Computed theoretical power for N=100 and N=200 scenarios
This commit is contained in:
1530
Modules/ado/personal/m/Multirsoort_indicefit.ado
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1530
Modules/ado/personal/m/Multirsoort_indicefit.ado
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File diff suppressed because it is too large
Load Diff
24
Modules/ado/personal/m/matrixlisthtml.ado
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24
Modules/ado/personal/m/matrixlisthtml.ado
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program define matrixlisthtml
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args matrixname precision
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*mat li `matrixname'
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local rownames: rown `matrixname'
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local colnames: coln `matrixname'
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di "<tr><th></th>"
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foreach mycol in `colnames' {
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di "<th>" as text "`mycol'" "</th>"
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}
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di "</tr>"
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local rowindex=1
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forvalues rowptr=1/`=rowsof(`matrixname')' {
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local rowname: word `rowindex' of `rownames'
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di "<tr><th>" "`rowname'" "</th>"
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forvalues colptr=1/`=colsof(`matrixname')' {
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di "<td>" `precision' `matrixname'[`rowptr', `colptr'] "</td>"
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}
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di "</tr>"
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local ++rowindex
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}
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end
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57
Modules/ado/personal/m/mdreplace.ado
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57
Modules/ado/personal/m/mdreplace.ado
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*! version 1 19september2018
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*! Jean-Benoit Hardouin
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*
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************************************************************************************************************
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* Stata program : mdreplace
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* Missing data handling in a dataset
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*
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* Historic
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* Version 1 (2018-09-19): Jean-Benoit Hardouin
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*
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* Jean-benoit Hardouin, Plateforme de Méthodologie et de Biostatistique - DRCi - CHU de Nantes - France
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* jean-benoit.hardouin@univ-nantes.fr
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*
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* Copyright 2018 Jean-Benoit Hardouin
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*
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************************************************************************************************************
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program define mdreplace
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version 7.0
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syntax varlist(min=1) , list(string)
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if "`list'"=="" {
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di in red "Please indicate in the list option the strings to replace by missing data"
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}
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foreach i of varlist `varlist' {
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foreach e in `list' {
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*di "variable : `i' code : `e'"
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capture replace `i'="." if `i'=="`e'"
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if (_rc==0) {
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qui replace `i'="" if `i'=="`e'"
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}
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}
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capture destring `i', replace
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if (_rc==0) {
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qui destring `i', replace
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}
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}
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end
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210
Modules/ado/personal/m/mi_twoway.ado
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210
Modules/ado/personal/m/mi_twoway.ado
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program define mi_twoway,rclass
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syntax varlist [, SCorename(string) REPlace ADD(int 10) STyle(string) clear DA ITerate(int 10)]
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qui{
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tempfile bddini
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save `bddini', replace
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if "`scorename'"==""{
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local scorename score
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}
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capture tab `scorename'
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if _rc==0{
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if "`replace'"!=""{
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drop `scorename'
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}
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else{
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noi di in red "variable `scorename' already exists"
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use `bddini',replace
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error 100
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}
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}
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local nombase ""
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if wordcount("`style'")==0{
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local style mlong
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}
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else if wordcount("`style'")==1{
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if "`=word("`style'", 1)'"=="ml" | "`=word("`style'", 1)'"=="mlo" | "`=word("`style'", 1)'"=="mlon" | "`=word("`style'", 1)'"=="mlong" | "`=word("`style'", 1)'"=="fl" | "`=word("`style'", 1)'"=="flo" | "`=word("`style'", 1)'"=="flon" | "`=word("`style'", 1)'"=="flong" | "`=word("`style'", 1)'"=="w" | "`=word("`style'", 1)'"=="wi" | "`=word("`style'", 1)'"=="wid" | "`=word("`style'", 1)'"=="wide"{
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local style `style'
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}
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else{
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noi di in red "option {it:style} improperly fulfilled"
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noi di in red " Only {it:mlong}, {it:flong}, {it:wide} and {it:flongsep} are available"
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noi di in red " A database name must be proposed if the style {it:flongsep} is selected (see mi_styles)"
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use `bddini',replace
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error 100
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}
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}
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else if wordcount("`style'")==2{
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if "`=word("`style'", 1)'"=="flongs" | "`=word("`style'", 1)'"=="flongse" | "`=word("`style'", 1)'"=="flongsep"{
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local nombase `=word("`style'", 2)'
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local style `=word("`style'", 1)'
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}
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else{
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noi di in red "option {it:style} improperly fulfilled"
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noi di in red " Only {it:mlong}, {it:flong}, {it:wide} and {it:flongsep} are available"
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noi di in red " A database name must be proposed if the style {it:flongsep} is selected (see mi_styles)"
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use `bddini',replace
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error 100
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}
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}
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tempvar var
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local M `_dta[_mi_M]'
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if "`M'"!=""{
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if "`clear'"==""{
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noi di in red "no; data are mi set"
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noi di in red " Use {it:clear} option to replace imputed values using mi_twoway, or"
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noi di in red " other MI commands (like {it:mi extract})"
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use `bddini',replace
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error 100
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}
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else{
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mi extract 0, clear
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}
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}
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gen `var'id=_n
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sort `var'id
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save `bddini'_bis, replace
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tokenize `varlist'
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if `add'==.{
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local add==10
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}
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if `iterate'==.{
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local iterate==10
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}
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egen keep=rowmean(`varlist')
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keep if keep!=.
