,`k']
|
|
local meanpos=`meanpos'+`estpos`j''[1,`k']
|
|
}
|
|
local meanneg=`meanneg'/`nbitems'
|
|
local meanpos=`meanpos'/`nbitems'
|
|
forvalues k=1/`nbcol' {
|
|
matrix `estneg`j''[1,`k']=`estneg`j''[1,`k']-`meanneg'
|
|
matrix `estpos`j''[1,`k']=`estpos`j''[1,`k']-`meanpos'
|
|
if "`method'"=="cml" {
|
|
matrix `estneg`j''=`estneg`j'',-`meanneg'
|
|
matrix `estpos`j''=`estpos`j'',-`meanpos'
|
|
}
|
|
}
|
|
drop _all
|
|
qui set obs `=`nbitems'+1'
|
|
tempvar neg pos name diag
|
|
qui gen `neg'=.
|
|
qui gen `pos'=.
|
|
qui gen str9 `name'=""
|
|
local min=`estneg`j''[1,1]
|
|
local max=`estneg`j''[1,1]
|
|
forvalues k=1/`=`nbitems'-1' {
|
|
qui replace `neg'=`estneg`j''[1,`k'] in `k'
|
|
qui replace `pos'=`estpos`j''[1,`k'] in `k'
|
|
local min=min(`min',`estneg`j''[1,`k'],`estpos`j''[1,`k'])
|
|
local max=max(`max',`estneg`j''[1,`k'],`estpos`j''[1,`k'])
|
|
local tmp:word `k' of `listitems'
|
|
qui replace `name'="`tmp'" in `k'
|
|
}
|
|
local min=floor(`min')
|
|
local max=floor(`max')+1
|
|
qui gen `diag'=.
|
|
qui replace `diag'=`min' in `nbitems'
|
|
qui replace `diag'=`max' in `=`nbitems'+1'
|
|
local Zgr=round(2*(`llneg'+`llpos'-`llnegpos'),0.001)
|
|
local Zgr=substr("`Zgr'",1,6)
|
|
local pgr=round(1-chi2(`=`nbitems'-1',`Zgr'),0.0001)
|
|
local pgr=substr("`pgr'",1,5)
|
|
local note="Z=`Zgr', df=`=`nbitems'-1' , p=`pgr'"
|
|
if "`filessave'"!=""{
|
|
local saving "`dirsave'\\split``j''"
|
|
}
|
|
if "`v8'"!="" {
|
|
graph twoway (scatter `pos' `neg' ,mlabel(`name')) (line `diag' `diag'), ytitle("Positive answer") xtitle("Negative answer") xlabel(`min'(1)`max') ylabel(`min'(1)`max') title(Splitting on ``j'') `draw' legend(off) note("Andersen Z test: `note'") name(split,replace) `draw'
|
|
pause
|
|
if "`filessave'"!="" {
|
|
graph save split "`saving'" , `replace'
|
|
}
|
|
}
|
|
else {
|
|
if "`filessave'"!="" {
|
|
local saving "saving(`saving'"
|
|
if "`replace'"=="" {
|
|
local saving "`saving')"
|
|
}
|
|
else {
|
|
local saving "`saving',replace)"
|
|
}
|
|
}
|
|
qui replace `diag'=`neg' if `diag'==.
|
|
graph `pos' `diag' `diag', `saving' c(.l) sy([`name']i) l1title("") l2title("Positive answer") b2title("Negative answer") xlabel(`min'(1)`max') ylabel(`min'(1)`max') title("Splitting on ``j'' (`note')") `draw'
|
|
pause
|
|
}
|
|
}
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
OPTION GRAPH
|
|
************************************************************************************************************/
|
|
|
|
if "`graph'"!=""&"`v8'"!="" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Graph option"
|
|
}
|
|
tempvar latent2 tl2 delta2 tlth2 labdelta2 labtl2
|
|
drop _all
|
|
qui set obs 1001
|
|
gen `latent2'=(_n-501)/100
|
|
label variable `latent2' "latent trait"
|
|
qui gen `tl2'=.
|
|
qui gen `labtl2'=""
|
|
forvalues i=0/`nbitems' {
|
|
*if (`i'!=0&`i'!=`nbitems')|"`method'"!="cml" {
|
|
qui replace `tl2'=`nbrealscore`i''/`N' if round(`latent2',0.01)==round(`theta`i'',0.01)
|
|
qui replace `labtl2'="`i'" if round(`latent2',0.01)==round(`theta`i'',0.01)
|
|
*}
|
|
}
|
|
qui gen `delta2'=.
