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3267 lines
114 KiB
Plaintext
3267 lines
114 KiB
Plaintext
*! version 2.2 february2019
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*! Myriam Blanchin - Priscilla Brisson
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*! construction
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************************************************************************************************************
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* ROSALI: RespOnse-Shift ALgorithm at Item-level
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* Response-shift detection based on Rasch models family
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*
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* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
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* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
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* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
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* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
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* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, keep name of variables, optimization */
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*
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* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
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* myriam.blanchin@univ-nantes.fr
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*
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* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
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* priscilla.brisson@univ-nantes.fr
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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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program define rosali22, rclass
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timer clear 1
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timer on 1
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syntax varlist(min=2 numeric) [if] [in] [,ID(string) MODA(string) GROUP(string) NODIF]
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version 15
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tempfile saverspcm
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qui save `saverspcm',replace
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local save1=_rc
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if "`if'"!=""|"`in'"!="" {
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qui keep `if' `in'
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}
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/**************************************************************************/
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set more off
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set matsize 5000
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local id "`id'"
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local gp "`group'"
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tokenize `varlist'
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local nbitems:word count `varlist'
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/* Vérif nb d'items pair */
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local mod=mod(`nbitems',2)
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if `mod'!=0 {
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di in red "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
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error 198
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exit
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}
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local nbitems=`nbitems'/2
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* Vérification du format wide
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if "`id'" == "" {
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di in red "You must enter an identifiant ({hi:id} option). Please correct this option."
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error 198
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exit
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}
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qui tab `id'
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local nbpat = r(r)
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local nbline = _N
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if `nbpat' != `nbline' {
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di in red "Data must be in wide format : one line for one patient. Please correct this."
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error 198
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exit
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}
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if "`group'"=="" & "`nodif'"!="" {
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di in red "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
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error 198
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exit
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}
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if "`moda'"!="" {
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local listmoda:word count `moda'
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if `listmoda'!=`nbitems' {
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di in red "You have indicated a number of categories ({hi:moda} option) different of the number of items. Please correct this option."
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if `listmoda' < `nbitems' {
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error 122
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exit
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}
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if `listmoda' > `nbitems' {
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error 123
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exit
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}
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}
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}
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local nbc: word count `group'
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if `nbc' >= 2 {
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di in red "Only one variable can be used for group option ({hi:group} option). Please correct this option."
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error 198
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exit
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}
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/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
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if "`group'"!="" {
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qui tab `group'
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local nbgrp = r(r)
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if `nbgrp' != 2 {
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di in red "The option group must be used with only 2 groups ({hi:group} option). Please correct this option."
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error 420
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exit
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}
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}
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/* recoder la variable de groupe en 0, 1*/
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if "`group'"!="" {
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qui tab `gp', matrow(rep)
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qui matrix list rep
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if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
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forvalues i=1/`=rowsof(rep)'{
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qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
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di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
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}
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}
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forvalues g = 0/1 {
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qui tab `gp' if `gp' == `g'
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local nbp_gp`g' = r(N)
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}
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}
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/*item rename*/
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/*
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Items au temps 1 : 1 à nbitems ``j''
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Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
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Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
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*/
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di
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di "WARNING : Automatic recoding, so that the first response category is 0. see {help rosali22:help rosali22}."
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di
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/*verif modalités répondues*/
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if "`gp'" == "" { // Si pas d'option groupe
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forvalues j = 1 / `nbitems' {
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local recoda_`j' = 0
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qui tab ``j'' // Récupération des infos moda du temps 1
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local nbm`j'_t1 = r(r)
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qui su ``j''
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local minm`j'_t1 = r(min)
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local maxm`j'_t1 = r(max)
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qui tab ``=`j'+`nbitems''' // Récupération des infos moda du temps 2
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local nbm`j'_t2 = r(r)
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qui su ``=`j'+`nbitems'''
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local minm`j'_t2 = r(min)
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local maxm`j'_t2 = r(max)
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local minm_`j' = min(`minm`j'_t1',`minm`j'_t2') // Info moda pour l'item j
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local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
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local nbm_`j' = `=`maxm_`j''-`minm_`j'''
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//Recodage des réponses en 0, 1, 2, etc...
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forvalues r = 0/`=`maxm_`j''-1' {
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qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
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qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
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}
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// Vérif. Que toutes les modas sont utilisées & concordance entre temps
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forvalues m = 0/`nbm_`j'' {
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qui count if ``j'' == `m'
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local nb_rn1 = r(N)
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qui count if ``=`j'+`nbitems''' == `m'
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local nb_rn2 = r(N)
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local nb_rn = min(`nb_rn1',`nb_rn2')
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if `nb_rn' == 0 { // Une moda n'est pas utilisée
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local recoda_`j' = 1
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if `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'+`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'+`k'' merged "
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local stop = 0
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}
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}
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}
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else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' { // La (ou les) moda max ne sont pas utilisée(s)
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local stop = 1
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forvalues k = 1/`m' {
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qui count if ``j'' == `=`m' - `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''=`=`m' - `k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else {
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local hour=real(substr("$S_TIME",1,2))
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local min=real(substr("$S_TIME",4,2))
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local sec=real(substr("$S_TIME",7,2))
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local jour=real(substr("$S_DATE",1,2))
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global seed=784556+`sec'*1000000+`min'*10000+`hour'*100+`jour'
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set seed $seed
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if runiform()>0.5{ // Tirage au sort pour regrouper
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local stop = 1
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forvalues k = 1/`m' {
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qui count if ``j'' == `=`m' - `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'-`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else {
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''=`=`m' + `k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'+`k'' merged"
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local stop = 0
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}
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else {
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if `stop' != 0 {
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qui replace ``j''= `nbm_`j'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item answers ``j'': `m' and `nbm_`j'' merged"
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local stop = 0
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}
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}
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}
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}
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}
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}
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}
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}
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}
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else { // Cas où l'option groupe est utilisée
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forvalues j = 1 / `nbitems' {
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local recoda_`j' = 0
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qui tab ``j'' if `gp' == 0 // Récupération des infos moda du temps 1pour chaque groupe
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local nbm`j'_t1_g0 = r(r)
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qui su ``j'' if `gp' == 0
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local minm`j'_t1_g0 = r(min)
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local maxm`j'_t1_g0 = r(max)
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qui tab ``j'' if `gp' == 1
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local nbm`j'_t1_g1 = r(r)
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qui su ``j'' if `gp' == 1
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local minm`j'_t1_g1 = r(min)
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local maxm`j'_t1_g1 = r(max)
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qui tab ``=`j'+`nbitems''' if `gp' == 0 // Récupération des infos moda du temps 2 pour chaque groupe
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local nbm`j'_t2_g0 = r(r)
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qui su ``=`j'+`nbitems''' if `gp' == 0
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local minm`j'_t2_g0 = r(min)
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local maxm`j'_t2_g0 = r(max)
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qui tab ``=`j'+`nbitems''' if `gp' == 1
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local nbm`j'_t2_g1 = r(r)
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qui su ``=`j'+`nbitems''' if `gp' == 1
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local minm`j'_t2_g1 = r(min)
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local maxm`j'_t2_g1 = r(max)
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local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1') // Info moda pour l'item j
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local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
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local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
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//Recodage des réponses en 0, 1, 2, etc...
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forvalues r = 0/`=`maxm_`j''-1' {
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qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
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qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
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}
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// Vérif. Que toutes les modas sont utilisées & concordance entre temps
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forvalues m = 0/`=`nbm_`j''-1' {
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qui count if ``j'' == `m' & `gp' == 0
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local nb_rn1_g0 = r(N)
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qui count if ``j'' == `m' & `gp' == 1
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local nb_rn1_g1 = r(N)
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qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
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local nb_rn2_g0 = r(N)
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qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
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local nb_rn2_g1 = r(N)
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local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
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if `nb_rn' == 0 { // Une moda n'est pas utilisée
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local recoda_`j' = 1
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if `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k'' & `gp' == 0
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local v`k'1_0 = r(N)
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qui count if ``j'' == `=`m' + `k'' & `gp' == 1
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local v`k'1_1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
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local v`k'2_0 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
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local v`k'2_1 = r(N)
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if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'+`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'+`k'' merged"
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local stop = 0
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}
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}
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}
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else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' { // La moda max n'est pas utilisée
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local stop = 1
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forvalues k = 1/`=`m'' {
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qui count if ``j'' == `=`m' - `k'' & `gp' == 0
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local v`k'1_0 = r(N)
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qui count if ``j'' == `=`m' - `k'' & `gp' == 1
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local v`k'1_1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
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local v`k'2_0 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
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local v`k'2_1 = r(N)
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if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
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qui replace ``j''= `=`m' - `k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else { // Moda central non utilisée
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local hour=real(substr("$S_TIME",1,2))
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local min=real(substr("$S_TIME",4,2))
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local sec=real(substr("$S_TIME",7,2))
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local jour=real(substr("$S_DATE",1,2))
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global seed=784556+`sec'*1000000+`min'*10000+`hour'*100+`jour'
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set seed $seed
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if runiform()>0.5{ // Tirage au sort pour regrouper
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local stop = 1
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forvalues k = 1/`m' {
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qui count if ``j'' == `=`m' - `k'' & `gp' == 0
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local v`k'1_0 = r(N)
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qui count if ``j'' == `=`m' - `k'' & `gp' == 1
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local v`k'1_1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
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local v`k'2_0 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
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local v`k'2_1 = r(N)
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if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'-`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: item ``j'': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else {
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k'' & `gp' == 0
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local v`k'1_0 = r(N)
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qui count if ``j'' == `=`m' + `k'' & `gp' == 1
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local v`k'1_1 = r(N)
|
|
qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
|
|
local v`k'2_0 = r(N)
|
|
qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
|
|
local v`k'2_1 = r(N)
|
|
if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
|
|
qui replace ``j''=`=`m' + `k'' if ``j''==`m'
|
|
qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
|
|
di "WARNING: item ``j'': answers `m' and `=`m'+`k'' merged"
|
|
local stop = 0
|
|
}
|
|
else {
|
|
if `stop' != 0 {
|
|
qui replace ``j''= `nbm_`j'' if ``j''==`m'
|
|
qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
|
|
di "WARNING: item ``j'': answers `m' and `nbm_`j'' merged"
|
|
local stop = 0
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
forvalues j =1/`nbitems' {
|
|
qui tab ``j'', matrow(rec) // Récupération des infos moda du temps 1
|
|
local nbm`j'_t1 = r(r)
|
|
|
|
qui tab ``=`j'+`nbitems''' // Récupération des infos moda du temps 2
|
|
local nbm`j'_t2 = r(r)
|
|
|
|
local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
|
|
//Recodage des réponses en 0, 1, 2, etc...
|
|
forvalues r = 0/`=`nbm_`j''-1' {
|
|
qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
|
|
qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
|
|
}
|
|
}
|
|
|
|
|
|
forvalues j = 1/`nbitems' {
|
|
if `recoda_`j'' == 1 {
|
|
if "`gp'" != "" {
|
|
di
|
|
di "``j'' & ``=`j'+`nbitems''' after automatic recoding :"
|
|
di
|
|
tab ``j'' `gp'
|
|
tab ``=`j'+`nbitems''' `gp'
|
|
}
|
|
else {
|
|
di
|
|
di "``j'' & ``=`j'+`nbitems''' after automatic recoding :"
|
|
di
|
|
tab ``j''
|
|
tab ``=`j'+`nbitems'''
|
|
|
|
}
|
|
di
|
|
di
|
|
}
|
|
}
|
|
|
|
|
|
/* Calcul de nbmoda & nbdif */
|
|
forvalues j = 1/`nbitems' {
|
|
qui tab ``j''
|
|
local nbmoda_`j' = r(r)
|
|
local nbdif_`j' = r(r) - 1
|
|
}
|
|
|
|
local maxdif = 0
|
|
local nbmoda_sum = 0
|
|
forvalues j = 1/`nbitems' {
|
|
if `maxdif' < `nbdif_`j'' {
|
|
local maxdif = `nbdif_`j''
|
|
}
|
|
local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
|
|
}
|
|
|
|
/* Au moins 2 mmoda par item */
|
|
forvalues j=1/`nbitems' {
|
|
if `nbmoda_`j'' == 1 {
|
|
di in red "``j'' have only one response category, each item need at least 2 response categories"
|
|
error 198
|
|
exit
|
|
}
|
|
}
|
|
|
|
/* Vérification moda théorique VS moda réelles -> CONSTAT */
|
|
if "`moda'" != "" {
|
|
forvalues j=1/`nbitems' {
|
|
local nbmoda`j':word `j' of `moda'
|
|
|
|
if `nbmoda`j'' != `nbmoda_`j'' { //nbmodaj = nb moda annoncé par l'uti. (théorique) VS nbmoda_j = nb moda utilisée (réel)
|
|
di " WARNING : `nbmoda`j'' response categories exist for item ``j'' but only `nbmoda_`j'' seem to be used"
|
|
local recoda_`j' = 1
|
|
}
|
|
}
|
|
}
|
|
|
|
local coln ""
|
|
forvalues j =1 /`nbitems' {
|
|
local coln "`coln' ``j''"
|
|
}
|
|
|
|
matrix nbmod = J(2,`nbitems',.)
|
|
|
|
matrix colnames nbmod = `coln'
|
|
matrix rownames nbmod = NbModa Recoding
|
|
|
|
forvalues j = 1/`nbitems' {
|
|
matrix nbmod[1,`j'] = `nbmoda_`j''
|
|
matrix nbmod[2,`j'] = `recoda_`j''
|
|
}
|
|
|
|
*Erreur si plus de 200 difficultés
|
|
local nb_test = 0
|
|
forvalues j=1/`nbitems' {
|
|
local nb_test = `nb_test'+`nbmoda_`j'' -1
|
|
}
|
|
|
|
if `nb_test' >= 200 {
|
|
di in red "The number of items difficulties must be less than 200 ({hi:moda} option option). Please correct this option."
|
|
error 198
|
|
exit
|
|
}
|
|
|
|
local nbitp = 0
|
|
|
|
forvalues j = 1/`nbitems' {
|
|
if `nbmoda_`j'' >= 2 {
|
|
local nbitp = `nbitp' + 1
|
|
}
|
|
}
|
|
|
|
|
|
/*********************************
|
|
* AFFICHAGE INITIAL
|
|
*********************************/
|
|
di _col(5) "{hline 78}"
|
|
di _col(5) in ye %~30s "Time 1" _col(30) %~30s "Time 2" _col(55) "Nb of Responses Cat."
|
|
di _col(5) "{hline 78}"
|
|
forvalues j=1/`nbitems' {
|
|
di in gr _col(5) %~30s abbrev("``j''",20) _col(30) %~30s abbrev("``=`j'+`nbitems'''",20) _col(75) `nbmoda_`j''
|
|
}
|
|
di _col(5) "{hline 78}"
|
|
if "`group'" != "" {
|
|
di _col(10) "Nb. of patients : " abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
|
|
di _col(5) "{hline 78}"
|
|
}
|
|
else {
|
|
di _col(20) "Nb. of patients : `nbpat'"
|
|
di _col(5) "{hline 78}"
|
|
}
|
|
di
|
|
if `nbitems' == 1 {
|
|
di in red "2 items at least is necessary to detect DIF and/or RC at item level."