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drop keep
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local Nb=_N
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local nbit: word count `varlist'
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egen `var'i=rowmean(`varlist')
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forvalues j=1/`nbit'{
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gen it_`var'_`j'=``j''
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}
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keep it_`var'_* `var'id `var'i
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reshape long it_`var'_, i(`var'id) j(it)
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rename it_`var'_ it_`var'
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bysort it: egen Mj=mean(it_`var')
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su it_`var'
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local MM=r(mean)
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gen Me=`var'i+Mj-`MM'
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gen Dif2=(Me-it_`var')^2
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su Dif2
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local Var=r(sum)/(r(N)-1)
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gen __miss=it_`var'==.
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forvalues it=1/`add'{
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gen _`it'_`var'=it_`var'
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replace _`it'_`var'=rnormal(Me, `=sqrt(`Var')') if __miss==1
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}
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/* Data augmentation */
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if "`da'"!=""{
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gen `var'present=1-__miss
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bysort `var'id: egen `var'Cpre=total(`var'present)
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bysort it: egen `var'CItpre=total(`var'present)
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su `var'present
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local Nobs=r(sum)
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gen `var'Pre=_1_`var'
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su `var'Pre
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local mu=r(mean)
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bysort it: egen `var'Be=mean(`var'Pre)
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replace `var'Be=`var'Be-`mu'
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bysort `var'id: egen `var'Al=mean(`var'Pre)
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gen `var'sigInt=(`var'Pre-`var'Be-`var'Al)^2
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su `var'sigInt
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local sigma2=r(sum)/(`nbit'-1)/(`Nb'-1)
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gen `var'tauInt=(`var'Al-`mu')^2
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su `var'tauInt if it==1
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local tau2=r(sum)/(`Nb'-1)
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forvalues it=1/`add'{
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forvalues g=1/`iterate'{
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/* noi di "it = `g'"*/
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/* Redéfinition alpha */
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capture drop A2 `var'A2 `var'A2V B2 `var'Be2 Vbe2 S2 TAU
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gen A2=(it_`var'-`var'Be)/`sigma2'
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bysort `var'id: egen `var'A2=total(A2)
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replace `var'A2=(`var'A2+`mu'/`tau2')/(1/`tau2' + `var'Cpre/`sigma2')
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gen `var'A2V=sqrt(1/(1/`tau2'+`var'Cpre/`sigma2'))
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replace `var'Al=rnormal(`var'A2, `var'A2V)
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/* Redéfinition beta */
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gen B2=(it_`var'-`var'Al)
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bysort it: egen `var'Be2=mean(B2)
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gen Vbe2=sqrt(`sigma2'/`var'CItpre)
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replace `var'Be=rnormal(`var'Be2, Vbe2)
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/* Redefinition Sigma */
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gen S2=(it_`var'-`var'Al-`var'Be)^2
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su S2
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local sigma2b=`=r(N)'*`r(mean)'/rchi2(`=r(N)')
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if `sigma2b'!=0{
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local sigma2=`sigma2b'
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}
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/* Redefinition mu */
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su `var'Al if it==1
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local mu=rnormal(`=r(mean)', `=sqrt(`tau2'/r(N))')
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/* Redefinition tau2 */
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gen TAU=(`var'Al-`mu')^2
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su TAU if it==1
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local tau2b=`=r(N)'*`r(mean)'/rchi2(`=r(N)')
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/* 1/(rgamma(`=r(N)/2', `=2/r(N)/r(mean)')) OU `=r(N)'*`r(mean)'/rchi2(`=r(N)')*/
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if `tau2b'!=0{
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local tau2=`tau2b'
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}
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/*noi di "mu: `mu' tau2: `tau2' sigma2: `sigma2'"
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noi su `var'Al `var'Be*/
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}
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replace _`it'_`var'=rnormal(`mu',`=sqrt(`tau2')')+`var'Be+rnormal(0,`=sqrt(`sigma2')') if __miss==1
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}
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}
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/* Fin de data augmentation */
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bysort `var'id: egen _mi_miss=max(__miss)
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drop Mj Me Dif2 `var'i __miss
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keep *_`var' `var'id it _mi_miss