|
|
qui gen `labdelta2'=""
|
|
forvalues i=1/`nbitems' {
|
|
qui replace `delta2'=-.05 if round(`latent2',0.01)==round(`beta`i'',0.01)
|
|
qui replace `labdelta2'="``i''" if round(`latent2',0.01)==round(`beta`i'',0.01)
|
|
}
|
|
if "`method'"=="mml"|"`method'"=="gee" {
|
|
qui gen `tlth2'=1/(2*_pi*`sig')*exp(-.5*(`latent2'/`sig')^2)
|
|
label variable `tlth2' "Theorical distribution"
|
|
local graphmml line `tlth2' `latent2'
|
|
}
|
|
label variable `tl2' "Score"
|
|
label variable `delta2' "Items"
|
|
local saving "`dirsave'\\graph"
|
|
local min=-2
|
|
local max=2
|
|
forvalues j=1/`nbitems' {
|
|
if `beta`j''<`min' {
|
|
local min=`beta`j''-.5
|
|
}
|
|
if `beta`j''>`max'&`beta`j''!=. {
|
|
local max=`beta`j''+.5
|
|
}
|
|
}
|
|
if "`method'"=="cml" {
|
|
if `theta0'<`min' {
|
|
local min=`theta0'-.5
|
|
}
|
|
if `theta`=`nbitems'-1''>`max' {
|
|
local max=`theta`=`nbitems'-1''+.5
|
|
}
|
|
}
|
|
else if "`method'"!="cml" {
|
|
if `theta0'<`min'&`theta0'!=. {
|
|
local min=`theta0'-.5
|
|
}
|
|
if `theta`nbitems''>`max'&`theta`nbitems''!=. {
|
|
local max=`theta`nbitems''+.5
|
|
}
|
|
}
|
|
local min=floor(`min')
|
|
local max=floor(`max')+1
|
|
qui su `tl2'
|
|
local max2=r(max)
|
|
if "`method'"!="cml" {
|
|
qui su `tlth2'
|
|
local max2=max(`max2',`r(max)')
|
|
}
|
|
qui replace `latent2'=. if `latent2'<`min'|`latent2'>`max'
|
|
if "`v8'"!="" {
|
|
graph twoway (bar `tl2' `latent2',/*mlabel(`labtl2')*/ barwidth(.3) ) (dropline `delta2' `latent2',mlabel(`labdelta2') mlabposition(6)) (`graphmml'), name(graph,replace) ytitle("") xlabel(`min'(1)`max') ylabel(-.1 0(0.05)`max2') title("Individuals/items representations") `draw'
|
|
if "`filessave'"!="" {
|
|
graph save graph "`saving'" , `replace'
|
|
}
|
|
pause
|
|
}
|
|
}
|
|
else if "`graph'"!=""&"`v8'"=="" {
|
|
di in ye "The graph option is not available with Stata 7"
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
COMPARISON OF TWO POPULATION
|
|
************************************************************************************************************/
|
|
|
|
if "`comp'"!=""&"`method'"!="cml" {
|
|
di
|
|
di _col(4) "{hline 30}"
|
|
di _col(4) in green "Comparison of two populations"
|
|
di _col(4) "{hline 30}"
|
|
di
|
|
di _col(4) in green "`comp'==`mincomp':" _col(20) "N=" _col(22) in yellow %7.0f `Nmin' _col(30) in green "mean= " _col(35) in yellow %8.4f `meanmin' _col(45) in green "variance=" _col(55) in yellow %8.4f `varmin'
|
|
di _col(4) in green "`comp'==`maxcomp':" _col(20) "N=" _col(22) in yellow %7.0f `Nmax' _col(30) in green "mean= " _col(35) in yellow %8.4f `meanmax' _col(45) in green "variance=" _col(55) in yellow %8.4f `varmax'
|
|
di _col(33) in green "Z= " _col(35) in yellow %8.4f `Zcomp' _col(47) in green "pvalue= " _col(57) in yellow %6.4f `pvalue'
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
TEST DIF
|
|
************************************************************************************************************/
|
|
|
|
if "`method'"=="cml"&"`dif'"!=""&"`test'"!="NONE" {
|
|
if "`trace'"!="" {
|
|
di in green "*** Tests of DIF"
|
|
}
|
|
local ssll=0
|
|
tempname DIFfile
|
|
qui save "`DIFfile'", replace
|
|
qui use "`saveraschtest'",replace
|
|
qui keep if `touse'==1
|
|
unab list:`dif'
|
|
local nbdif:word count `list'
|
|
tempname DIF
|
|
matrix define `DIF'=J(`nbdif',4,0)
|
|
local count=1
|
|
di
|
|
di _col(4) in green "{hline 45}"
|
|
di _col(4) in green "Test of Differential Item Functioning (DIF)"
|
|
di _col(4) in green "{hline 45}"
|
|
foreach j in `list' {
|
|
qui inspect `j'
|
|
local nbdif=r(N_unique)
|
|
qui su `j'
|
|
local maxdif=r(max)
|
|
if `nbdif'>10&`maxdif'<=10 {
|
|
di in ye "The {hi:dif} option is available with variables containing 10 or less modalities (coded from 0 or 1 to k<10)."