|
|
error 198
|
|
exit
|
|
}
|
|
forvalues j = 1/`nbitems' {
|
|
if `nbmoda_`j'' == 2 {
|
|
di "WARNING: ``j'' have only 2 response categories, the type uniform or non-uniform can't be detected."
|
|
}
|
|
if `nbmoda_`j'' == 1 {
|
|
di in red "``j'' uses only `nbmoda_`j'' response category, each item need at least 2 response categories used."
|
|
error 198
|
|
exit
|
|
}
|
|
if `nbmoda_`j'' == 0 {
|
|
di in red "``j'' uses no response category, each item needs at least 2 response categories used."
|
|
error 198
|
|
exit
|
|
}
|
|
}
|
|
di
|
|
if "`group'" != "" {
|
|
di _col(2) in ye "For all models : - mean of latent trait of `gp' 0 at time 1 is constrained at 0"
|
|
di _col(19) "- Equality of variances between groups"
|
|
di
|
|
}
|
|
else {
|
|
di _col(2) in ye "For all models : mean of latent trait at time 1 is constrained at 0"
|
|
di
|
|
}
|
|
|
|
/*********************************
|
|
* DEFINITION DES CONTRAINTES
|
|
*********************************/
|
|
|
|
if "`group'"!="" { // Contraintes si option groupe
|
|
*EGALITE ENTRE GROUPES A T1 (1-200)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
|
|
}
|
|
}
|
|
|
|
*DIF UNIFORME A T1 (201-400)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=2/`nbdif_`j''{
|
|
constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
|
|
}
|
|
}
|
|
|
|
*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
|
|
}
|
|
}
|
|
|
|
*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
|
|
}
|
|
}
|
|
|
|
* RC COMMUNE (801-1000)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
|
|
}
|
|
}
|
|
|
|
* RC UNIFORME, groupe 0 (1001-1200)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=2/`nbdif_`j''{
|
|
constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
|
|
}
|
|
}
|
|
|
|
* RC UNIFORME, groupe 1 (1201-1400)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=2/`nbdif_`j''{
|
|
constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
|
|
}
|
|
}
|
|
|
|
*Sans interaction temps x groupe
|
|
constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
}
|
|
else { //Contraintes si pas d'option groupe
|
|
*EGALITE ENTRE T1 et T2 (401-600)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
|
|
}
|
|
}
|
|
*RC UNIFORME (1001-1200)
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=2/`nbdif_`j''{
|
|
constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
|
|
}
|
|
}
|
|
}
|
|
|
|
/*********************************
|
|
* MATRICE DES RESULTATS
|
|
*********************************/
|
|
matrix dif_rc=J(`nbitems',8,.)
|
|
matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
|
|
local rown ""
|
|
|
|
forvalues j =1 /`nbitems' {
|
|
local rown "`rown' ``j''"
|
|
}
|
|
matrix rownames dif_rc = `rown'
|
|
|
|
*Nb modalité max
|
|
local nbdif_max = 0
|
|
forvalues j=1/`nbitems' {
|
|
if `nbdif_max' < `nbdif_`j'' {
|
|
local nbdif_max = `nbdif_`j''
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//////// PARTIE 1 : DIF A T1 ? ////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
|
|
di _dup(59) "_ "
|
|
di
|
|
di _col(5) in ye "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT FIRST TIME OF MEASUREMENT"
|
|
|
|
*********************************
|
|
** MODEL B **
|
|
*********************************
|
|
|
|
local model ""
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
|
}
|
|
}
|
|
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v)
|
|
/* Stockage des estimations du modèle */
|
|
estimates store modeldifB
|
|
matrix val_mB = r(table)
|
|
matrix esti_B = e(b)
|
|
|
|
|
|
/* Calcul des difficultés d'item (delta_j) */
|
|
matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
|
}
|
|
}
|
|
local name_partOneL ""
|
|
|
|
forvalues j=1/`nbitems' {
|
|
local name_partOneL "`name_partOneL' ``j''"
|
|
}
|
|
|
|
matrix colnames delta_mB = `name_partOneC'
|
|
matrix rownames delta_mB = `name_partOneL'
|
|
matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC_se ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
|
}
|
|
}
|
|
|
|
matrix colnames delta_mB_se = `name_partOneC_se'
|
|
matrix rownames delta_mB_se = `name_partOneL'
|
|
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues g=0/1{
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mB=r(estimate)
|
|
local delta`j'_`p'g`g'mB_se=r(se)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mB = r(estimate)
|
|
local delta`j'_`p'g`g'mB_se = r(se)
|
|
}
|
|
matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
|
|
matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
|
|
}
|
|
}
|
|
}
|
|
|
|
matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
|
|
|
|
/*group effect*/
|
|
qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
|
|
local geffmB=r(estimate)
|
|
local segeffmB=r(se)
|
|
qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
|
|
local gcmBp=r(p)
|
|
local gcmBchi=r(chi2)
|
|
local gcmBdf=r(df)
|
|
|
|
|
|
*********************************
|
|
** MODEL A **
|
|
*********************************
|
|
|
|
local model ""
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
|
}
|
|
}
|
|
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip)
|
|
|
|
/* Stockage des estimations du modèle */
|
|
estimates store modeldifA
|
|
matrix val_mA = r(table)
|
|
matrix esti_A = e(b)
|
|
|
|
/* Calcul des difficultés d'item (delta_j) */
|
|
matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
|
}
|
|
}
|
|
local name_partOneL ""
|
|
|
|
forvalues j=1/`nbitems' {
|
|
local name_partOneL "`name_partOneL' ``j''"
|
|
}
|
|
matrix colnames delta_mA = `name_partOneC'
|
|
matrix rownames delta_mA = `name_partOneL'
|
|
matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC_se ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
|
}
|
|
}
|
|
|
|
matrix colnames delta_mA_se = `name_partOneC_se'
|
|
matrix rownames delta_mA_se = `name_partOneL'
|
|
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues g=0/1{
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mA=r(estimate)
|
|
local delta`j'_`p'g`g'mA_se=r(se)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mA = r(estimate)
|
|
local delta`j'_`p'g`g'mA_se = r(se)
|
|
}
|
|
matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
|
|
matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
|
|
}
|
|
}
|
|
}
|
|
//Variance et se mA
|
|
matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
|
|
|
|
|
|
*************************************************************
|
|
***********************AFFICHAGE*****************************
|
|
*************************************************************
|
|
|
|
//Affichage modèle A
|
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di
|
|
di in ye "PROCESSING STEP A"
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|
di
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/* Affichage des estimations des difficultés modèle A */
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di _col(5) in ye "{ul:MODEL A:} No group effect: mean of latent trait of group 1 = mean of latent trait of group 0,"
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di _col(7) in ye "all item difficulties are freely estimated in both groups"
|
|
di
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|
di _col(10) in ye "Item difficulties estimates (s.e.)"
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di _col(10) "{hline 70}"
|
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di _col(30) in ye abbrev("`gp'",20) "=0" _col(60) abbrev("`gp'",20) "=1"
|
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di _col(10) "{hline 70}"
|
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forvalues j=1/`nbitems' {
|
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di in ye _col(10) "``j''"
|
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forvalues p=1/`nbdif_`j'' {
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di in gr _col(10) "`p'" _col(30) %6.2f `delta`j'_`p'g0mA' %6.2f " (" %3.2f `delta`j'_`p'g0mA_se' ")" _col(60) %6.2f `delta`j'_`p'g1mA' " (" %3.2f `delta`j'_`p'g1mA_se' ")"
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}
|
|
}
|
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di _col(10) "{hline 70}"
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/* Affichage des estimations sur le trait latent du modèle A */
|
|
di
|
|
di _col(10) in ye "Latent trait distribution estimates"
|
|
di _col(10) "{hline 65}"
|
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di _col(45) in ye "Estimate" _col(60) "Standard error"
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|
di _col(10) "{hline 65}"
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di _col(10) in ye "Variance" in gr _col(45) %6.2f `=var_mA[1,1]' _col(62) %6.2f `=var_mA[2,1]'
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di _col(10) in ye "Group effect (mean gp 1)" in gr _col(44) %6.2f "0 (constrained)"
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di _col(10) "{hline 65}"
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|
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//*Affichage modèle B
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|
|
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di
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di in ye "PROCESSING STEP B"
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di
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/* Affichage des estimations des difficultés modèle B */
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|
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di _col(5) in ye "{ul:MODEL B:} Group effect estimated: mean of latent trait of group 1 free estimated,"
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di _col(7) in ye "Same item difficulties estimated between groups"
|
|
di
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di _col(10) in ye "Item difficulties estimates (s.e.)"
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di _col(10) "{hline 70}"
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di _col(30) in ye abbrev("`gp'",20) "=0" _col(60) abbrev("`gp'",20) "=1"
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di _col(10) "{hline 70}"
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|
|
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forvalues j=1/`nbitems' {
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di in ye _col(10) "``j''"
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forvalues p=1/`nbdif_`j'' {
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di in gr _col(10) "`p'" _col(30) %6.2f `delta`j'_`p'g0mB' " (" %3.2f `delta`j'_`p'g0mB_se' ")" _col(60) %6.2f `delta`j'_`p'g1mB' " (" %3.2f `delta`j'_`p'g1mB_se' ")"
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}
|
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}
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|
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di _col(10) "{hline 70}"
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/* Affichage des estimations sur le trait latent du modèle B */
|
|
di
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di _col(10) in ye "Latent trait distribution estimates"
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di _col(10) "{hline 80}"
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di _col(45) in ye "Estimate" _col(60) "Standard error" _col(77) "P-value"
|
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di _col(10) "{hline 80}"
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di _col(10) in ye "Variance" in gr _col(44) %6.2f `=var_mB[1,1]' _col(62) %6.2f `=var_mB[2,1]'
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di _col(10) in ye "Group effect (mean gp 1)" in gr _col(44) %6.2f `geffmB' _col(62) %6.2f `segeffmB' _col(77) %6.4f `gcmBp'
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di _col(10) "{hline 80}"
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di
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|
|
*****************************************************
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|
* Modèle A vs Modèle B *
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*****************************************************
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|
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qui lrtest modeldifA modeldifB
|
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local diftestp=r(p)
|
|
local diftestchi=r(chi2)
|
|
local diftestdf=r(df)
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|
|
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//affichage lrtest
|
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di _col(10) in ye "LIKELIHOOD-RATIO TEST MODEL A VS MODEL B : "
|
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di _col(10) "{hline 50}"
|
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di _col(10) in ye "Chi-square" _col(30) "DF" _col(50) "P-value"
|
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di _col(10) in gr %6.2f `diftestchi' _col(30) %2.0f `diftestdf' _col(50) %6.4f `diftestp'
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di _col(10) "{hline 50}"
|
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if `diftestp'<0.05{
|
|
di _col(10) in ye "DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS LIKELY"
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|
}
|
|
else{
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|
di _col(10) in ye "NO DIFFERENCE BETWEEN GROUPS DETECTED"
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|
}
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|
|
*********************************
|
|
*************MODEL C*************
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*********************************
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// Etape itérative si lrtest significatif
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|
local nb_stepC = 0
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|
|
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if `diftestp'<0.05{ /*If pvalue(LRtest)<0.05 then step C*/
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di
|
|
di in ye "PROCESSING STEP C"
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di
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/*test DIF pour chaque item*/
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local boucle = 1
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|
local stop = 0
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|
while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
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|
local nb_stepC = `boucle'
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|
local pajust=0.05/`=`nbitp'+1-`boucle''
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|
/*réinitialisation de la matrice de test*/
|
|
matrix test_difu_`boucle'=J(`nbitems',3,.)
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|
matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
|
|
matrix test_dif_`boucle'=J(`nbitems',3,.)