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reshape wide *_`var', i(`var'id) j(it)
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egen `scorename'=rowtotal(it_`var'*)
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replace `scorename'=. if _mi_miss==1
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forvalues j=1/`add'{
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egen _`j'_`scorename'=rowtotal(_`j'_*), missing
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}
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forvalues i=1/`nbit'{
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rename it_`var'`i' ``i''
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forvalues j=1/`add'{
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rename _`j'_`var'`i' _`j'_``i''
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label variable _`j'_``i'' ""
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}
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}
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drop `varlist'
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order `scorename' _mi_miss, first
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sort `var'id
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save `bddini'_ter, replace
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use `bddini'_bis
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merge `var'id using `bddini'_ter
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drop _merge `var'id
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char _dta[_mi_pvars] `scorename'
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char _dta[_mi_M] `add'
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char _dta[_mi_ivars] `varlist'
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char _dta[_mi_style] wide
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char _dta[_mi_marker] _mi_ds_1
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char _dta[_miTW] TW
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mi convert `style' `nombase', clear
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}
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end
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97
Modules/ado/personal/m/mi_twoway.hlp
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97
Modules/ado/personal/m/mi_twoway.hlp
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@ -0,0 +1,97 @@
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{smcl}
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{hline}
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{hline}
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help for {hi:mi_twoway}{right:Jean-François HAMEL}
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{hline}
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{title:Two-way imputations: estimating missing item responses in questionnaires for computing scores}
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{p 8 14 2}{cmd:mi_twoway} {it:varlist}, [{cmdab:sc:orename}({it:newvarname})
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{cmdab:rep:lace}
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{cmdab:add:}({it:#})
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{cmdab:st:yle}({it:keyword})
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{cmdab:clear}
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{cmdab:da}
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{cmdab:it:erate}({it:#})]
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{title:Description}
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{p 8 14 2}{cmd:mi_twoway} is an implementation of multiple imputation methods for tests and questionnaires
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based on a two-way ANOVA model of persons by items.{p_end}
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{p 14 14 2}Two methods are available: imputations based on a fixed effects two-way ANOVA, and imputations
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generated using data augmentation based on a mixed effect two-way ANOVA (with a random person effect
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assumed to follow a Norma distribution and a fixed item effect.{p_end}
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{p 14 14 2}{cmd:mi_twoway} is compatible with the Sata {help mi} procedures. The data is {help mi_set:set}
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and {help mi_impute:imputed} using {cmd:mi_twoway}, but all the estimations using multiple imputations are
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performed using the standard mi {help mi_estimate:estimate} procedures.
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{p 4 8 2}{it:varlist} is the list of the variables containing the item responses of the questionnaire
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(with possible missing data).
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{title:Options}
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||||
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{p 4 14 2}{cmd:scorename} specifies the name of the new variable containing, for each individual, the value of the
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score computed as the sum of the item responses. {it:scorename} is missing for each individual with at least one item
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||||
missing response.
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||||
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{p 4 14 2}{cmd:replace} allows to replace individual scores in existing variables
|
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||||
{p 4 14 2}{cmd:add} specifies the number of imputations to add; required with no imputations
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||||
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||||
{p 4 14 2}{cmd:style} specifies in which style should be recorded the data: wide, mlong, flong,
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or flongsep; see {help mi_styles:[MI] styles}.
|
||||
|
||||
{p 4 14 2}{cmd:clear} allows performing new imputations by remouving the previous one.
|
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|
||||
{p 4 14 2}{cmd:da} generates imputations using data augmentation based on a mixed effect two-way
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ANOVA (with a random person effect and a fixed item effect. By default, imputations are generated
|
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using a fixed effects two-way ANOVA.
|
||||
|
||||
{p 4 14 2}{cmd:iterate} defines the number of iterations of the data augmentation algorithm.
|
||||
By default, this number is fixed to 10.