|
|
di in ye "The variable `j' (`nbdif' modalities) is omitted."
|
|
}
|
|
else {
|
|
local ssll=0
|
|
forvalues i=0/10 {
|
|
qui count if `j'==`i'
|
|
local effdif=r(N)
|
|
if `effdif'>0 {
|
|
qui raschtestv7jf `varlist' if `j'==`i', test(NONE) id(`id')
|
|
local ll`i'=r(cll)
|
|
local ssll=`ssll'+(`ll`i'')
|
|
}
|
|
}
|
|
local Z=2*`ssll'-2*`ll'
|
|
local ddl=(`nbdif'-1)*(`nbitems'-1)
|
|
matrix define `DIF'[`count',1]=(`count',`Z',`ddl',1-chi2(`ddl',`Z'))
|
|
di
|
|
di _col(4) in green "Variable:" in ye " `j' " in green " Number of groups: " in ye "`nbdif'"
|
|
di _col(4) in green "LR Test Z=" in ye %8.3f `Z' in gr " ddl=" in ye %4.0f `ddl' in gr " p-value=" in ye %6.4f `=1-chi2(`ddl',`Z')'
|
|
di
|
|
}
|
|
local ++count
|
|
}
|
|
use "`DIFfile'",replace
|
|
}
|
|
|
|
/***********************************************************************************************************
|
|
RETURN
|
|
************************************************************************************************************/
|
|
|
|
if "`trace'"!="" {
|
|
di in green "*** Returns"
|
|
}
|
|
local colnametheta
|
|
|
|
forvalues i=0/`nbgroups' {
|
|
local colnametheta `colnametheta' score_`i'
|
|
}
|
|
|
|
|
|
return clear
|
|
|
|
if `nbitems'>=3&"`test'"!="NONE" {
|
|
matrix colnames `globalfit'=`namewp' df p
|
|
matrix rownames `globalfit'=`method'
|
|
matrix rownames `itemfit'=`varlist'
|
|
matrix roweq `itemfit'=`method'
|
|
|
|
if "`method'"=="cml" {
|
|
matrix colnames `itemfit'=`namewp' df p outfit infit U
|
|
matrix rownames `AndersenZ'=`method'
|
|
matrix colnames `AndersenZ'=Z df p
|
|
return matrix AndersenZ=`AndersenZ'
|
|
if "`dif'"!="" {
|
|
matrix rownames `DIF'=`list'
|
|
matrix colnames `DIF'=num Z df p
|
|
return matrix DIF=`DIF'
|
|
}
|
|
}
|
|
else {
|
|
matrix colnames `itemfit'=`namewp' df p outfit infit
|
|
}
|
|
return matrix itemFit=`itemfit'
|
|
return matrix globalFit=`globalfit'
|
|
}
|
|
|
|
matrix colnames `theta'=`colnametheta'
|
|
matrix rownames `theta'=theta
|
|
return matrix theta `theta'
|
|
matrix colnames `sdtheta'=`colnametheta'
|
|
matrix rownames `sdtheta'=`colnametheta'
|
|
return matrix Vartheta `sdtheta'
|
|
local varlist2
|
|
matrix coleq `beta'=`method'
|
|
matrix coleq `Vbeta'=`method'
|
|
matrix roweq `Vbeta'=`method'
|
|
if "`method'"=="cml" {
|
|
forvalues i=1/`=`nbitems'-1' {
|
|
local varlist2 `varlist2' ``i''
|
|
}
|
|
return scalar cll=`ll'
|
|
return scalar ll=`globalll'
|
|
local AIC=-2*`globalll'+2*(`nbitems'-1)
|
|
}
|
|
else {
|
|
return scalar ll=`ll'
|
|
local varlist2 `varlist'
|
|
return scalar sigma=`sig'
|
|
return scalar sesigma=`sesig'
|
|
local AIC=-2*`ll'+2*(`nbitems'+1)
|
|
}
|
|
|
|
if "`comp'"!="" {
|
|
return scalar Zcomp=`Zcomp'
|
|
return scalar pZcomp=`pvalue'
|
|
}
|
|
|
|
matrix colnames `beta'=`varlist2'
|
|
matrix rownames `beta'=beta
|
|
return matrix beta `beta'
|
|
|
|
matrix colnames `Vbeta'=`varlist2'
|
|
matrix rownames `Vbeta'=`varlist2'
|
|
return matrix Varbeta `Vbeta'
|
|
|
|
return scalar AIC=`AIC'
|
|
return scalar N=`nbind'
|
|
|
|
if "`method'"=="mml" {
|
|
return scalar PSI=`psi2'
|
|
return scalar PSIadj=`psi'
|
|
}
|
|
if "`covariables'"!="" {
|
|
local tmp
|
|
local tmp2
|
|
forvalues i=1/`nbcovariables' {
|
|
local tmp `tmp' `covariable`i''
|
|
local tmp2 "`tmp2' :`covariable`i''"
|
|
}
|
|
matrix colnames `betacov'=`tmp2'
|
|
matrix rownames `Vbetacov'=`tmp2'
|
|
matrix colnames `Vbetacov'=`tmp2'
|
|
matrix colnames `zcovariates'=`tmp'
|
|