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|
matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
|
|
local nbsig=0
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|
local minpval=1
|
|
local itemdif=0
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|
di _col(10) "{hline 70}"
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|
di _col(10) in ye "Loop `boucle'" _col(50) "Adjusted alpha: " %6.4f `pajust'
|
|
di
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di in ye _col(40) "Chi-Square" _col(55) "DF" _col(60) "P-Value"
|
|
/*boucle de test*/
|
|
forvalues j=1/`nbitems'{
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|
//if `nbdif_`j'' > 2 {
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local model ""
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|
local listconst ""
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if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
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|
/*on libère le DIF de l'item i: pas de contraintes*/
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|
forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
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|
if `k'!=`j' & `nbmoda_`j'' >= 2 {
|
|
if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
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|
forvalues p=1/`nbdif_`k''{
|
|
qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
else{
|
|
if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
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|
forvalues p=2/`nbdif_`k''{
|
|
qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
forvalues jj=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`jj''{
|
|
local model "`model' (`p'.``jj''<-THETA@`p')"
|
|
}
|
|
}
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B)
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|
estimates store modeldif3b`boucle'it`i'
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|
|
|
*************************
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|
*****test DIF item i*****
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|
*************************
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|
qui test [1.``j'']0bn.`gp'=[1.``j'']1.`gp'
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if `nbmoda_`j'' > 2 {
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|
forvalues p=2/`nbdif_`j''{
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|
qui test [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp', acc
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|
}
|
|
}
|
|
matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
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|
|
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/* Test DIF Uniforme */
|
|
if `nbmoda_`j'' > 2 {
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|
qui test 2*([1.``j'']0bn.`gp'-[1.``j'']1.`gp')=[2.``j'']0bn.`gp'-[2.``j'']1.`gp'
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forvalues p=3/`nbdif_`j''{
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qui test `p'*([1.``j'']0bn.`gp'-[1.``j'']1.`gp')=[`p'.``j'']0bn.`gp'-[`p'.``j'']1.`gp', acc
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|
}
|
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matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
|
|
}
|
|
|
|
if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
|
|
local ++nbsig
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|
if test_dif_`boucle'[`j',3]<`minpval'{
|
|
local minpval=test_dif_`boucle'[`j',2]
|
|
local itemdif=`j'
|
|
}
|
|
}
|
|
di in ye _col(12) %-30s abbrev("``j'' :",22) in gr _col(40) %6.3f test_dif_`boucle'[`j',1] _col(55) test_dif_`boucle'[`j',2] _col(60) %6.4f test_dif_`boucle'[`j',3]
|
|
}
|
|
}
|
|
/*si nb de tests significatifs=0, on arrête*/
|
|
if `nbsig'==0{
|
|
local stop=1
|
|
if `boucle' == 1 {
|
|
di
|
|
di _col(20) in ye ">>> No significant test: no difference between groups detected, no DIF detected"
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
else {
|
|
di
|
|
di _col(20) in ye ">>> No other significant tests"
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
}
|
|
else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
|
|
matrix dif_rc[`itemdif',1]=`boucle'
|
|
di _col(15) _dup(60) "-"
|
|
di _col(15) in ye "Difference between groups on ``itemdif'' at time 1"
|
|
if `nbmoda_`itemdif'' > 2 {
|
|
di
|
|
di _col(40) "Chi-Square" _col(55) "DF" _col(60) "P-value"
|
|
di _col(15) in ye "Uniform ? " in gr _col(40) %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(55) `=test_difu_`boucle'[`itemdif',2]' _col(60) %4.2f `=test_difu_`boucle'[`itemdif',3]'
|
|
if test_difu_`boucle'[`itemdif',3]<0.05{ /*DIF NU détectée*/
|
|
matrix dif_rc[`itemdif',2]=0
|
|
di
|
|
di _col(17) in ye " >>> ``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
|
|
di _col(15) _dup(60) "-"
|
|
|
|
}
|
|
else{/*DIF U détectée*/
|
|
matrix dif_rc[`itemdif',2]=`boucle'
|
|
di
|
|
di _col(17) in ye ">>> ``itemdif'' : Uniform differences of item difficulties between groups at T1"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
}
|
|
else {
|
|
// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
}
|
|
local ++boucle
|
|
}
|
|
}
|
|
|
|
/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
|
|
if `nb_stepC' > 1 {
|
|
forvalues j=1/`nbitems'{
|
|
local model ""
|
|
local listconst ""
|
|
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else {
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
|
}
|
|
}
|
|
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B)
|
|
/* Stockage des estimations du modèle */
|
|
estimates store modeldifCFin
|
|
matrix val_mC = r(table)
|
|
|
|
/* Calcul des difficultés d'item (delta_j) */
|
|
matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC ""
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
|
}
|
|
}
|
|
local name_partOneL ""
|
|
forvalues j=1/`nbitems' {
|
|
local name_partOneL "`name_partOneL' ``j''"
|
|
}
|
|
matrix colnames delta_mCFin = `name_partOneC'
|
|
matrix rownames delta_mCFin = `name_partOneL'
|
|
|
|
matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
|
|
local name_partOneC_se ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues g=0/1 {
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
|
}
|
|
}
|
|
matrix colnames delta_mCFin_se = `name_partOneC_se'
|
|
matrix rownames delta_mCFin_se = `name_partOneL'
|
|
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues g=0/1{
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mCFin=r(estimate)
|
|
local delta`j'_`p'g`g'mCFin_se=r(se)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
|
local delta`j'_`p'g`g'mCFin = r(estimate)
|
|
local delta`j'_`p'g`g'mCFin_se = r(se)
|
|
}
|
|
matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
|
|
matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
|
|
}
|
|
}
|
|
}
|
|
if "`group'" != "" { //Variance et se mA
|
|
matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
|
|
}
|
|
/*group effect*/
|
|
qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
|
|
local geffmCFin=r(estimate)
|
|
local segeffmCFin=r(se)
|
|
qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
|
|
local gcmCFinp=r(p)
|
|
local gcmCFinchi=r(chi2)
|
|
local gcmCFindf=r(df)
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//////// PARTIE 2 : RECALIBRATION ? ////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
di
|
|
di _dup(59) "_ "
|
|
di
|
|
if "`group'" != "" {
|
|
di in ye "PART 2 : DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIMES (RECALIBRATION)"
|
|
}
|
|
else {
|
|
di in ye "DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIMES (RECALIBRATION)"
|
|
}
|
|
|
|
*********************************
|
|
** MODEL 2 **
|
|
*********************************
|
|
|
|
|
|
local listconst ""
|
|
|
|
forvalues j=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes)*/
|
|
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 {/*pas de DIF à T1 sur item k: contraintes 1*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else{
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2] != 0 { /*diff T1 U: contraintes 200*/
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
forvalues p=1/`nbdif_`j''{ /* egalites entre temps : groupe 0 (401-600)*/
|
|
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
forvalues p=1/`nbdif_`j''{ /* egalites entre temps : groupe 1 (601-800)*/
|
|
local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
|
|
local model ""
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
local model "`model' (`p'.``j''<-THETA1@`p')(`p'.``=`j'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst')
|
|
}
|
|
else {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@m20) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst')
|
|
}
|
|
|
|
/*Stockage des données du modèle 2 */
|
|
estimates store model2
|
|
matrix val_m2 = r(table)
|
|
matrix esti_2 = e(b)
|
|
|
|
if "`group'" != "" {
|
|
matrix var_m2 = (val_m2[1,"/var(THETA1)#0bn.`gp'"],val_m2[1,"/var(THETA2)#0bn.`gp'"]\val_m2[2,"/var(THETA1)#0bn.`gp'"],val_m2[2,"/var(THETA2)#0bn.`gp'"])
|
|
matrix covar_m2 = (val_m2[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m2[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m2[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[4,"/cov(THETA1,THETA2)#1.`gp'"])
|
|
}
|
|
else {
|
|
matrix var_m2 = (val_m2[1,"/var(THETA1)"],val_m2[1,"/var(THETA2)"]\val_m2[2,"/var(THETA1)"],val_m2[2,"/var(THETA2)"])
|
|
matrix covar_m2 = (val_m2[1,"/cov(THETA1,THETA2)"]\val_m2[2,"/cov(THETA1,THETA2)"]\val_m2[4,"/cov(THETA1,THETA2)"])
|
|
}
|
|
|
|
/*group effect*/
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
local geffm2=r(estimate)
|
|
local segeffm2=r(se)
|
|
local ubgeffm2 = r(ub)
|
|
local lbgeffm2 = r(lb)
|
|
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
|
|
local gpm2p=r(p)
|
|
local gpm2chi=r(chi2)
|
|
local gpm2df=r(df)
|
|
}
|
|
|
|
/*time effect*/
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
local teffm2=r(estimate)
|
|
local seteffm2=r(se)
|
|
local ubteffm2 = r(ub)
|
|
local lbteffm2 = r(lb)
|
|
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
|
|
local tm2p=r(p)
|
|
local tm2chi=r(chi2)
|
|
local tm2df=r(df)
|
|
}
|
|
else {
|
|
qui lincom [/]:mean(THETA2) /* [/]:mean(THETA1)*/
|
|
local teffm2=r(estimate)
|
|
local seteffm2=r(se)
|
|
local ubteffm2 = r(ub)
|
|
local lbteffm2 = r(lb)
|
|
qui test [/]:mean(THETA2) = 0 /* [/]:mean(THETA1)*/
|
|
local tm2p=r(p)
|
|
local tm2chi=r(chi2)
|
|
local tm2df=r(df)
|
|
}
|
|
|
|
*INTERACTION
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp'
|
|
local interm2=r(estimate)
|
|
local seinterm2=r(se)
|
|
local ubinterm2 = r(ub)
|
|
local lbinterm2 = r(lb)
|
|
qui test [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp' = 0
|
|
local interm2p=r(p)
|
|
local interm2chi=r(chi2)
|
|
local interm2df=r(df)
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
matrix mod2 = J(7,`=`nbmoda_sum'*4+6',.)
|
|
local name_partTwoC ""
|
|
forvalues j = 1/`nbitems' {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
forvalues t=1/2 {
|
|
forvalues g = 0/1 {
|
|
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_gp`g'_t`t'"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) GROUP_Effect TIME_Effect INTER_TxG "
|
|
matrix colnames mod2 = `name_partTwoC'
|
|
matrix rownames mod2 = Estimate se Upper_b Lower_b Chi_square DF pvalue
|
|
}
|
|
else {
|
|
matrix mod2 = J(7,`=`nbmoda_sum'*2+4',.)
|
|
local name_partTwoC ""
|
|
forvalues j = 1/`nbitems' {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
forvalues t=1/2 {
|
|
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_t`t'"
|
|
}
|
|
}
|
|
}
|
|
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) TIME_Effect "
|
|
matrix colnames mod2 = `name_partTwoC'
|
|
matrix rownames mod2 = Estimate se Upper_b Lower_b Chi_square DF pvalue
|
|
}
|
|
|
|
*Difficultés
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues t=1/2{
|
|
if "`group'" != "" { // groupe binaire
|
|
forvalues g=0/1 {
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm2= r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm2_se= r(se)
|
|
local delta`t'_`j'_`p'g`g'm2_ub=r(ub)
|
|
local delta`t'_`j'_`p'g`g'm2_lb=r(lb)
|
|
local delta`t'_`j'_`p'g`g'm2_p=r(p)
|
|
if `p'>1 {
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm2=r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm2_se=r(se)
|
|
local delta`t'_`j'_`p'g`g'm2_ub=r(ub)
|
|
local delta`t'_`j'_`p'g`g'm2_lb=r(lb)
|
|
local delta`t'_`j'_`p'g`g'm2_p=r(p)
|
|
}
|
|
local place = 0
|
|
local compt = 1
|
|
while `compt' < `j' {
|
|
local place = `place' + `nbdif_`compt''
|
|
local ++compt
|
|
}
|
|
if `t' == 1 {
|
|
matrix mod2[1,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2'
|
|
matrix mod2[2,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_se'
|
|
matrix mod2[3,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_ub'
|
|
matrix mod2[4,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_lb'
|
|
matrix mod2[7,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_p'
|
|
}
|
|
if `t' == 2 {
|
|
matrix mod2[1,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2'
|
|
matrix mod2[2,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_se'
|
|
matrix mod2[3,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_ub'
|
|
matrix mod2[4,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_lb'
|
|
matrix mod2[7,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_p'
|
|
}
|
|
}
|
|
}
|
|
else { // groupe unique (=gp0)
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']_cons
|
|
local delta`t'_`j'_`p'g0m2= r(estimate)
|
|
local delta`t'_`j'_`p'g0m2_se= r(se)
|
|
local delta`t'_`j'_`p'g0m2_ub=r(ub)
|
|
local delta`t'_`j'_`p'g0m2_lb=r(lb)
|
|
local delta`t'_`j'_`p'g0m2_p=r(p)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']_cons
|
|
local delta`t'_`j'_`p'g0m2=r(estimate)
|
|
local delta`t'_`j'_`p'g0m2_se=r(se)
|
|
local delta`t'_`j'_`p'g0m2_ub=r(ub)
|
|
local delta`t'_`j'_`p'g0m2_lb=r(lb)
|
|
local delta`t'_`j'_`p'g0m2_p=r(p)
|
|
}
|
|
local place = 0
|
|
local compt = 1
|
|
while `compt' < `j' {
|
|
local place = `place' + `nbdif_`compt''
|
|
local ++compt
|
|
}
|
|
if `t' == 1 {
|
|
matrix mod2[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2'
|
|
matrix mod2[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_se'
|
|
matrix mod2[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_ub'
|
|
matrix mod2[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_lb'
|
|
matrix mod2[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_p'
|
|
}
|
|
if `t' == 2 {
|
|
matrix mod2[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2'
|
|
matrix mod2[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_se'
|
|
matrix mod2[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_ub'
|
|
matrix mod2[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_lb'
|
|
matrix mod2[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_p'
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
matrix mod2[1,`=4*`nbmoda_sum'+1'] = (val_m2[1,"/var(THETA1)#0bn.`gp'"], val_m2[1,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod2[2,`=4*`nbmoda_sum'+1'] = (val_m2[2,"/var(THETA1)#0bn.`gp'"],val_m2[2,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod2[3,`=4*`nbmoda_sum'+1'] = (val_m2[6,"/var(THETA1)#0bn.`gp'"],val_m2[6,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod2[4,`=4*`nbmoda_sum'+1'] = (val_m2[5,"/var(THETA1)#0bn.`gp'"],val_m2[5,"/var(THETA2)#0bn.`gp'"])
|
|
|
|
matrix mod2[1,`=4*`nbmoda_sum'+2+1'] = (val_m2[1,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod2[2,`=4*`nbmoda_sum'+2+1'] = (val_m2[2,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod2[3,`=4*`nbmoda_sum'+2+1'] = (val_m2[6,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod2[4,`=4*`nbmoda_sum'+2+1'] = (val_m2[5,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
|
|
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1'] = `geffm2'
|
|
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1'] = `segeffm2'
|
|
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1'] = `ubgeffm2'
|
|
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1'] = `lbgeffm2'
|
|
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1'] = `gpm2chi'
|
|
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1'] = `gpm2df'
|
|
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1'] = `gpm2p'
|
|
|
|
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1+1'] = `teffm2'
|
|
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1+1'] = `seteffm2'
|
|
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1+1'] = `ubteffm2'
|
|
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1+1'] = `lbteffm2'
|
|
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2chi'
|
|
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2df'
|
|
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2p'
|
|
|
|
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2'
|
|
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1+1+1'] = `seinterm2'
|
|
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1+1+1'] = `ubinterm2'
|
|
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1+1+1'] = `lbinterm2'
|
|
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2chi'
|
|
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2df'
|
|
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2p'
|
|
}
|
|
else {
|
|
matrix mod2[1,`=2*`nbmoda_sum'+1'] = (val_m2[1,"/var(THETA1)"],val_m2[1,"/var(THETA2)"])
|
|
matrix mod2[2,`=2*`nbmoda_sum'+1'] = (val_m2[2,"/var(THETA1)"],val_m2[2,"/var(THETA2)"])
|
|
matrix mod2[3,`=2*`nbmoda_sum'+1'] = (val_m2[6,"/var(THETA1)"],val_m2[6,"/var(THETA2)"])
|
|
matrix mod2[4,`=2*`nbmoda_sum'+1'] = (val_m2[5,"/var(THETA1)"],val_m2[5,"/var(THETA2)"])
|
|
|
|
matrix mod2[1,`=2*`nbmoda_sum'+2+1'] = (val_m2[1,"/cov(THETA1,THETA2)"])
|
|
matrix mod2[2,`=2*`nbmoda_sum'+2+1'] = (val_m2[2,"/cov(THETA1,THETA2)"])
|
|
matrix mod2[3,`=2*`nbmoda_sum'+2+1'] = (val_m2[6,"/cov(THETA1,THETA2)"])
|
|
matrix mod2[4,`=2*`nbmoda_sum'+2+1'] = (val_m2[5,"/cov(THETA1,THETA2)"])
|
|
|
|
matrix mod2[1,`=2*`nbmoda_sum'+2+1+1'] = `teffm2'
|
|
matrix mod2[2,`=2*`nbmoda_sum'+2+1+1'] = `seteffm2'
|
|
matrix mod2[3,`=2*`nbmoda_sum'+2+1+1'] = `ubteffm2'
|
|
matrix mod2[4,`=2*`nbmoda_sum'+2+1+1'] = `lbteffm2'
|
|
matrix mod2[5,`=2*`nbmoda_sum'+2+1+1'] = `tm2chi'
|
|
matrix mod2[6,`=2*`nbmoda_sum'+2+1+1'] = `tm2df'
|
|
matrix mod2[7,`=2*`nbmoda_sum'+2+1+1'] = `tm2p'
|
|
}
|
|
|
|
|
|
*********************************
|
|
** MODEL 1 **
|
|
*********************************
|
|
|
|
|
|
/*PCM longitudinal, no true change, group effect, interaction*/
|
|
local listconst ""
|
|
forvalues j=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
|
|
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 {/*pas de DIF sur item k: contraintes 1*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else{
|
|
if `nbdif_`j'' > 1 {
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2] != 0 { /*diff T1 U: contraintes 201*/
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
local model ""
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
local model "`model' (`p'.``j''<-THETA1@`p')(`p'.``=`j'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
|
|
if "`group'"!="" {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@0) means(1: THETA1@m1 THETA2@m1) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
}
|
|
else {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@0) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) from(esti_2, skip)
|
|
}
|
|
|
|
/* Stockage des estimations du modèle 1 */
|
|
estimates store model1
|
|
matrix val_m1 = r(table)
|
|
|
|
/* Calcul des difficultés d'item (delta_j) */
|
|
matrix delta_m1 = J(`nbitems',`=`nbdif_max'*4',.)