|
||||
|
||||
{marker example}{...}
|
||||
{title:Example}
|
||||
|
||||
{pstd}
|
||||
Simulation of the data (using {help simirt}):
|
||||
|
||||
{cmd:. simirt, nbobs(200) dim(5) group(0.5) deltagroup(0.4) clear}{right:(1) }
|
||||
|
||||
{pstd}
|
||||
Creating the missing data, with a non-response rate of 10%:
|
||||
|
||||
. {cmd:set more off }{right:(2) }
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||||
. {cmd:forvalues i=1/200{c -(} }{right:(3) }
|
||||
2. {cmd:forvalues j=1/5{c -(}}{right:(4) }
|
||||
3. {cmd:replace item`j'=. if runiform()<0.1 & id==`i'}{right:(5) }
|
||||
4. {cmd:{c )-}}{right:(6) }
|
||||
5. {cmd:{c )-}}{right:(7) }
|
||||
|
||||
{pstd}
|
||||
Generating 10 multiple imputations using a fixed effects two-way ANOVA:
|
||||
|
||||
. {cmd:mi_twoway item*, scorename(score) add(10) style(wide)}{right:(8) }
|
||||
|
||||
{pstd}
|
||||
Modeling score depending on {it:group} covariate using multiple imputations estimates ({help mi_estimate}):
|
||||
|
||||
. {cmd:mi estimate: regress score i.group}{right:(9) }
|
||||
|
||||
{pstd}
|
||||
Changing the way to impute data, using data augmentation based on a mixed effect two-way ANOVA:
|
||||
|
||||
. {cmd:mi_twoway item*, scorename(score) replace add(10) style(wide) da clear}{right:(10) }
|
||||
|
||||
{pstd}
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||||
Changing the style of the data from {it:wide} to {it:mlong} ({help mi_convert}):
|
||||
|
||||
. {cmd:mi convert mlong}{right:(11) }
|
||||
|
||||
{pstd}
|
||||
Removal of the multiple imputations ({help mi_set##unset:mi_unset}):
|
||||
|
||||
. {cmd:mi extract 0, clear}{right:(12) }
|
||||
|
||||
|
||||
320
Modules/ado/personal/m/mmsrm.ado
Normal file
320
Modules/ado/personal/m/mmsrm.ado
Normal file
@ -0,0 +1,320 @@
|
||||
*! Version 4 7December2012
|
||||
************************************************************************************************************
|
||||
* Stata program : mmsrm
|
||||
* Estimate the parameters of the Multidimensional Marginally Sufficient Rasch Model (MMSRM)
|
||||
* Version 4 : December 7, 2012 /* id option*/
|
||||
*
|
||||
* Historic :
|
||||
* Version 1 (May 14, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 2 (May 26, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 3 (July 3, 2005) [Jean-Benoit Hardouin]
|
||||
* Version 3.1 : July 8, 2010 /* correction of a bug for the name of the items */
|
||||
*
|
||||
* Jean-benoit Hardouin, phD, Assistant Professor
|
||||
* Team of Biostatistics, Pharmacoepidemiology and Subjective Measures in Health Sciences (UPRES EA 4275 SPHERE)
|
||||
* University of Nantes - Faculty of Pharmaceutical Sciences
|
||||
* France
|
||||
* jean-benoit.hardouin@anaqol.org
|
||||
*
|
||||
* Use the Stata programs raschtest and gammasym who can be download on http://anaqol.free.fr
|
||||
* Use the Stata program gllamm who can be obtained by : ssc install gllamm
|
||||
* News about this program :http://anaqol.free.fr
|
||||
*
|
||||
* Copyright 2004-2005, 2010, 2012 Jean-Benoit Hardouin
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation; either version 2 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||||
*
|
||||
************************************************************************************************************
|
||||
|
||||
|
||||
program define mmsrm,eclass
|
||||
version 8.0
|
||||
syntax varlist(min=3 numeric) [if] [in] , id(varname) [PARTition(numlist) NODETails TRAce ITerate(int 30) ADapt METHod(string)]
|
||||
preserve
|
||||
tempfile mmsrmfile
|
||||
qui save `mmsrmfile',replace
|
||||
|
||||