matrix colnames `pcovariates'=`tmp'
|
|
return matrix betacovariates=`betacov'
|
|
return matrix Vbetacovariates=`Vbetacov'
|
|
return matrix zcovariates=`zcovariates'
|
|
return matrix pcovariates=`pcovariates'
|
|
}
|
|
if "`pause'"!="" {
|
|
pause off
|
|
}
|
|
|
|
drop _all
|
|
|
|
restore,not
|
|
use "`saveraschtest'"
|
|
|
|
/***********************************************************************************************************
|
|
CREATE EVENTUAL NEW VARIABLES
|
|
************************************************************************************************************/
|
|
|
|
|
|
if "`genfit'"!=""|"`genlt'"!=""|"`genscore'"!="" {
|
|
tempname genlt2 genscore2 outfit2 infit2
|
|
qui gen `score'=0 `if'
|
|
forvalues i=1/`nbitems' {
|
|
qui replace `score'=`score'+``i'' `if'
|
|
}
|
|
if "`genscore'"!="" {
|
|
qui gen `genscore'=`score' `if'
|
|
}
|
|
if "`genlt'"!="" {
|
|
if "`replacegenlt'"=="replace" {
|
|
capture drop `genlt'
|
|
capture drop se`genlt'
|
|
}
|
|
if "`method'"=="mml" {
|
|
qui sort `id'
|
|
qui merge `id' using `ltsave'
|
|
qui rename `u'm1 `genlt'
|
|
qui rename `u's1 se`genlt'
|
|
qui drop _merge
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
else if "`method'"=="cml" {
|
|
|
|
qui sort `id'
|
|
qui merge `id' using `verytmp2'
|
|
qui rename `gentl3' `genlt'
|
|
qui rename `sdgentl3' se`genlt'
|
|
qui drop _merge
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
else if "`method'"!="mml" {
|
|
qui gen `genlt2'=. `if'
|
|
forvalues i=0/`nbitems' {
|
|
qui replace `genlt2'=`theta`i'' `if'&`score'==`i'
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}
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qui gen `genlt'=`genlt2' `if'
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}
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}
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if "`genfit'"!="" {
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local outfit:word 1 of `genfit'
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local infit:word 2 of `genfit'
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qui gen `outfit'=0 `if'
|
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qui gen `infit'=0 `if'
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|
tempname infit1 infit2
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qui gen `infit1'=0 `if'
|
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qui gen `infit2'=0 `if'
|
|
forvalues j=1/`nbitems' {
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|
tempname pi`j'
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|
qui gen `pi`j''=exp(`genlt2'-`beta`j'')/(1+exp(`genlt2'-`beta`j'')) `if'
|
|
qui replace `pi`j''=0 `if'
|
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forvalues s=1/`nbitems' {
|
|
qui replace `pi`j''=`Pi'[`j',`s'] `if'&`score'==`s'
|
|
}
|
|
qui gen `outfit'``j''=(``j''-`pi`j'')^2/(`pi`j''*(1-`pi`j'')) `if'
|
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qui replace `infit1'=`infit1'+(``j''-`pi`j'')^2 `if'
|
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qui replace `infit2'=`infit2'+(`pi`j''*(1-`pi`j'')) `if'
|
|
qui replace `outfit'=`outfit'+`outfit'``j''/`nbitems' `if'
|
|
}
|
|
qui replace `infit'=`infit1'/`infit2' `if'
|
|
}
|
|
}
|
|
|
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if "`trace'"!=""|"`time'"!="" {
|
|
capture qui elapse `st'
|
|
di in green "** Time : " in yellow "$S_elap " in green "seconds"
|
|
}
|
|
|
|
end
|