|
|
local name_partTwoC ""
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues t=1/2 {
|
|
forvalues g = 0/1 {
|
|
local name_partTwoC "`name_partTwoC' delta_t`t'_`p'_gp`g'"
|
|
}
|
|
}
|
|
}
|
|
|
|
local name_partTwoL ""
|
|
forvalues j=1/`=`nbitems'*2' {
|
|
if `j' <= `nbitems' {
|
|
local name_partTwoL "`name_partTwoL' ``j''"
|
|
}
|
|
else {
|
|
local name_partTwoL "`name_partTwoL' ``=`nbitems'+`j'''"
|
|
}
|
|
}
|
|
|
|
matrix colnames delta_m1 = `name_partTwoC'
|
|
matrix rownames delta_m1 = `name_partTwoL'
|
|
|
|
matrix delta_m1_se = J(`nbitems',`=`nbdif_max'*4',.)
|
|
local name_partTwoC_se ""
|
|
|
|
forvalues p=1/`nbdif_max' {
|
|
forvalues t=1/2 {
|
|
forvalues g = 0/1 {
|
|
local name_partTwoC_se "`name_partTwoC_se' delta_t`t'_`p'_gp`g'_se"
|
|
}
|
|
}
|
|
}
|
|
|
|
matrix colnames delta_m1_se = `name_partTwoC_se'
|
|
matrix rownames delta_m1_se = `name_partTwoL'
|
|
|
|
if "`group'"!="" {
|
|
forvalues t=1/2{
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues g=0/1{
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm1= r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm1_se= r(se)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm1=r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm1_se=r(se)
|
|
}
|
|
if `t' == 1 {
|
|
matrix delta_m1[`j',`=4*(`p'-1)+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1'
|
|
matrix delta_m1_se[`j',`=4*(`p'-1)+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1_se'
|
|
}
|
|
if `t' == 2 {
|
|
matrix delta_m1[`j',`=4*(`p'-1)+1+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1'
|
|
matrix delta_m1_se[`j',`=4*(`p'-1)+1+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1_se'
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
forvalues t=1/2 {
|
|
forvalues j=1/`nbitems' {
|
|
forvalues p = 1/`nbdif_`j'' {
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:_cons
|
|
local delta`t'_`j'_`p'g0m1= r(estimate)
|
|
local delta`t'_`j'_`p'g0m1_se= r(se)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']:_cons
|
|
local delta`t'_`j'_`p'g0m1=r(estimate)
|
|
local delta`t'_`j'_`p'g0m1_se=r(se)
|
|
}
|
|
if `t' == 1 {
|
|
matrix delta_m1[`j',`=4*(`p'-1)+`t'']=`delta`t'_`j'_`p'g0m1'
|
|
matrix delta_m1_se[`j',`=4*(`p'-1)+`t'']=`delta`t'_`j'_`p'g0m1_se'
|
|
}
|
|
if `t' == 2 {
|
|
matrix delta_m1[`j',`=4*(`p'-1)+1+`t'']=`delta`t'_`j'_`p'g0m1'
|
|
matrix delta_m1_se[`j',`=4*(`p'-1)+1+`t'']=`delta`t'_`j'_`p'g0m1_se'
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
matrix var_m1 = (val_m1[1,"/var(THETA1)#0bn.`gp'"],val_m1[1,"/var(THETA2)#0bn.`gp'"]\val_m1[2,"/var(THETA1)#0bn.`gp'"],val_m1[2,"/var(THETA2)#0bn.`gp'"])
|
|
matrix covar_m1 = (val_m1[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m1[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m1[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[4,"/cov(THETA1,THETA2)#1.`gp'"])
|
|
}
|
|
else {
|
|
matrix var_m1 = (val_m1[1,"/var(THETA1)"],val_m1[1,"/var(THETA2)"]\val_m1[2,"/var(THETA1)"],val_m1[2,"/var(THETA2)"])
|
|
matrix covar_m1 = (val_m1[1,"/cov(THETA1,THETA2)"]\val_m1[2,"/cov(THETA1,THETA2)"]\val_m1[4,"/cov(THETA1,THETA2)"])
|
|
}
|
|
|
|
/*group effect*/
|
|
if "`group'"!="" {
|
|
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
local geffm1=r(estimate)
|
|
local segeffm1=r(se)
|
|
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
|
|
local gpm1p=r(p)
|
|
local gpm1chi=r(chi2)
|
|
local gpm1df=r(df)
|
|
}
|
|
|
|
*************************************************************
|
|
***********************AFFICHAGE*****************************
|
|
*************************************************************
|
|
|
|
// Affichage du modèle 1
|
|
di
|
|
di in ye "PROCESSING STEP 1"
|
|
di
|
|
/* Affichage des estimations des difficultés */
|
|
|
|
if "`group'" != "" {
|
|
di _col(5) in ye "{ul:MODEL 1:} Group effect estimated, no time effect (mean of latent trait of group 0 at T2) equal to mean of group 0 at T1),"
|
|
di _col(7) in ye "all item difficulties are freely estimated across times"
|
|
di
|
|
di _col(10) in ye "Item difficulties estimates (s.e.)"
|
|
di _col(10) "{hline 85}"
|
|
di _col(30) "Time 1" _col(70) "Time 2"
|
|
di in ye _col(20) abbrev("`gp'",15) "=0" _col(38) abbrev("`gp'",15) "=1" _col(58) abbrev("`gp'",15) "=0" _col(76) abbrev("`gp'",15) "=1"
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(5) in ye "{ul:MODEL 1}: no time effect,"
|
|
di _col(7) in ye "all item difficulties are freely estimated across times"
|
|
di
|
|
di _col(10) in ye "Item difficulties estimates (s.e.)"
|
|
di _col(10) "{hline 50}"
|
|
di _col(30) "Time 1" _col(45) "Time 2"
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
|
|
forvalues j=1/`nbitems' {
|
|
di in ye _col(10) "``j''"
|
|
forvalues p=1/`nbdif_`j'' {
|
|
if "`group'" != "" {
|
|
di in gr _col(10) "`p'" _col(20) %6.2f `delta1_`j'_`p'g0m1' " (" %4.2f `delta1_`j'_`p'g0m1_se' ")" _col(38) %6.2f `delta1_`j'_`p'g1m1' " (" %4.2f `delta1_`j'_`p'g1m1_se' ")" ///
|
|
_col(58) %6.2f `delta2_`j'_`p'g0m1' " (" %4.2f `delta2_`j'_`p'g0m1_se' ")" _col(76) %6.2f `delta2_`j'_`p'g1m1' " (" %4.2f `delta2_`j'_`p'g1m1_se' ")"
|
|
}
|
|
else {
|
|
di in gr _col(10) "`p'" _col(25) %6.2f `delta1_`j'_`p'g0m1' " (" %4.2f `delta1_`j'_`p'g0m1_se' ")" _col(42) %6.2f `delta2_`j'_`p'g0m1' " (" %4.2f `delta2_`j'_`p'g0m1_se' ")"
|
|
}
|
|
}
|
|
}
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
/* Affichage des estimations du trait latent du modèle 1 */
|
|
di
|
|
di _col(10) in ye "Latent trait distribution estimates"
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 80}"
|
|
di _col(45) in ye "Estimate" _col(60) "Standard error" _col(77) "P-value"
|
|
di _col(10) "{hline 80}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 70}"
|
|
di _col(45) in ye "Estimate" _col(60) "Standard error" "
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
di _col(10) in ye "Variance Time 1" in gr _col(44) %6.2f `=var_m1[1,1]' _col(62) %6.2f `=var_m1[2,1]'
|
|
di _col(10) in ye "Variance Time 2" in gr _col(44) %6.2f `=var_m1[1,2]' _col(62) %6.2f `=var_m1[2,2]'
|
|
di _col(10) in ye "Covariance" in gr _col(44) %6.2f `=covar_m1[1,1]' _col(62) %6.2f `=covar_m1[2,1]'
|
|
if "`group'" != "" {
|
|
di _col(10) in ye "Group effect (mean gp 1 at T1)" in gr _col(44) %6.2f `geffm1' _col(62) %6.2f `segeffm1' _col(77) %6.4f `gpm1p'
|
|
}
|
|
di _col(10) in ye "Time effect (mean gp 0 at T2)" in gr _col(44) "0 (constrained)"
|
|
if "`group'" != "" {
|
|
di _col(10) in ye "TimexGroup inter" in gr _col(44) "0 (constrained)"
|
|
}
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 80}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
//Affichage du modèle 2
|
|
di
|
|
di in ye "PROCESSING STEP 2"
|
|
di
|
|
/* Affichage des estimations des difficultés */
|
|
|
|
if "`group'" != "" {
|
|
di _col(5) in ye "{ul:MODEL 2:} Group effect, time effect (mean of latent trait of group 0 at T2) free estimated, time x group interaction estimated,"
|
|
di _col(7) in ye "Same item difficulties estimated across times"
|
|
di
|
|
di _col(10) in ye "Item difficulties estimates (s.e.)"
|
|
di _col(10) "{hline 85}"
|
|
di _col(30) "Time 1" _col(70) "Time 2"
|
|
di in ye _col(20) abbrev("`gp'",15) "=0" _col(38) abbrev("`gp'",15) "=1" _col(58) abbrev("`gp'",15) "=0" _col(76) abbrev("`gp'",15) "=1"
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(5) in ye "{ul:MODEL 2}: time effect estimated (mean of latent trait group 0 at T2) free estimated,"
|
|
di _col(7) in ye "Same item difficulties estimated across times"
|
|
di
|
|
di _col(10) in ye "Item difficulties estimates (s.e.)"
|
|
di _col(10) "{hline 50}"
|
|
di _col(30) "Time 1" _col(45) "Time 2"
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
|
|
forvalues j=1/`nbitems' {
|
|
di in ye _col(10) "``j''"
|
|
forvalues p=1/`nbdif_`j'' {
|
|
if "`group'" != "" {
|
|
di in gr _col(10) "`p'" _col(20) %6.2f `delta1_`j'_`p'g0m2' " (" %4.2f `delta1_`j'_`p'g0m2_se' ")" _col(38) %6.2f `delta1_`j'_`p'g1m2' " (" %4.2f `delta1_`j'_`p'g1m2_se' ")" ///
|
|
_col(58) %6.2f `delta2_`j'_`p'g0m2' " (" %4.2f `delta2_`j'_`p'g0m2_se' ")" _col(76) %6.2f `delta2_`j'_`p'g1m2' " (" %4.2f `delta2_`j'_`p'g1m2_se' ")"
|
|
}
|
|
else {
|
|
di in gr _col(10) "`p'" _col(25) %6.2f `delta1_`j'_`p'g0m2' " (" %4.2f `delta1_`j'_`p'g0m2_se' ")" _col(42) %6.2f `delta2_`j'_`p'g0m2' " (" %4.2f `delta2_`j'_`p'g0m2_se' ")"
|
|
}
|
|
}
|
|
}
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
/* Affichage des estimations du trait latent du modèle 2 */
|
|
di
|
|
di _col(10) in ye "Latent trait distribution estimates"
|
|
di _col(10) "{hline 80}"
|
|
di _col(45) in ye "Estimate" _col(60) "Standard error" _col(77) "P-value"
|
|
di _col(10) "{hline 80}"
|
|
|
|
if "`group'" == "" {
|
|
local fact_k = 2
|
|
}
|
|
else {
|
|
local fact_k = 4
|
|
}
|
|
|
|
di _col(10) in ye "Variance Time 1" in gr _col(44) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+1']' _col(62) %6.2f =mod2[2,`=`fact_k'*`nbmoda_sum'+1']
|
|
di _col(10) in ye "Variance Time 2" in gr _col(44) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+2']' _col(62) %6.2f `=mod2[2,`=`fact_k'*`nbmoda_sum'+2']'
|
|
di _col(10) in ye "Covariance" in gr _col(44) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+3']' _col(62) %6.2f `=mod2[2,`=`fact_k'*`nbmoda_sum'+3']'
|
|
|
|
if "`group'" != "" {
|
|
|
|
di _col(10) in ye "Group effect (mean gp 1 at T1)" in gr _col(44) %6.2f `geffm2' _col(62) %6.2f `segeffm2' _col(77) %6.4f `gpm2p'
|
|
}
|
|
di _col(10) in ye "Time effect (mean gp 0 at T2)" in gr _col(44) %6.2f `teffm2' _col(62) %6.2f `seteffm2' _col(77) %6.4f `tm2p'
|
|
|
|
if "`group'" != "" {
|
|
di _col(10) in ye "TimexGroup inter" in gr _col(44) %6.2f `interm2' _col(62) %6.2f `seinterm2' _col(77) %6.4f `interm2p'
|
|
}
|
|
di _col(10) "{hline 80}"
|
|
di
|
|
*****************************************************
|
|
* Modèle 1 vs Modèle 2 *
|
|
*****************************************************
|
|
qui lrtest model2 model1
|
|
|
|
local rstestp=r(p)
|
|
local rstestchi=r(chi2)
|
|
local rstestdf=r(df)
|
|
di _col(10) in ye "LIKELIHOOD-RATIO TEST MODEL 1 VS MODEL 2"
|
|
di _col(10) "{hline 50}"
|
|
di _col(10) in ye "Chi-square" _col(30) "DF" _col(50) "P-value"
|
|
di _col(10) in gr %6.2f `rstestchi' _col(30) %2.0f `rstestdf' _col(50) %6.4f `rstestp'
|
|
di _col(10) "{hline 50}"
|
|
if `rstestp'<0.05{
|
|
di _col(10) in ye "DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIMES LIKELY"
|
|
}
|
|
else{
|
|
di _col(10) in ye "NO DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIMES DETECTED, NO RECALIBRATION DETECTED"
|
|
}
|
|
|
|
*********************************
|
|
*************MODEL 3*************
|
|
*********************************
|
|
// Etape itérative si lrtest significatif
|
|
local nb_step3=0
|
|
|
|
if `rstestp' < 0.05 { /* If pvalue(LRtest)<0.05 then step 3 */
|
|
di
|
|
di in ye "PROCESSING STEP 3"
|
|
di
|
|
/*test RC pour chaque item*/
|
|
local boucle = 1
|
|
local stop = 0
|
|
//matrix list dif_rc
|
|
while `boucle' <= `=`nbitp'-1' & `stop' == 0 { /*on s'arrête quand on a libéré du RC sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
|
|
local nb_step3 = `boucle'
|
|
local pajust=0.05/`=`nbitp'+1-`boucle'' // local pajust=0.05/`=`nbitems'+1-`boucle'
|
|
if "`group'" != "" {
|
|
local pajust2 = 0.05/`nbgrp'
|
|
}
|
|
/*réinitialisation de la matrice de test*/
|
|
matrix test_rc_`boucle'=J(`nbitems',9,.)
|
|
matrix test_rcCOMM_`boucle'=J(`nbitems',3,.)
|
|
matrix test_rcU_`boucle'=J(`nbitems',6,.)