/*******************************************************************************
|
||||
INTRODUCTION AND TESTS
|
||||
********************************************************************************/
|
||||
|
||||
marksample touse
|
||||
qui keep if `touse'
|
||||
|
||||
local nbitems : word count `varlist'
|
||||
if "`partition'"=="" {
|
||||
local partition=`nbitems'
|
||||
}
|
||||
if "`method'"=="" {
|
||||
local method mml
|
||||
}
|
||||
local method=lower("`method'")
|
||||
local nbpart:word count `partition'
|
||||
|
||||
if `nbpart'>3 {
|
||||
di in red "{p}The mmsrm module cannot estimate the parameters of the models with more than three dimensions.{p_end}"
|
||||
error 198
|
||||
exit
|
||||
}
|
||||
else if `nbpart'==3&"`method'"=="gee" {
|
||||
di in red "{p}You cannot estimate the parameters of a MMSRM with 3 dimension and the GEE method.{p_end}"
|
||||
error 198
|
||||
exit
|
||||
}
|
||||
|
||||
if "`adapt'"!=""&"`method'"!="mml" {
|
||||
di in green "{p}the {cmd:adapt} option is ignored with GEE.{p_end}"
|
||||
}
|
||||
|
||||
local comptitems=0
|
||||
tokenize `varlist'
|
||||
forvalues i=1/`nbpart' {
|
||||
local firstpart`i'=`comptitems'+1
|
||||
local part`i': word `i' of `partition'
|
||||
local set`i'
|
||||
local comptitems=`comptitems'+`part`i''
|
||||
forvalues j=`firstpart`i''/`comptitems' {
|
||||
local set`i' "`set`i'' ``j''"
|
||||
}
|
||||
}
|
||||
|
||||
if `comptitems'<`nbitems' {
|
||||
di in error "{p}Your {cmd:partition} describes less items than the number of items defined in the {it:varlist}.{p_end}"
|
||||
error 198
|
||||
exit
|
||||
}
|
||||
if `comptitems'>`nbitems' {
|
||||
di in error "{p}Your {cmd:partition} describes more items than the number of items defined in the {it:varlist}.{p_end}"
|
||||
error 198
|
||||
exit
|
||||
}
|
||||
|
||||
/*******************************************************************************
|
||||
FORMATING AND ESTIMATION (with MML)
|
||||
********************************************************************************/
|
||||
if `nbpart'== 1 {
|
||||
raschtestv7 `varlist', test(none) method(`method') id(`id')
|
||||
local ll=r(ll)
|
||||
tempname beta1 Varbeta1 M
|
||||
matrix `beta1'=r(beta)
|
||||
matrix `Varbeta1'=r(Varbeta)
|
||||
local sigma1=r(sigma)
|
||||
matrix `M'=(`sigma1'^2)
|
||||
}
|
||||
else if "`method'"=="mml" {
|
||||
forvalues i=1/`nbpart' {
|
||||
if "`details'"=="" {
|
||||
di in green "{p}Estimation of the difficulty parameters of the dimension `i'.{p_end}"
|
||||
}
|
||||
*set trace on
|
||||
if `part`i''>1 {
|
||||
qui raschtestv7 `set`i'',meth(`method') test(none) id(`id')
|
||||
tempname beta`i' Varbeta`i'
|
||||
matrix `beta`i''=r(beta)
|
||||
matrix `Varbeta`i''=r(Varbeta)
|
||||
local sigma`i'=r(sigma)
|
||||
forvalues j=1/`part`i'' {
|
||||
local parambeta`=`firstpart`i''+`j'-1'=`beta`i''[1,`j']
|
||||
}
|
||||
}
|
||||
else {
|
||||
qui count
|
||||
local N=r(N)
|
||||
qui count if ``firstpart`i'''==1
|
||||
local pos=r(N)
|
||||
local parambeta`firstpart`i''=-log(`pos'/(`N'-`pos'))
|
||||
local sigma`i'=0
|
||||
}
|
||||
}
|
||||
|
||||
if "`details'"=="" {
|
||||
di
|
||||
di in green "{p}Estimation of the parameters of the distribution of the multidimensional latent trait.{p_end}"
|
||||
di in green "{p}This process could be long to run. Be patient !{p_end}"
|
||||
}
|
||||
|
||||
keep `varlist'
|
||||
|
||||
tempname rep id item offset
|
||||
forvalues i=1/`nbitems' {
|
||||
rename ``i'' `rep'`i'
|
||||
}
|
||||
|
||||
gen `id'=_n
|
||||
qui reshape long `rep', i(`id') j(`item')
|
||||
|
||||
gen `offset'=0
|
||||
label variable `offset' "offset"
|
||||
|
||||
forvalues i=1/`nbitems' {
|