|
|
matrix colnames test_rc_`boucle'= chi_RC df_RC pvalue_RC chi_RCg0 df_RCg0 pvalue_RCg0 chi_RCg1 df_RCg1 pvalue_RCg1
|
|
matrix colnames test_rcCOMM_`boucle'= chi_RCCOMM df_RCCOMM pvalue_RCCOMM
|
|
matrix colnames test_rcU_`boucle'= chi_RCUg0 df_RCUg0 pvalue_RCUg0 chi_RCUg1 df_RCUg1 pvalue_RCUg1
|
|
local nbsig=0
|
|
local minpval=1
|
|
local itemrc=0
|
|
di _col(10) "{hline 70}"
|
|
di _col(10) in ye "Loop `boucle'" _col(50) "Adjusted alpha : " %6.4f `pajust'
|
|
di
|
|
di in ye _col(40) "Chi-Square" _col(55) "DF" _col(60) "P-Value"
|
|
/*boucle de test*/
|
|
forvalues j=1/`nbitems'{
|
|
if `nbdif_`j'' >= 1 {
|
|
local model ""
|
|
local listconst ""
|
|
if dif_rc[`j',3]==. { /*si pas de RC déjà détecté sur l'item j -> test item j*/
|
|
/*on libère la RC de l'item j: pas de contraintes*/
|
|
forvalues k=1/`nbitems'{
|
|
/* Contraintes de DIF */
|
|
if dif_rc[`k',1]==.|dif_rc[`k',1]==0 { // contraintes si pas de DIF (1-200)
|
|
forvalues p=1/`nbdif_`k''{
|
|
local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
else { // Présence de DIF
|
|
if dif_rc[`k',2]!=. & dif_rc[`k',2]!=0 { // contraintes de DIF U (201-400)
|
|
if `nbmoda_`k'' > 2 {
|
|
forvalues p=2/`nbdif_`k''{
|
|
local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if `k'!=`j'{ /*contraintes pour les autres items */
|
|
if dif_rc[`k',3]==. | dif_rc[`k',3]==0 {/*pas de RC sur item k: contraintes 401-600 601-800*/
|
|
forvalues p=1/`nbdif_`k''{
|
|
local listconst "`listconst' `=400+`maxdif'*(`k'-1)+`p'' `=600+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`k'-1)+`p'' `=600+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
else { //RC détectée sur l'item k
|
|
if dif_rc[`k',4]==0{ /*RC commune: contraintes 801-1000*/
|
|
forvalues p=1/`nbdif_`k''{ /***************************** j=1 ou 2 ?****/
|
|
local listconst "`listconst' `=800+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=800+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
if dif_rc[`k',6]!=. & dif_rc[`k',6]!=0 { // RC commune unif.
|
|
if `nbmoda_`k'' > 2 {
|
|
forvalues p=2/`nbdif_`k''{
|
|
local listconst "`listconst' `=1000+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else { // RC diff
|
|
if dif_rc[`k',5]==. | dif_rc[`k',5]==0 { // RC gp0 (400)
|
|
forvalues p=1/`nbdif_`k''{
|
|
local listconst "`listconst' `=400+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`k',6]!=. & dif_rc[`k',6]!=0 { // RCU gp0 (1001-1200)
|
|
if `nbmoda_`k'' > 2 {
|
|
forvalues p=2/`nbdif_`k''{
|
|
local listconst "`listconst' `=1000+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
if dif_rc[`k',7]==. | dif_rc[`k',7]==0 { // RC gp1 (600)
|
|
forvalues p=1/`nbdif_`k''{
|
|
local listconst "`listconst' `=600+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=600+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`k',8]!=. & dif_rc[`k',8]!=0 { // RCU gp1 (1201-1400)
|
|
if `nbmoda_`k'' > 2 {
|
|
forvalues p=2/`nbdif_`k''{
|
|
local listconst "`listconst' `=1200+`maxdif'*(`k'-1)+`p''"
|
|
qui constraint list `=1200+`maxdif'*(`k'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
qui di "`listconst'"
|
|
local model ""
|
|
forvalues jj=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`jj''{
|
|
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
if "`group'" == "" { // Sans l'option group
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@m20) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
|
|
/*****************/
|
|
/*tests RC item i*/
|
|
/*****************/
|
|
|
|
/* RC ? */
|
|
qui test [1.``j'']_cons =[1.``=`j'+`nbitems''']_cons
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``j'']_cons =[`p'.``=`j'+`nbitems''']_cons, acc
|
|
}
|
|
}
|
|
matrix test_rc_`boucle'[`j',1]=(r(chi2),r(df),r(p))
|
|
|
|
/* RCU ? */
|
|
if `nbmoda_`j'' > 2 {
|
|
qui test 2*([1.``=`j'+`nbitems''']_cons -[1.``j'']_cons)=[2.``=`j'+`nbitems''']_cons -[2.``j'']_cons
|
|
forvalues p=3/`nbdif_`j''{
|
|
qui test `p'*([1.``=`j'+`nbitems''']_cons -[1.``j'']_cons)=[`p'.``=`j'+`nbitems''']_cons -[`p'.``j'']_cons , acc
|
|
}
|
|
matrix test_rcU_`boucle'[`j',1]=(r(chi2), r(df),r(p))
|
|
}
|
|
}
|
|
else { // Avec l'option group
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
|
|
/*****************/
|
|
/*tests RC item i*/
|
|
/*****************/
|
|
|
|
/* RC ? */
|
|
qui test [1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']0bn.`gp'
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp', acc
|
|
}
|
|
}
|
|
qui test [1.``j'']1.`gp'=[1.``=`j'+`nbitems''']1.`gp', acc
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp', acc
|
|
}
|
|
}
|
|
matrix test_rc_`boucle'[`j',1]=(r(chi2),r(df),r(p))
|
|
/* RC COMMUNE ? */
|
|
qui test [1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp'
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp', acc
|
|
}
|
|
}
|
|
matrix test_rcCOMM_`boucle'[`j',1]=(r(chi2),r(df),r(p))
|
|
/* RC groupe 0 ? */
|
|
qui test [1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']0bn.`gp'
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp', acc
|
|
}
|
|
}
|
|
matrix test_rc_`boucle'[`j',4]=(r(chi2),r(df),r(p))
|
|
|
|
/* RCU grp 0 ? */
|
|
if `nbmoda_`j'' > 2 {
|
|
qui test 2*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[2.``=`j'+`nbitems''']0bn.`gp'-[2.``j'']0bn.`gp'
|
|
forvalues p=3/`nbdif_`j''{
|
|
qui test `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp', acc
|
|
}
|
|
matrix test_rcU_`boucle'[`j',1]=(r(chi2),r(df),r(p))
|
|
}
|
|
|
|
/* RC groupe 1 ? */
|
|
qui test [1.``j'']1.`gp'=[1.``=`j'+`nbitems''']1.`gp'
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui test [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp', acc
|
|
}
|
|
}
|
|
matrix test_rc_`boucle'[`j',7]=(r(chi2),r(df),r(p))
|
|
|
|
/* RCU grp 1 ? */
|
|
if `nbmoda_`j'' > 2 {
|
|
qui test 2*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[2.``=`j'+`nbitems''']1.`gp'-[2.``j'']1.`gp'
|
|
forvalues p=3/`nbdif_`j''{
|
|
qui test `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp', acc
|
|
}
|
|
matrix test_rcU_`boucle'[`j',4]=(r(chi2),r(df),r(p))
|
|
}
|
|
}
|
|
/******* Matrice test complète *********/
|
|
di in ye _col(10) %-30s abbrev("``j'' :",22) in gr _col(40) %6.3f test_rc_`boucle'[`j',1] _col(55) test_rc_`boucle'[`j',2] _col(60) %6.4f test_rc_`boucle'[`j',3]
|
|
}
|
|
}
|
|
}
|
|
//matrix list test_rc_`boucle'
|
|
forvalues j=1/`nbitems'{
|
|
if test_rc_`boucle'[`j',3]<`pajust'{/*si RC sur item i*/
|
|
if test_rc_`boucle'[`j',3]<`minpval'{
|
|
local minpval=test_rc_`boucle'[`j',3]
|
|
local itemrc=`j'
|
|
}
|
|
}
|
|
}
|
|
if `itemrc' != 0 { // itemrc = numéro de l'item avec le test le + sig.
|
|
if "`group'" == "" { // Recalibration si pas d'option groupe
|
|
local ++nbsig
|
|
matrix dif_rc[`itemrc',3]=`boucle'
|
|
matrix dif_rc[`itemrc',5]=`boucle'
|
|
if `nbmoda_`itemrc'' > 2 {
|
|
di _col(15) _dup(60) "-"
|
|
di _col(15) in ye "Recalibration on ``itemrc''"
|
|
di _col(40) "Chi-square" _col(55) "DF" _col(60) "P-value"
|
|
di _col(15) in ye "Uniform RC ? " in gr _col(40) %4.2f `=test_rcU_`boucle'[`itemrc',1]' _col(55) `=test_rcU_`boucle'[`itemrc',2]' _col(60) %6.4f `=test_rcU_`boucle'[`itemrc',3]'
|
|
if test_rcU_`boucle'[`itemrc',3] >= 0.05 { //RC Uniforme sur itemRC
|
|
matrix dif_rc[`itemrc',6]=`boucle'
|
|
di
|
|
di _col(17) in ye ">>> ``itemrc'' : Uniform RC"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
else {
|
|
matrix dif_rc[`itemrc',6]=0
|
|
di
|
|
di _col(17) in ye ">>> ``itemrc'' : Non-uniform RC"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
}
|
|
else {
|
|
di
|
|
di _col(17) in ye ">>> ``itemrc'' : Recalibration "
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
}
|
|
else { // Option groupe
|
|
di _col(15) _dup(60) "-"
|
|
di _col(15) in ye "Recalibration on ``itemrc''"
|
|
di
|
|
di _col(40) "Chi-Square" _col(55) "DF" _col(60) "P-value"
|
|
di _col(15) in ye "Common RC ? " in gr _col(40) %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' _col(55) `=test_rcCOMM_`boucle'[`itemrc',2]' _col(60) %6.4f `=test_rcCOMM_`boucle'[`itemrc',3]'
|
|
if test_rcCOMM_`boucle'[`itemrc',3] < 0.05 { //RC différentielle
|
|
di
|
|
di _col(15) in ye "RC group 0 ? " in gr _col(40) %4.2f `=test_rc_`boucle'[`itemrc',4]' _col(55) `=test_rc_`boucle'[`itemrc',5]' _col(60) %6.4f `=test_rc_`boucle'[`itemrc',6]' "{it: - with adjusted alpha = `pajust2' }"
|
|
if test_rc_`boucle'[`itemrc',6] < `pajust2' { //RC gp 0
|
|
local ++nbsig
|
|
matrix dif_rc[`itemrc',3]=`boucle'
|
|
matrix dif_rc[`itemrc',4]=`boucle'
|
|
matrix dif_rc[`itemrc',5]=`boucle'
|
|
if `nbmoda_`itemrc'' > 2 {
|
|
di _col(15) in ye "Uniform RC on gp 0 ? " in gr _col(40) %4.2f `=test_rcU_`boucle'[`itemrc',1]' _col(55) `=test_rcU_`boucle'[`itemrc',2]' _col(60) %6.4f `=test_rcU_`boucle'[`itemrc',3]'
|
|
if test_rcU_`boucle'[`itemrc',3] >= 0.05 { // RCU gp 0
|
|
matrix dif_rc[`itemrc',6]=`boucle'
|
|
local phrase_diff = ">>> ``itemrc'' : Uniform differential RC on group 0."
|
|
}
|
|
else {
|
|
matrix dif_rc[`itemrc',6]=0
|
|
local phrase_diff = ">>> ``itemrc'' : Non-uniform differential RC on group 0."
|
|
}
|
|
}
|
|
else {
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC on group 0."
|
|
}
|
|
}
|
|
di
|
|
di _col(15) in ye "RC group 1 ? " in gr _col(40) %4.2f `=test_rc_`boucle'[`itemrc',7]' _col(55) `=test_rc_`boucle'[`itemrc',8]' _col(60) %6.4f `=test_rc_`boucle'[`itemrc',9]' "{it: - with adjusted alpha = `pajust2' }"
|
|
if test_rc_`boucle'[`itemrc',9] < `pajust2' { //RC gp 1
|
|
local ++nbsig
|
|
matrix dif_rc[`itemrc',3]=`boucle'
|
|
matrix dif_rc[`itemrc',4]=`boucle'
|
|
matrix dif_rc[`itemrc',7]=`boucle'
|
|
if `nbmoda_`itemrc'' > 2 {
|
|
di _col(15) in ye "Uniform RC on gp 1 ? " in gr _col(40) %4.2f `=test_rcU_`boucle'[`itemrc',4]' _col(55) `=test_rcU_`boucle'[`itemrc',5]' _col(60) %6.4f `=test_rcU_`boucle'[`itemrc',6]'
|
|
if test_rcU_`boucle'[`itemrc',6] >= 0.05 { // RCU gp 1
|
|
matrix dif_rc[`itemrc',8]=`boucle'
|
|
di
|
|
if dif_rc[`itemrc',5] != `boucle' { //RC slmt sur g1
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, uniform RC on group 1."
|
|
}
|
|
else {
|
|
if dif_rc[`itemrc',6] == 0 { // + RCNU g0
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, non-uniform RC on group 0 and uniform RC on group 1."
|
|
}
|
|
else { // + RCU G0
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, uniform RC on group 0 and uniform RC on group 1."
|
|
}
|
|
}
|
|
}
|
|
else { //RCNU gp 1
|
|
matrix dif_rc[`itemrc',8]=0
|
|
di
|
|
if dif_rc[`itemrc',5] != `boucle' {
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, non-uniform RC on group 1."
|
|
}
|
|
else {
|
|
if dif_rc[`itemrc',6] == 0 { // + RCNU g0
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, non-uniform RC on group 0 and non-uniform RC on group 1."
|
|
}
|
|
else { // + RCU G0
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC, uniform RC on group 0 and non-uniform RC on group 1."
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
if dif_rc[`itemrc',5] != `boucle' {
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC on group 1."
|
|
}
|
|
else {
|
|
local phrase_diff = ">>> ``itemrc'' : Differential RC on group 0 and differential RC on group 1."
|
|
}
|
|
}
|
|
}
|
|
di
|
|
di _col(17) in ye "`phrase_diff'"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
else { // RC commune -> MAJ modèle 3
|
|
/*******************************************************************************************************************/
|
|
if `nbmoda_`itemrc'' == 2 {
|
|
di
|
|
di _col(14) in ye ">>> {ul:``itemrc''}: recalibration"
|
|
di _col(20) in ye "Common " in gr "{it:(Chi-s: " %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' ", DF: `=test_rcCOMM_`boucle'[`itemrc',2]' p-val. : " %4.2f `=test_rcCOMM_`boucle'[`itemrc',3]' ")}"
|
|
matrix dif_rc[`itemrc',3]=`boucle'
|
|
matrix dif_rc[`itemrc',4]=0
|
|
matrix dif_rc[`itemrc',5]=`boucle'
|
|
matrix dif_rc[`itemrc',7]=`boucle'
|
|
local ++nbsig
|
|
}
|
|
else {
|
|
matrix dif_rc[`itemrc',3]=`boucle'
|
|
matrix dif_rc[`itemrc',4]=0
|
|
matrix dif_rc[`itemrc',5]=`boucle'
|
|
matrix dif_rc[`itemrc',7]=`boucle'
|
|
//matrix list dif_rc
|
|
local model ""
|
|
local listconst ""
|
|
forvalues j=1/`nbitems'{
|
|
/* Contraintes de DIF */
|
|
if dif_rc[`j',1]==.|dif_rc[`j',1]==0 { // contraintes si pas de DIF (1-200)
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else { // Présence de DIF
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { // contraintes de DIF U (201-400)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if `j' != `itemrc'{ /*contraintes pour les autres items */
|
|
if dif_rc[`j',3]==. | dif_rc[`j',3]==0 {/*pas de RC sur item p: contraintes 401-600 601-800*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else { //RC détectée sur l'item p
|
|
if dif_rc[`j',4]==0{ /*RC commune: contraintes 801-1000*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=800+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=800+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RC commune unif.