||||
qui replace `offset'=-`parambeta`i'' if `item'==`i'
|
||||
}
|
||||
|
||||
local eqs
|
||||
forvalues i=1/`nbpart' {
|
||||
tempname B`i'
|
||||
gen `B`i''=0
|
||||
eq sc`i':`B`i''
|
||||
local eqs `eqs' sc`i'
|
||||
forvalues j=`firstpart`i''/`=`firstpart`i''+`part`i''-1' {
|
||||
qui replace `B`i''=1 if `item'==`j'
|
||||
}
|
||||
}
|
||||
|
||||
label variable `rep' "response"
|
||||
label variable `id' "identifiant"
|
||||
|
||||
tempname first
|
||||
local four=substr("`id'",1,3)
|
||||
matrix define `first'=(0,`sigma1',0,`sigma2')
|
||||
matrix colnames `first'=`rep':_cons `four'1_1:`B1' `four'1_2:`B2' `four'1_2_1:_cons
|
||||
|
||||
if "`trace'"!="" {
|
||||
local quigllamm
|
||||
}
|
||||
else {
|
||||
local quigllamm qui
|
||||
}
|
||||
|
||||
`quigllamm' gllamm `rep', from(`first') link(logit) fam(bin) i(`id') offset(`offset') nrf(`nbpart') eqs(`eqs') nip(6) dots `trace' `adapt' iterate(`iterate')
|
||||
local ll=e(ll)
|
||||
tempname cosig varsig L M
|
||||
matrix `cosig'=e(b)
|
||||
matrix `varsig'=e(V)
|
||||
matrix `L'=e(chol)
|
||||
matrix `M'=`L'*`L''
|
||||
}
|
||||
/*******************************************************************************
|
||||
FORMATING AND ESTIMATION (with GEE)
|
||||
********************************************************************************/
|
||||
else if "`method'"=="gee" {
|
||||
tempname coef
|
||||
matrix `coef'=J(`nbitems',`nbpart',0)
|
||||
forvalues i=1/`nbpart' {
|
||||
forvalues j=`firstpart`i''/`=`firstpart`i''+`part`i''-1' {
|
||||
matrix `coef'[`j',`i']=1
|
||||
}
|
||||
}
|
||||
if "`trace'"!="" {
|
||||
local quigee
|
||||
}
|
||||
else {
|
||||
local quigee quietly
|
||||
}
|
||||
`quigee' geekel2d `varlist',coef(`coef') ll nbit(`iterate')
|
||||
local ll=r(ll)
|
||||
tempname cosig varsig M
|
||||
matrix `cosig'=r(b)
|
||||
matrix `M'=J(2,2,0)
|
||||
matrix `M'[1,1]=`cosig'[1,`=`nbitems'+1']
|
||||
matrix `M'[2,2]=`cosig'[1,`=`nbitems'+2']
|
||||
matrix `M'[1,2]=`cosig'[1,`=`nbitems'+3']
|
||||
matrix `M'[2,1]=`cosig'[1,`=`nbitems'+3']
|
||||
matrix `cosig'=`cosig'[1,1..`nbitems']
|
||||
matrix `varsig'=r(V)
|
||||
matrix `varsig'=`varsig'[1..`nbitems',1..`nbitems']
|
||||
forvalues i=1/`nbpart' {
|
||||
tempname beta`i' Varbeta`i'
|
||||
matrix `beta`i''=`cosig'[1,`firstpart`i''..`=`firstpart`i''+`part`i''-1']
|
||||
matrix `Varbeta`i''=`varsig'[`firstpart`i''..`=`firstpart`i''+`part`i''-1',`firstpart`i''..`=`firstpart`i''+`part`i''-1']
|
||||
if `part`i''==1 {
|
||||
local parambeta`firstpart`i''=`cosig'[1,`firstpart`i'']
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/*******************************************************************************
|
||||
DISPLAYING OF THE RESULTS
|
||||
********************************************************************************/
|
||||
|
||||
local AIC=-2*`ll'+2*(`nbitems'+`nbpart'*(`nbpart'+1)/2)
|
||||
|
||||
if `nbpart'>1 {
|
||||
forvalues i=1/`nbpart' {
|
||||
local var`i'=`M'[`i',`i']
|
||||
forvalues j=`=`i'+1'/`nbpart' {
|
||||
local cov`i'`j'=`M'[`i',`j']
|
||||
}
|
||||
}
|
||||
di
|
||||
di in green _col(4) "Log-likelihood:" in yellow %-12.4f `ll'
|
||||
di
|
||||
noi di in green _col(4) "Items" _col(16) "Parameters" _col(29) "std Err."
|
||||
di in green _col(4) "{hline 33}"
|
||||
forvalues p=1/`nbpart' {
|
||||
forvalues i=1/`part`p'' {
|
||||
local name:word `i' of `set`p''
|
||||
if `part`p''!=1 {
|
||||
noi di in yellow _col(4) "`name'" _col(18) %8.5f `beta`p''[1,`i'] _col(30) %6.5f sqrt(`Varbeta`p''[`i',`i'])
|
||||
}
|
||||
else {
|
||||
noi di in yellow _col(4) "`name'" _col(18) %8.5f `parambeta`firstpart`p''' _col(30) "."
|
||||
}
|
||||
}
|
||||
}
|
||||
di in green _col(4) "{hline 33}"
|
||||
|
||||
forvalues i=1/`nbpart' {
|
||||
noi di in yellow _col(4) "Var`i'" _col(18) %8.5f `var`i''
|
||||
}
|
||||
|
||||
forvalues i=1/`nbpart' {
|
||||
forvalues j=`=`i'+1'/`nbpart' {
|
||||
di in green _col(4) in yellow "cov`i'`j'" _col(18) %8.5f `cov`i'`j''
|
||||
}
|
||||
}
|
||||
di in green _col(4) "{hline 33}"
|
||||
}
|
||||
if "`trace'"==""&"`details'"=="" {
|
||||
di in green "{p}Add the -trace- option to obtain the standard errors of the elements of the covariance matrix of the latent traits.{p_end}"
|
||||
}
|
||||
|
||||
/*******************************************************************************
|
||||
OUTPUTS
|
||||
********************************************************************************/
|
||||
|
||||
ereturn clear
|
||||
ereturn scalar AIC=`AIC'
|
||||
ereturn scalar ll=`ll'
|
||||
ereturn scalar dimension=`nbpart'
|
||||
forvalues i=1/`nbpart' {
|
||||
ereturn scalar nbitems`i'=`part`i''
|
||||
ereturn local set`i' `set`i''
|
||||
if `part`i''>1 {
|
||||
matrix colnames `beta`i''=`set`i''
|
||||
matrix rownames `beta`i''=beta
|
||||
ereturn matrix beta`i'=`beta`i''
|
||||
matrix colnames `Varbeta`i''=`set`i''
|
||||
matrix rownames `Varbeta`i''=`set`i''
|
||||
ereturn matrix Varbeta`i' `Varbeta`i''
|
||||
}
|
||||
else {
|
||||
ereturn scalar beta`i'=`parambeta`firstpart`i'''
|
||||
}
|
||||
}
|
||||
|
||||
tempname matrixsigma
|
||||
matrix `matrixsigma'=`M'
|
||||
local list
|
||||
forvalues i=1/`nbpart' {
|
||||
local list `list' latenttrait`i'
|
||||
}
|
||||
matrix colnames `matrixsigma'=`list'
|
||||
matrix rownames `matrixsigma'=`list'
|
||||
ereturn matrix covar=`matrixsigma'
|
||||
|
||||
drop _all
|
||||
qui use `mmsrmfile'
|
||||
end
|
||||
72
Modules/ado/personal/m/mmsrm.hlp
Normal file
72
Modules/ado/personal/m/mmsrm.hlp
Normal file
@ -0,0 +1,72 @@
|
||||
{smcl}
|
||||
{* 5may2013}{...}
|
||||
{hline}
|
||||
help for {hi:mmsrm}
|
||||
{hline}
|
||||
|
||||
{title:Estimation of the parameters of a Multidimensional Marginally Sufficient Rasch Model (MMSRM)}
|
||||
|
||||
{p 8 14 2}{cmd:mmsrm} {it:varlist} {cmd:id}({it:varname}) [{cmd:,} {cmdab:part:ition}({it:numlist}) {cmdab:nodet:ails} {cmdab:trac:e} {cmdab:it:erate}({it:#}) {cmdab:ad:apt} {cmdab:meth:od}({it:mml/gee})]
|
||||
|
||||
{p 8 14 2}{it:varlist} is a list of two existing binary variables or more.
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 8 2}{cmd:mmsrm} estimates by marginal maximum likelihood (MML) or
|
||||
generalized estimating equations (GEE) the parameters of the Multidimensional
|
||||
Marginally Sufficient Rasch Model (MMSRM) defined by Hardouin and Mesbah (2004).
|
||||
This model is an Item Response Model (IRM) with one or several latent traits.
|
||||
This is a particular multidimensionnal extension of the Rasch model. In this
|
||||
model, the items are separated in Q groups and each group of items is linked to
|
||||
one and only one latent trait. Each group fits a Rasch model relatively to the
|
||||
corresponding latent trait, so the score computed in each group of item is a
|
||||
sufficient statistics of this latent trait (to a specific value of this score
|
||||
is associated only one value for the latent trait). The program allows
|
||||
computing the parameter of a MMSRM with less than 4 latent traits.
|
||||
To improve the time of computing, the difficulty parameters are estimated in
|
||||
each unidimensional Rasch model and used as an offset variable to estimate the
|
||||
parameters of the distribution of the multidimensional latent trait. This model
|
||||
allows estimating the correlations between different latent traits measured by
|
||||
Rasch models.