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else if dif_rc[`j',4] != 0 & dif_rc[`j',4]!=0. { // RC diff
|
|
if dif_rc[`j',5]==. | dif_rc[`j',5]==0 { // RC gp0 (400)
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RCU gp0 (1001-1200)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
if dif_rc[`j',7]==. | dif_rc[`j',7]==0 { // RC gp1 (600)
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`j',8]!=. & dif_rc[`j',8]!=0 { // RCU gp1 (1201-1400)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=1200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else { // Contrainte de RC commune pour l'itemrc
|
|
forvalues p=1/`nbdif_`j''{
|
|
qui local listconst "`listconst' `=800+`maxdif'*(`itemrc'-1)+`p''"
|
|
qui constraint list `=800+`maxdif'*(`itemrc'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
qui di "`listconst'"
|
|
local model ""
|
|
forvalues jj=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`jj''{
|
|
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
|
|
/************************/
|
|
/*tests RC item `itemrc'*/
|
|
/************************/
|
|
matrix commU_`boucle'=J(`nbitems',3,.) //Matrice des tests de RCU slmt si RC commune
|
|
matrix colnames commU_`boucle'= chi_RCU df_RCU p_RCU
|
|
|
|
/* RCU grp 0 ? */
|
|
if `nbmoda_`itemrc'' > 2 {
|
|
qui test 2*([1.``=`itemrc'+`nbitems''']0bn.`gp'-[1.``itemrc'']0bn.`gp')=[2.``=`itemrc'+`nbitems''']0bn.`gp'-[2.``itemrc'']0bn.`gp'
|
|
forvalues j=3/`nbdif_`itemrc''{
|
|
qui test `j'*([1.``=`itemrc'+`nbitems''']0bn.`gp'-[1.``itemrc'']0bn.`gp')=[`j'.``=`itemrc'+`nbitems''']0bn.`gp'-[`j'.``itemrc'']0bn.`gp', acc
|
|
}
|
|
matrix commU_`boucle'[`itemrc',1]=(r(chi2),r(df),r(p))
|
|
di _col(15) in ye "Uniform RC ?" in gr _col(40) %4.2f `=commU_`boucle'[`itemrc',1]' _col(55) `=commU_`boucle'[`itemrc',2]' _col(60) %6.4f `=commU_`boucle'[`itemrc',3]'
|
|
if commU_`boucle'[`itemrc',3] >= 0.05 { // RCU
|
|
local ++nbsig
|
|
matrix dif_rc[`itemrc',6]=`boucle'
|
|
matrix dif_rc[`itemrc',8]=`boucle'
|
|
di
|
|
//di _col(14) in ye ">>> {ul:``itemrc''}: recalibration"
|
|
//di _col(20) in ye "Common " in gr "{it:(Chi-s: " %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' ", DF: `=test_rcCOMM_`boucle'[`itemrc',2]' p-val. : " %4.2f `=test_rcCOMM_`boucle'[`itemrc',3]' ")}"
|
|
di _col(17) in ye ">>> ``itemrc'' : Uniform common RC"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
else {
|
|
local ++nbsig
|
|
matrix dif_rc[`itemrc',6]=0
|
|
matrix dif_rc[`itemrc',8]=0
|
|
di
|
|
di _col(17) in ye ">>> ``itemrc'' : Non-uniform common RC"
|
|
di _col(15) _dup(60) "-"
|
|
}
|
|
}
|
|
}
|
|
} // fin de RC commune
|
|
}
|
|
}
|
|
else {
|
|
local stop = 1
|
|
}
|
|
/*******************************************************************************************************************/
|
|
// Fin de RC sur item i
|
|
if `nbsig'==0{
|
|
local stop=1
|
|
if `boucle' == 1 {
|
|
di
|
|
di _col(20) in ye "No significant tests, no recalibration detected"
|
|
di _col(10) "{hline 70}"
|
|
di
|
|
}
|
|
else {
|
|
di
|
|
di _col(20) in ye ">>> No other significant tests"
|
|
di _col(10) "{hline 70}"
|
|
di
|
|
}
|
|
}
|
|
local ++boucle
|
|
}
|
|
}
|
|
|
|
|
|
|
|
*********************************
|
|
*** BILAN ***
|
|
*********************************
|
|
|
|
|
|
|
|
if "`group'" != "" & "`nodif'" == "" {
|
|
di
|
|
di _col(2) "{hline 80}"
|
|
di in ye _col(18) "Difference in"
|
|
di in ye _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration" _col(54) "RC " abbrev("`gp'",10) " 0" _col(72) "RC " abbrev("`gp'",10) " 1"
|
|
di _col(2) "{hline 80}"
|
|
forvalues j=1/`nbitems' {
|
|
local RC
|
|
local RCg0
|
|
local RCg1
|
|
local difft1
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
|
|
local RC "Common"
|
|
}
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
|
|
local RC "Differential"
|
|
}
|
|
if `nbmoda_`j'' > 2 {
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
|
local RCg0 "Uniform"
|
|
}
|
|
if (dif_rc[`j',6] == 0) {
|
|
local RCg0 "Non-uniform"
|
|
}
|
|
if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
|
|
local RCg1 "Uniform"
|
|
}
|
|
if ( dif_rc[`j',8] == 0) {
|
|
local RCg1 "Non-uniform"
|
|
}
|
|
if (dif_rc[`j',1] != . ) {
|
|
if (dif_rc[`j',2]!=0) {
|
|
local difft1 "Uniform"
|
|
}
|
|
else {
|
|
local difft1 "Non-uniform"
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
if dif_rc[`j',6] != . {
|
|
local RCg0 " X "
|
|
}
|
|
if dif_rc[`j',8] != . {
|
|
local RCg1 " X "
|
|
}
|
|
if dif_rc[`j',1] != . {
|
|
local difft1 " X "
|
|
}
|
|
}
|
|
di in ye _col(2) abbrev("``j''",15) in gr _col(18) "`difft1'" _col(36) "`RC'" _col(54) "`RCg0'" _col(72) "`RCg1'"
|
|
}
|
|
di _col(2) "{hline 80}"
|
|
}
|
|
else if "`group'" != "" & "`nodif'" != "" {
|
|
di
|
|
di _col(10) "{hline 70}"
|
|
di in ye _col(10) "Item" _col(26) "Recalibration" _col(46) "RC `gp' 0" _col(62) "RC `gp' 1"
|
|
di _col(10) "{hline 70}"
|
|
forvalues j=1/`nbitems' {
|
|
local RC
|
|
local RCg0
|
|
local RCg1
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
|
|
local RC "Common"
|
|
}
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
|
|
local RC "Differential"
|
|
}
|
|
if `nbmoda_`j'' > 2 {
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
|
local RCg0 "Uniform"
|
|
}
|
|
if (dif_rc[`j',6] == 0) {
|
|
local RCg0 "Non-uniform"
|
|
}
|
|
if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
|
|
local RCg1 "Uniform"
|
|
}
|
|
if ( dif_rc[`j',8] == 0) {
|
|
local RCg1 "Non-uniform"
|
|
}
|
|
}
|
|
else {
|
|
if dif_rc[`j',6] != . {
|
|
local RCg0 " X "
|
|
}
|
|
if dif_rc[`j',8] != . {
|
|
local RCg1 " X "
|
|
}
|
|
}
|
|
di in ye _col(10) "``j''" in gr _col(26) "`RC'" _col(44) "`RCg0'" _col(62) "`RCg1'"
|
|
}
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
else if "`group'" == "" {
|
|
di
|
|
di _col(10) "{hline 40}"
|
|
di in ye _col(10) "Item" _col(36) "Recalibration"
|
|
di _col(10) "{hline 40}"
|
|
forvalues j=1/`nbitems' {
|
|
local RC
|
|
if dif_rc[`j',3] != . {
|
|
if `nbmoda_`j'' > 2 {
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
|
local RC "Uniform"
|
|
}
|
|
if (dif_rc[`j',6] == 0) {
|
|
local RC "Non-uniform"
|
|
}
|
|
}
|
|
else {
|
|
local RC " X "
|
|
}
|
|
}
|
|
di in ye _col(10) "``j''" in gr _col(38) "`RC'"
|
|
}
|
|
di _col(10) "{hline 40}"
|
|
}
|
|
|
|
|
|
*********************************
|
|
** MODEL 4 **
|
|
*********************************
|
|
di
|
|
di in ye "PROCESSING STEP 4"
|
|
di
|
|
//matrix list dif_rc, title ("Constraints")
|
|
|
|
local model ""
|
|
local listconst ""
|
|
forvalues j=1/`nbitems'{
|
|
if dif_rc[`j',1]==.|dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200 */
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else { // Présence de DIF
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { // contraintes de DIF U (201-400)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if dif_rc[`j',3]==. | dif_rc[`j',3]==0 {/*pas de RC : contraintes 401-600 601-800*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
else { //RC détectée sur l'item j
|
|
if dif_rc[`j',4]==0{ /*RC commune: contraintes 801-1000*/
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=800+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=800+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RC commune unif.
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else { // RC diff
|
|
if dif_rc[`j',5]==. | dif_rc[`j',5]==0 { // RC gp0 (400)
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RCU gp0 (1001-1200)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
if dif_rc[`j',7]==. | dif_rc[`j',7]==0 { // RC gp1 (600)
|
|
forvalues p=1/`nbdif_`j''{
|
|
local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
if dif_rc[`j',8]!=. & dif_rc[`j',8]!=0 { // RCU gp1 (1201-1400)
|
|
if `nbmoda_`j'' > 2 {
|
|
forvalues p=2/`nbdif_`j''{
|
|
local listconst "`listconst' `=1200+`maxdif'*(`j'-1)+`p''"
|
|
qui constraint list `=1200+`maxdif'*(`j'-1)+`p''
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
qui di "`listconst'"
|
|
local model ""
|
|
|
|
forvalues jj=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`jj''{
|
|
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
}
|
|
else {
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) means( THETA1@0 THETA2@m2) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip)
|
|
}
|
|
/* Stockage des estimations du modèle */
|
|
matrix val_m4 = r(table)
|
|
matrix esti_4 = e(b)
|
|
|
|
if "`group'" != "" {
|
|
matrix var_m4 = (val_m4[1,"/var(THETA1)#0bn.`gp'"],val_m4[1,"/var(THETA2)#0bn.`gp'"]\val_m4[2,"/var(THETA1)#0bn.`gp'"],val_m4[2,"/var(THETA2)#0bn.`gp'"])
|
|
matrix covar_m4 = (val_m4[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m4[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m4[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[4,"/cov(THETA1,THETA2)#1.`gp'"])
|
|
}
|
|
else {
|
|
matrix var_m4 = (val_m4[1,"/var(THETA1)"],val_m4[1,"/var(THETA2)"]\val_m4[2,"/var(THETA1)"],val_m4[2,"/var(THETA2)"])
|
|
matrix covar_m4 = (val_m4[1,"/cov(THETA1,THETA2)"]\val_m4[2,"/cov(THETA1,THETA2)"]\val_m4[4,"/cov(THETA1,THETA2)"])
|
|
}
|
|
|
|
/* Matrice des tests effet grp, tps et inter */
|
|
matrix effet = J(5,3,.)
|
|
matrix colnames effet= Groupe Temps Interaction
|
|
matrix rownames effet = Esti Std_Err Pvalue Chi DF
|
|
|
|
/*group effect*/
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
matrix effet[1,1] =r(estimate)
|
|
matrix effet[2,1]=r(se)
|
|
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
|
|
matrix effet[3,1]=r(p)
|
|
matrix effet[4,1]=r(chi2)
|
|
matrix effet[5,1]=r(df)
|
|
}
|
|
|
|
/*time effect*/
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
matrix effet[1,2]=r(estimate)
|
|
matrix effet[2,2]=r(se)
|
|
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
|
|
matrix effet[3,2]=r(p)
|
|
matrix effet[4,2]=r(chi2)
|
|
matrix effet[5,2]=r(df)
|
|
}
|
|
else {
|
|
qui lincom [/]:mean(THETA2) /* -[/]:mean(THETA1)*/
|
|
local teffm4=r(estimate)
|
|
local seteffm4=r(se)
|
|
local ubteffm4 = r(ub)
|
|
local lbteffm4 = r(lb)
|
|
qui test [/]:mean(THETA2) /* -[/]:mean(THETA1) */ = 0
|
|
local tm4p=r(p)
|
|
local tm4chi=r(chi2)
|
|
local tm4df=r(df)
|
|
}
|
|
|
|
*INTERACTION
|
|
if "`group'" != "" {
|
|
qui lincom [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp'
|
|
matrix effet[1,3]=r(estimate)
|
|
matrix effet[2,3]=r(se)
|
|
local ubinterm4=r(ub)
|
|
local lbinterm4=r(lb)
|
|
qui test [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp' = 0
|
|
matrix effet[3,3]=r(p)
|
|
matrix effet[4,3]=r(chi2)
|
|
matrix effet[5,3]=r(df)
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
local effet_tps = 0
|
|
local effet_grp = 0
|
|
|
|
if effet[3,3] >= 0.05 { // Si option group, on s'interesse à l'interaction temps x group, et MAJ modèle >>> modèle final = modèle 4 + contrainte 1999 (Interaction = 0)
|
|
/* Affichage des estimations sur le trait latent du modèle 4 */
|
|
di
|
|
di _col(10) in ye "Latent trait estimates"
|
|
di _col(10) "{hline 80}"
|
|
di _col(45) in ye "Estimate" _col(60) "Standard error" _col(77) "P-value"
|
|
di _col(10) "{hline 80}"
|
|
di _col(10) in ye "Variance Time 1" in gr _col(44) %6.2f `=var_m4[1,1]' _col(62) %6.2f `=var_m4[2,1]'
|
|
di _col(10) in ye "Variance Time 2" in gr _col(44) %6.2f `=var_m4[1,2]' _col(62) %6.2f `=var_m4[2,2]'
|
|
di _col(10) in ye "Covariance" in gr _col(44) %6.2f `=covar_m4[1,1]' _col(62) %6.2f `=covar_m4[2,1]'
|
|
if "`group'" != "" {
|
|
di _col(10) in ye "Group effect (mean gp 1 at T1)" in gr _col(44) %6.2f effet[1,1] _col(62) %6.2f effet[2,1] _col(77) %6.4f effet[3,1]
|
|
}
|
|
di _col(10) in ye "Time effect (mean gp 0 at T2)" in gr _col(44) %6.2f effet[1,2] _col(62) %6.2f effet[2,2] _col(77) %6.4f effet[3,2]
|
|
|
|
if "`group'" != "" {
|
|
di _col(10) in ye "TimexGroup inter" in gr _col(44) %6.2f effet[1,3] _col(62) %6.2f effet[2,3] _col(77) %6.4f effet[3,3]
|
|
}
|
|
di _col(10) "{hline 80}"
|
|
di
|
|
di in ye ">>> Time x group interaction : no significant test, estimate of model 4 with constraint of time x group interaction at 0 "
|
|
di
|
|
local yn_inter = 0
|
|
local listconst "`listconst' 1999"
|
|
qui di "`listconst'"
|
|
local model ""
|
|
forvalues jj=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`jj''{
|
|
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
|
|
}
|
|
}
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_4, skip)
|
|
|
|
matrix val_m4 = r(table)
|
|
}
|
|
else {
|
|
local yn_inter = 1
|
|
}
|
|
|
|
/*group effect*/
|
|
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
local geffm4=r(estimate)
|
|
local segeffm4=r(se)
|
|
local ubgeffm4=r(ub)
|
|
local lbgeffm4=r(lb)
|
|
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
|
|
local gpm4p=r(p)
|
|
local gpm4chi=r(chi2)
|
|
local gpm4df=r(df)
|
|
|
|
/*time effect*/
|
|
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
|
local teffm4=r(estimate)
|
|
local seteffm4=r(se)
|
|
local lbteffm4=r(lb)
|
|
local ubteffm4=r(ub)
|
|
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
|
|
local tm4p=r(p)
|
|
local tm4chi=r(chi2)
|
|
local tm4df=r(df)
|
|
}
|
|
|
|
/* Calcul des difficultés (delta_j) */
|
|
if "`group'" != "" {
|
|
matrix mod4 = J(7,`=`nbmoda_sum'*4+6',.)
|
|
local name_partTwoC ""
|
|
forvalues j = 1/`nbitems' {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
forvalues t=1/2 {
|
|
forvalues g = 0/1 {
|
|
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_gp`g'_t`t'"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) GROUP_Effect TIME_Effect INTER_TxG "
|
|
matrix colnames mod4 = `name_partTwoC'
|
|
matrix rownames mod4 = Estimate se Upper_b Lower_b Chi_square DF pvalue
|
|
}
|
|
else {
|
|
matrix mod4 = J(7,`=`nbmoda_sum'*2+4',.)
|
|
local name_partTwoC ""
|
|
forvalues j = 1/`nbitems' {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
forvalues t=1/2 {
|
|
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_t`t'"
|
|
}
|
|
}
|
|
}
|
|
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) TIME_Effect "
|
|
matrix colnames mod4 = `name_partTwoC'
|
|
matrix rownames mod4 = Estimate se Upper_b Lower_b Chi_square DF pvalue
|
|
}
|
|
|
|
*Difficultés
|
|
forvalues j=1/`nbitems'{
|
|
forvalues p=1/`nbdif_`j''{
|
|
forvalues t=1/2{
|
|
if "`group'" != "" { // groupe binaire
|
|
forvalues g=0/1 {
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm4= r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm4_se= r(se)
|
|
local delta`t'_`j'_`p'g`g'm4_ub=r(ub)
|
|
local delta`t'_`j'_`p'g`g'm4_lb=r(lb)
|
|
local delta`t'_`j'_`p'g`g'm4_p=r(p)
|
|
if `p'>1 {
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
|
|
local delta`t'_`j'_`p'g`g'm4=r(estimate)
|
|
local delta`t'_`j'_`p'g`g'm4_se=r(se)
|
|
local delta`t'_`j'_`p'g`g'm4_ub=r(ub)
|
|
local delta`t'_`j'_`p'g`g'm4_lb=r(lb)
|
|
local delta`t'_`j'_`p'g`g'm4_p=r(p)
|
|
}
|
|
local place = 0
|
|
local compt = 1
|
|
while `compt' < `j' {
|
|
local place = `place' + `nbdif_`compt''
|
|
local ++compt
|
|
}
|
|
if `t' == 1 {
|
|
matrix mod4[1,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4'
|
|
matrix mod4[2,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_se'
|
|
matrix mod4[3,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_ub'
|
|
matrix mod4[4,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_lb'
|
|
matrix mod4[7,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_p'
|
|
}
|
|
if `t' == 2 {
|
|
matrix mod4[1,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4'
|
|
matrix mod4[2,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_se'
|
|
matrix mod4[3,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_ub'
|
|
matrix mod4[4,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_lb'
|
|
matrix mod4[7,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_p'
|
|
}
|
|
}
|
|
}
|
|
else { // groupe unique (=gp0)
|
|
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']_cons
|
|
local delta`t'_`j'_`p'g0m4= r(estimate)
|
|
local delta`t'_`j'_`p'g0m4_se= r(se)
|
|
local delta`t'_`j'_`p'g0m4_ub=r(ub)
|
|
local delta`t'_`j'_`p'g0m4_lb=r(lb)
|
|
local delta`t'_`j'_`p'g0m4_p=r(p)
|
|
if `p'>1{
|
|
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']_cons
|
|
local delta`t'_`j'_`p'g0m4=r(estimate)
|
|
local delta`t'_`j'_`p'g0m4_se=r(se)
|
|
local delta`t'_`j'_`p'g0m4_ub=r(ub)
|
|
local delta`t'_`j'_`p'g0m4_lb=r(lb)
|
|
local delta`t'_`j'_`p'g0m4_p=r(p)
|
|
}
|
|
local place = 0
|
|
local compt = 1
|
|
while `compt' < `j' {
|
|
local place = `place' + `nbdif_`compt''
|
|
local ++compt
|
|
}
|
|
if `t' == 1 {
|
|
matrix mod4[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4'
|
|
matrix mod4[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_se'
|
|
matrix mod4[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_ub'
|
|
matrix mod4[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_lb'
|
|
matrix mod4[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_p'
|
|
}
|
|
if `t' == 2 {
|
|
matrix mod4[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4'
|
|
matrix mod4[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_se'
|
|
matrix mod4[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_ub'
|
|
matrix mod4[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_lb'
|
|
matrix mod4[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_p'
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
matrix mod4[1,`=4*`nbmoda_sum'+1'] = (val_m4[1,"/var(THETA1)#0bn.`gp'"], val_m4[1,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod4[2,`=4*`nbmoda_sum'+1'] = (val_m4[2,"/var(THETA1)#0bn.`gp'"],val_m4[2,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod4[3,`=4*`nbmoda_sum'+1'] = (val_m4[6,"/var(THETA1)#0bn.`gp'"],val_m4[6,"/var(THETA2)#0bn.`gp'"])
|
|
matrix mod4[4,`=4*`nbmoda_sum'+1'] = (val_m4[5,"/var(THETA1)#0bn.`gp'"],val_m4[5,"/var(THETA2)#0bn.`gp'"])
|
|
|
|
matrix mod4[1,`=4*`nbmoda_sum'+2+1'] = (val_m4[1,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod4[2,`=4*`nbmoda_sum'+2+1'] = (val_m4[2,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod4[3,`=4*`nbmoda_sum'+2+1'] = (val_m4[6,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
matrix mod4[4,`=4*`nbmoda_sum'+2+1'] = (val_m4[5,"/cov(THETA1,THETA2)#0.`gp'"])
|
|
|
|
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1'] = `geffm4'
|
|
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1'] = `segeffm4'
|
|
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1'] = `ubgeffm4'
|
|
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1'] = `lbgeffm4'
|
|
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1'] = `gpm4chi'
|
|
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1'] = `gpm4df'
|
|
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1'] = `gpm4p'
|
|
|
|
|
|
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1+1'] = `teffm4'
|
|
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1+1'] = `seteffm4'
|
|
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1+1'] = `ubteffm4'
|
|
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1+1'] = `lbteffm4'
|
|
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4chi'
|
|
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4df'
|
|
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4p'
|
|
|
|
if `yn_inter' == 1 { //Slmt si model avec interaction
|
|
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[1,3]
|
|
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[2,3]
|
|
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1+1+1'] = `ubinterm4'
|
|
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1+1+1'] = `lbinterm4'
|
|
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[4,3]
|
|
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[5,3]
|
|
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[3,3]
|
|
}
|
|
|
|
}
|
|
else {
|
|
matrix mod4[1,`=2*`nbmoda_sum'+1'] = (val_m4[1,"/var(THETA1)"],val_m4[1,"/var(THETA2)"])
|
|
matrix mod4[2,`=2*`nbmoda_sum'+1'] = (val_m4[2,"/var(THETA1)"],val_m4[2,"/var(THETA2)"])
|
|
matrix mod4[3,`=2*`nbmoda_sum'+1'] = (val_m4[6,"/var(THETA1)"],val_m4[6,"/var(THETA2)"])
|
|
matrix mod4[4,`=2*`nbmoda_sum'+1'] = (val_m4[5,"/var(THETA1)"],val_m4[5,"/var(THETA2)"])
|
|
|
|
matrix mod4[1,`=2*`nbmoda_sum'+2+1'] = (val_m4[1,"/cov(THETA1,THETA2)"])
|
|
matrix mod4[2,`=2*`nbmoda_sum'+2+1'] = (val_m4[2,"/cov(THETA1,THETA2)"])
|
|
matrix mod4[3,`=2*`nbmoda_sum'+2+1'] = (val_m4[6,"/cov(THETA1,THETA2)"])
|
|
matrix mod4[4,`=2*`nbmoda_sum'+2+1'] = (val_m4[5,"/cov(THETA1,THETA2)"])
|
|
|
|
matrix mod4[1,`=2*`nbmoda_sum'+2+1+1'] = `teffm4'
|
|
matrix mod4[2,`=2*`nbmoda_sum'+2+1+1'] = `seteffm4'
|
|
matrix mod4[3,`=2*`nbmoda_sum'+2+1+1'] = `ubteffm4'
|
|
matrix mod4[4,`=2*`nbmoda_sum'+2+1+1'] = `lbteffm4'
|
|
matrix mod4[5,`=2*`nbmoda_sum'+2+1+1'] = `tm4chi'
|
|
matrix mod4[6,`=2*`nbmoda_sum'+2+1+1'] = `tm4df'
|
|
matrix mod4[7,`=2*`nbmoda_sum'+2+1+1'] = `tm4p'
|
|
}
|
|
|
|
|
|
/* Affichage des estimations des difficultés */
|
|
di _col(5) in ye "{ul:MODEL 4} = Final model"
|
|
di
|
|
di _col(10) in ye "Item difficulties estimates (s.e.)"
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 85}"
|
|
di _col(30) "Time 1" _col(70) "Time 2"
|
|
di in ye _col(20) abbrev("`gp'",15) "=0" _col(38) abbrev("`gp'",15) "=1" _col(58) abbrev("`gp'",15) "=0" _col(76) abbrev("`gp'",15) "=1"
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 50}"
|
|
di _col(30) "Time 1" _col(45) "Time 2"
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
|
|
forvalues j=1/`nbitems' {
|
|
di in ye _col(10) "``j''"
|
|
forvalues p=1/`nbdif_`j'' {
|
|
if "`group'" != "" {
|
|
di in gr _col(10) "`p'" _col(20) %6.2f `delta1_`j'_`p'g0m4' " (" %4.2f `delta1_`j'_`p'g0m4_se' ")" _col(38) %6.2f `delta1_`j'_`p'g1m4' " (" %4.2f `delta1_`j'_`p'g1m4_se' ")" ///
|
|
_col(58) %6.2f `delta2_`j'_`p'g0m4' " (" %4.2f `delta2_`j'_`p'g0m4_se' ")" _col(76) %6.2f `delta2_`j'_`p'g1m4' " (" %4.2f `delta2_`j'_`p'g1m4_se' ")"
|
|
}
|
|
else {
|
|
di in gr _col(10) "`p'" _col(25) %6.2f `delta1_`j'_`p'g0m4' " (" %4.2f `delta1_`j'_`p'g0m4_se' ")" _col(42) %6.2f `delta2_`j'_`p'g0m4' " (" %4.2f `delta2_`j'_`p'g0m4_se' ")"
|
|
}
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" {
|
|
di _col(10) "{hline 85}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
|
|
/* Affichage des estimations sur le trait latent du modèle final */
|
|
di
|
|
di _col(10) in ye "Latent trait distribution estimates"
|
|
di _col(10) "{hline 80}"
|
|
di _col(45) in ye "Estimate" _col(60) "Standard error" _col(77) "P-value"
|
|
di _col(10) "{hline 80}"
|
|
|
|
if "`group'" == "" {
|
|
local fact_k = 2
|
|
}
|
|
else {
|
|
local fact_k = 4
|
|
}
|
|
|
|
di _col(10) in ye "Variance Time 1" in gr _col(44) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+1']' _col(62) %6.2f =mod4[2,`=`fact_k'*`nbmoda_sum'+1']
|
|
di _col(10) in ye "Variance Time 2" in gr _col(44) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+2']' _col(62) %6.2f `=mod4[2,`=`fact_k'*`nbmoda_sum'+2']'
|
|
di _col(10) in ye "Covariance" in gr _col(44) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+3']' _col(62) %6.2f `=mod4[2,`=`fact_k'*`nbmoda_sum'+3']'
|
|
|
|
if "`group'" != "" {
|
|
|
|
di _col(10) in ye "Group effect (mean gp 1 at T1)" in gr _col(44) %6.2f `geffm4' _col(62) %6.2f `segeffm4' _col(77) %6.4f `gpm4p'
|
|
}
|
|
di _col(10) in ye "Time effect (mean gp 0 at T2)" in gr _col(44) %6.2f `teffm4' _col(62) %6.2f `seteffm4' _col(77) %6.4f `tm4p'
|
|
|
|
if "`group'" != "" {
|
|
if effet[3,3] < 0.05 {
|
|
di _col(10) in ye "TimexGroup inter" in gr _col(44) %6.2f effet[1,3] _col(62) %6.2f effet[2,3] _col(77) %6.4f effet[3,3]
|
|
}
|
|
else {
|
|
di _col(10) in ye "TimexGroup inter" in gr _col(44) "0 (constrained)"
|
|
}
|
|
}
|
|
|
|
di _col(10) "{hline 80}"
|
|
|
|
/***************************************/
|
|
/* Calcul des valeurs de DIF et de RC */
|
|
/*************************************/
|
|
|
|
forvalues j=1/`nbitems' {
|
|
if `nbmoda_`j'' >= 2 {
|
|
matrix valeur_difrc_`j' = J(`nbdif_`j'',8,.)
|
|
matrix colnames valeur_difrc_`j' = DIFT1 DIFT1_SE RC_GP0 RC_GP0_SE RC_GP1 RC_GP1_SE
|
|
}
|
|
}
|
|
|
|
forvalues j=1/`nbitems'{
|
|
if `nbmoda_`j'' >= 2 {
|
|
if "`group'" != "" {
|
|
*DIF
|
|
if "`nodif'"=="" {
|
|
if (dif_rc[`j',1] != . ) {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
if `p' == 1 {
|
|
qui lincom -[1.``j'']:1.`gp'+[1.``j'']:0.`gp'
|
|
matrix valeur_difrc_`j'[`p',1] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',2] = round(r(se),0.01)
|
|
}
|
|
if `p' > 1 {
|
|
qui lincom [`=`p'-1'.``j'']:1.`gp' - [`p'.``j'']:1.`gp' -[`=`p'-1'.``j'']:0.`gp' + [`p'.``j'']:0.`gp'
|
|
matrix valeur_difrc_`j'[`p',1] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',2] = round(r(se),0.01)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
*RC GROUP 0
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',5] != . ) {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
qui lincom -[1.``=`j'+`nbitems''']:0.`gp' + [1.``j'']:0.`gp'
|
|
matrix valeur_difrc_`j'[`p',3] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
|
|
if `p' > 1 {
|
|
qui lincom [`=`p'-1'.``=`j'+`nbitems''']:0.`gp' - [`p'.``=`j'+`nbitems''']:0.`gp' -[`=`p'-1'.``j'']:0.`gp' + [`p'.``j'']:0.`gp'
|
|
matrix valeur_difrc_`j'[`p',3] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
|
|
}
|
|
}
|
|
}
|
|
*RC GROUP 1
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',7] != . ) {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
qui lincom -[1.``=`j'+`nbitems''']:1.`gp' + [1.``j'']:1.`gp'
|
|
matrix valeur_difrc_`j'[`p',5] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',6] = round(r(se),0.01)
|
|
if `p' > 1 {
|
|
qui lincom [`=`p'-1'.``=`j'+`nbitems''']:1.`gp' - [`p'.``=`j'+`nbitems''']:1.`gp' -[`=`p'-1'.``j'']:1.`gp' + [`p'.``j'']:1.`gp'
|
|
matrix valeur_difrc_`j'[`p',5] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',6] = round(r(se),0.01)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
forvalues p=1/`nbdif_`j'' {
|
|
qui lincom -[1.``=`j'+`nbitems''']_cons + [1.``j'']_cons
|
|
matrix valeur_difrc_`j'[`p',3] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
|
|
if `p' > 1 {
|
|
qui lincom [`=`p'-1'.``=`j'+`nbitems''']_cons - [`p'.``=`j'+`nbitems''']_cons -[`=`p'-1'.``j'']_cons + [`p'.``j'']_cons
|
|
matrix valeur_difrc_`j'[`p',3] = r(estimate)
|
|
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
forvalues j = 1/`nbitems' {
|
|
if `nbmoda_`j'' >= 2 {
|
|
forvalues p = 1/`nbdif_`j'' {
|
|
forvalues k = 1/8 {
|
|
if valeur_difrc_`j'[`p',`k'] == . {
|
|
matrix valeur_difrc_`j'[`p',`k'] = 0
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Affichage des estimations des valeurs de DIF et de RC */
|
|
di
|
|
di _col(10) in ye "Values of differences between groups and values of recalibration"
|
|
if "`group'" != "" & "`nodif'"==""{
|
|
di _col(10) "{hline 70}"
|
|
di _col(30) "Difference of" _col(52) "RECALIBRATION"
|
|
di _col(30) "groups at T1" _col(47) in ye abbrev("`gp'",15) "=0" _col(62) abbrev("`gp'",15) " =1"
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
else if "`group'" != "" & "`nodif'"!="" {
|
|
di _col(10) "{hline 50}"
|
|
di _col(32) "RECALIBRATION"
|
|
di in ye _col(27) "`gp'=`=rep[1,1]'" _col(47) "`gp'=`=rep[2,1]'"
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 30}"
|
|
di _col(15) "RECALIBRATION"
|
|
di _col(10) "{hline 30}"
|
|
}
|
|
|
|
forvalues j=1/`nbitems' {
|
|
if `nbmoda_`j'' >= 2 {
|
|
if "`group'" != "" & "`nodif'" == "" {
|
|
di in ye _col(10) "``j''"
|
|
}
|
|
else {
|
|
di in ye _col(10) "``j''"
|
|
}
|
|
forvalues p=1/`nbdif_`j'' {
|
|
if "`group'" != "" & "`nodif'"=="" {
|
|
di in gr _col(10) "`p'" _col(29) %6.2f `=valeur_difrc_`j'[`p',1]' " (" %4.2f `=valeur_difrc_`j'[`p',2]' ")" ///
|
|
_col(47) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")" _col(62) %6.2f `=valeur_difrc_`j'[`p',5]' " (" %4.2f `=valeur_difrc_`j'[`p',6]' ")"
|
|
}
|
|
else if "`group'" != "" & "`nodif'"!="" {
|
|
di in gr _col(10) "`p'" _col(25) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")" _col(45) %6.2f `=valeur_difrc_`j'[`p',5]' " (" %4.2f `=valeur_difrc_`j'[`p',6]' ")"
|
|
}
|
|
else {
|
|
di in gr _col(10) "`p'" _col(25) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if "`group'" != "" & "`nodif'"=="" {
|
|
di _col(10) "{hline 70}"
|
|
}
|
|
else if "`group'" != "" & "`nodif'"!=""{
|
|
di _col(10) "{hline 50}"
|
|
}
|
|
else {
|
|
di _col(10) "{hline 30}"
|
|
}
|
|
di
|
|
|
|
*****************************************************
|
|
** OUTPUTS POUR SIMULATIONS **
|
|
*****************************************************
|
|
|
|
/*
|
|
forvalues j= `nbitems' (-1) 1 {
|
|
if `nbmoda_`j'' >= 2 {
|
|
return matrix val_difrc_item`j'=valeur_difrc_`j'
|
|
}
|
|
}
|
|
|
|
* Modèle 4
|
|
return matrix table_m4 = val_m4
|
|
return matrix covariance_m4 = covar_m4
|
|
return matrix variance_m4 = var_m4
|
|
return matrix se_diff_m4 = delta_m4_se
|
|
return matrix difficultiesm4 = delta_m4
|
|
|
|
if "`group'" != "" {
|
|
return scalar with_inter=`yn_inter'
|
|
*Inter
|
|
return scalar interm4df=effet[5,3]
|
|
return scalar interm4chi=effet[4,3]
|
|
return scalar interm4p=effet[3,3]
|
|
return scalar interm4se=effet[2,3]
|
|
return scalar interm4=effet[1,3]
|
|
|
|
*Retour paramètres théta modèle 4
|
|
if effet[3,3] < 0.05 { // Si modèle avec inter
|
|
return scalar teffm4df=effet[5,2]
|
|
return scalar teffm4chi=effet[4,2]
|
|
return scalar teffm4p=effet[3,2]
|
|
return scalar teffm4se=effet[2,2]
|
|
return scalar teffm4= effet[1,2]
|
|
* Effet groupe
|
|
return scalar gpeffm4df=effet[5,1]
|
|
return scalar gpeffm4chi=effet[4,1]
|
|
return scalar gpeffm4p=effet[3,1]
|
|
return scalar gpeffm4se=effet[2,1]
|
|
return scalar gpeffm4=effet[1,1]
|
|
}
|
|
if effet[3,3] >= 0.05 {
|
|
* Effet temps
|
|
return scalar teffm4df=`tm4df'
|
|
return scalar teffm4chi=`tm4chi'
|
|
return scalar teffm4p=`tm4p'
|
|
return scalar teffm4se=`seteffm4'
|
|
return scalar teffm4=`teffm4'
|
|
* Effet groupe
|
|
return scalar gpeffm4df=`gpm4df'
|
|
return scalar gpeffm4chi=`gpm4chi'
|
|
return scalar gpeffm4p=`gpm4p'
|
|
return scalar gpeffm4se=`segeffm4'
|
|
return scalar gpeffm4=`geffm4'
|
|
}
|
|
}
|
|
else {
|
|
if effet[3,3] < 0.05 { // Si modèle avec inter
|
|
return scalar teffm4df=effet[5,2]
|
|
return scalar teffm4chi=effet[4,2]
|
|
return scalar teffm4p=effet[3,2]
|
|
return scalar teffm4se=effet[2,2]
|
|
return scalar teffm4= effet[1,2]
|
|
}
|
|
if effet[3,3] >= 0.05 {
|
|
* Effet temps
|
|
return scalar teffm4df=`tm4df'
|
|
return scalar teffm4chi=`tm4chi'
|
|
return scalar teffm4p=`tm4p'
|
|
return scalar teffm4se=`seteffm4'
|
|
return scalar teffm4=`teffm4'
|
|
}
|
|
}
|
|
|
|
* Boucle de RC
|
|
if `nb_step3' != 0 {
|
|
forvalues item = 1/`nbitems' {
|
|
forvalues s =1/`=`nb_step3'-1' {
|
|
if dif_rc[`item',4] == 0 & dif_rc[`item',3]==`s' & `nbmoda_`item'' > 2 {
|
|
return matrix commU_bcle`s'=commU_`s'
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if `nb_step3' != 0 {
|
|
forvalues s =1/`nb_step3' {
|
|
return matrix ptest_rc_bcle`s'=ptest_rc_`s'
|
|
return matrix chitest_rc_bcle`s'=chitest_rc_`s'
|
|
return matrix dftest_rc_bcle`s'=dftest_rc_`s'
|
|
}
|
|
}
|
|
|
|
* Modèle 2
|
|
return matrix table_m2 = val_m2
|
|
return matrix covariance_m2 = covar_m2
|
|
return matrix variance_m2 = var_m2
|
|
return matrix se_diff_m2 = delta_m2_se
|
|
return matrix difficultiesm2 = delta_m2
|
|
|
|
* Effet temps
|
|
if "`group'" != "" {
|
|
* Interaction
|
|
return scalar interm2df=`interm2df'
|
|
return scalar interm2chi=`interm2chi'
|
|
return scalar interm2p=`interm2p'
|
|
return scalar interm2se=`seinterm2'
|
|
return scalar interm2=`interm2'
|
|
}
|
|
return scalar teffm2df=`tm2df'
|
|
return scalar teffm2chi=`tm2chi'
|
|
return scalar teffm2p=`tm2p'
|
|
return scalar teffm2se=`seteffm2'
|
|
return scalar teffm2=`teffm2'
|
|
|
|
if "`group'" != "" { // SI option group
|
|
* Effet groupe
|
|
return scalar gpeffm2df=`gpm2df'
|
|
return scalar gpeffm2chi=`gpm2chi'
|
|
return scalar gpeffm2p=`gpm2p'
|
|
return scalar gpeffm2se=`segeffm2'
|
|
return scalar gpeffm2=`geffm2'
|
|
}
|
|
|
|
* Modèle 1
|
|
return matrix table_m1 = val_m1
|
|
return matrix covariance_m1 = covar_m1
|
|
return matrix variance_m1 = var_m1
|
|
return matrix se_diff_m1 = delta_m1_se
|
|
return matrix difficultiesm1 = delta_m1
|
|
|
|
if "`group'" != "" { // Si option group
|
|
* Effet groupe
|
|
return scalar gpeffm1df=`gpm1df'
|
|
return scalar gpeffm1chi=`gpm1chi'
|
|
return scalar gpeffm1p=`gpm1p'
|
|
return scalar gpeffm1se=`segeffm1'
|
|
return scalar gpeffm1=`geffm1'
|
|
}
|
|
|
|
* Modèle C
|
|
if "`group'" != "" & "`nodif'"=="" {
|
|
if `nb_stepC' != 0 {
|
|
forvalues s =1/`nb_stepC' {
|
|
return matrix ptest_dif_bcle`s'=ptest_dif_`s'
|
|
return matrix chitest_dif_bcle`s'=chitest_dif_`s'
|
|
return matrix dftest_dif_bcle`s'=dftest_dif_`s'
|
|
}
|
|
|
|
if `nb_stepC' != 1 {
|
|
* Effet groupe
|
|
return scalar gpeffmCdf=`gcmCFindf'
|
|
return scalar gpeffmCchi=`gcmCFinchi'
|
|
return scalar gpeffmCp=`gcmCFinp'
|
|
return scalar gpeffmCse=`segeffmCFin'
|
|
return scalar gpeffmC=`geffmCFin'
|
|
*Retour modèle C Final
|
|
return matrix table_mC = val_mC
|
|
return matrix variance_mC = var_mC
|
|
return matrix se_diff_mC = delta_mCFin_se
|
|
return matrix difficultiesmC = delta_mCFin
|
|
}
|
|
}
|
|
|
|
* Modèle B
|
|
return matrix table_mB = val_mB
|
|
return matrix variance_mB = var_mB
|
|
return matrix se_diff_mB = delta_mB_se
|
|
return matrix difficultiesmB = delta_mB
|
|
|
|
* Effet groupe
|
|
return scalar gpeffmBdf=`gcmBdf'
|
|
return scalar gpeffmBchi=`gcmBchi'
|
|
return scalar gpeffmBp=`gcmBp'
|
|
return scalar gpeffmBse=`segeffmB'
|
|
return scalar gpeffmB=`geffmB'
|
|
|
|
*Modèle A
|
|
return matrix table_mA = val_mA
|
|
return matrix variance_mA = var_mA
|
|
return matrix se_diff_mA = delta_mA_se
|
|
return matrix difficultiesmA = delta_mA
|
|
|
|
}
|
|
|
|
*Matrice dif_rc
|
|
return matrix rcres=dif_rc
|
|
|
|
forvalues j = 1/`nbitems' {
|
|
local colnam = "`colnam' item`j'"
|
|
}
|
|
|
|
return matrix modait= nbmod
|
|
|
|
*Retour test modèle 1 vs modèle 2
|
|
return scalar rstchi12=`rstestchi'
|
|
return scalar rstdf12=`rstestdf'
|
|
return scalar rstp12=`rstestp'
|
|
|
|
if "`group'" != "" & "`nodif'"=="" {
|
|
*Retour test modèle A vs modèle B
|
|
return scalar diftchiAB=`diftestchi'
|
|
return scalar diftdfAB=`diftestdf'
|
|
return scalar diftpAB=`diftestp'
|
|
}
|
|
|
|
//return scalar nb_boucle_rc=`nb_step3'
|
|
|
|
/*
|
|
if "`group'" != "" & "`nodif'"==""{
|
|
return scalar nb_boucle_dif=`nb_stepC'
|
|
}
|
|
*/
|
|
|
|
timer off 1
|
|
qui timer list 1
|
|
local temps = r(t1)
|
|
local minute = floor(`=`temps'/60')
|
|
local seconde = floor(`temps' - `=`minute'*60')
|
|
//di "Time : `temps's = `minute'min `seconde's"
|
|
*/
|
|
|
|
//return scalar time = `temps'
|
|
|
|
*******************************************************************************
|
|
* New outputs
|
|
|
|
if "`group'" == "" {
|
|
matrix testlrm = J(1,3,.)
|
|
matrix colnames testlrm = chi_square df pvalue
|
|
matrix rownames testlrm = m1_vs_m2
|
|
matrix testlrm[1,1] = (`rstestchi',`rstestdf',`rstestp')
|
|
return matrix test_model = testlrm
|
|
}
|
|
else if "`nodif'" != "" {
|
|
matrix testlrm = J(1,3,.)
|
|
matrix colnames testlrm = chi_square df pvalue
|
|
matrix rownames testlrm = m1_vs_m2
|
|
matrix testlrm[1,1] = (`rstestchi',`rstestdf',`rstestp')
|
|
return matrix test_model = testlrm
|
|
}
|
|
else {
|
|
matrix testlrm = J(2,3,.)
|
|
matrix colnames testlrm = chi_square df pvalue
|
|
matrix rownames testlrm = mA_vs_mB m1_vs_m2
|
|
matrix testlrm[1,1] = (`diftestchi',`diftestdf',`diftestp')
|
|
matrix testlrm[2,1] = (`rstestchi',`rstestdf',`rstestp')
|
|
return matrix test_model = testlrm
|
|
}
|
|
|
|
return matrix model_4 = mod4
|
|
return matrix model_2 = mod2
|
|
|
|
end
|
|
|