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 8 2}{cmd:id} defines an identifiant variable of the individuals.
|
||||
|
||||
{p 4 8 2}{cmd:partition} allows defining the number of items relied to each
|
||||
latent trait. The sum of the numbers indicated in the {it:numlist} must be
|
||||
equal to the total number of items. The number of elements indicates the number of
|
||||
latent traits. The items are taken in the same order than this one of {it:varlist}.
|
||||
By default, only one latent trait is assumed (Rasch model). The number of elements of
|
||||
{cmd:partition} must be inferior or equal to 3.
|
||||
|
||||
{p 4 8 2}{cmd:method} defines the estimation method between the marginal maximum
|
||||
likelihood ({it:mml} - by default) and the generalized estimating equations ({it:gee}).
|
||||
You cannot estimate the parameters of a MMSRM with 3 dimensions with the GEE method.
|
||||
|
||||
{p 4 8 2}{cmd:nodetails} deletes the details about the procedure.
|
||||
|
||||
{p 4 8 2}{cmd:trace} displays details about the estimation algorithm.
|
||||
|
||||
{p 4 8 2}{cmd:iterate} defines the maximal number of iterations of the estimation algorithm
|
||||
(30 by default).
|
||||
|
||||
{p 4 8 2}{cmd:adapt} allows using the adaptive quadrature if you use the MML method of estimation. This option
|
||||
is useless if you use GEE.
|
||||
|
||||
{title:Example}
|
||||
|
||||
{p 16 22 2} {inp:. mmsrm item1-item9 , part(4 5) trace method(gee)}
|
||||
|
||||
{p 16 22 2} {inp:. mmsrm item1 item2 item3 item4 /*Rasch model*/}
|
||||
|
||||
{p 16 22 2} {inp:. mmsrm c1-c9 , part(3 3 3) nodetails adapt iterate(10)}
|
||||
|
||||
{title:References}
|
||||
|
||||
{p 4 8 2}Hardouin J.-B. and Mesbah M. Clustering binary variables in subscales using an extended Rasch model and Akaike Information Criterion. {it:Communications in Statistics - Theory and Methods}, {cmd:33}(6), pp. 1277-1294, (2004).
|
||||
|
||||
{title:Author}
|
||||
|
||||
{p 4 8 2}Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France.
|
||||
You can contact the author at {browse "mailto:jean-benoit.hardouin@neuf.fr":jean-benoit.hardouin@neuf.fr} and visit the websites {browse "http://anaqol.free.fr":AnaQol} and {browse "http://freeirt.free.fr":FreeIRT}
|
||||
241
Modules/ado/personal/m/mmsrm2.ado
Normal file
241
Modules/ado/personal/m/mmsrm2.ado
Normal file
@ -0,0 +1,241 @@
|
||||
************************************************************************************************************
|
||||
* Stata program : mmsrm
|
||||
* Estimate the parameters of the Multidimensional Marginally Sufficient Rasch Model (MMSRM)
|
||||
* Release 2 : May 26, 2004
|
||||
*
|
||||
* Historic :
|
||||
* Version 1 (May 14, 2004) [Jean-Benoit Hardouin]
|
||||
*
|
||||
* Jean-benoit Hardouin, Regional Health Observatory of Orl<72>ans - France
|
||||
* jean-benoit.hardouin@neuf.fr
|
||||
*
|
||||
* Use the Stata programs raschtest and gammasym who can be download on http://anaqol.free.fr
|
||||
* Use the Stata program gllamm who can be obtained by : ssc install gllamm
|
||||
* News about this program :http://anaqol.free.fr
|
||||
*
|
||||
* All the necessary programs on the FreeIRT Project website : http://freeirt.free.fr
|
||||
*
|
||||
* Copyright 2004 Jean-Benoit Hardouin
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation; either version 2 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||||
*
|
||||
************************************************************************************************************
|
||||
|
||||
|
||||
program define mmsrm,eclass
|
||||
version 8.0
|
||||
syntax varlist(min=3 numeric) [, PARTition(numlist) NODETails TRAce]
|
||||
preserve
|
||||
|
||||
local nbitems : word count `varlist'
|
||||
if "`part'"=="" {
|
||||
local part=`nbitems'
|
||||
}
|
||||
local nbpart:word count `partition'
|
||||
|
||||
if `nbpart'== 1 {
|
||||
raschtest `varlist', notest mml
|
||||
}
|
||||
else if `nbpart'>2 {
|
||||
di in red "The mmsrm module do not run models with more than two dimensions for the moment."
|
||||
}
|
||||
if `nbpart'!=2 {
|
||||
exit
|
||||
}
|
||||
|
||||
local comptitems=0
|
||||
tokenize `varlist'
|
||||
forvalues i=1/`nbpart' {
|
||||
local firstpart`i'=`comptitems'+1
|
||||
local part`i': word `i' of `partition'
|
||||
local set`i'
|
||||
local comptitems=`comptitems'+`part`i''
|
||||
forvalues j=`firstpart`i''/`comptitems' {
|
||||
local set`i' "`set`i'' ``j''"
|
||||
}
|
||||
}
|
||||
|
||||
if `comptitems'<`nbitems' {
|
||||
di in error "Your partition describe less items than the number of items defined in the varlist."
|
||||
di in error "Correct your code."
|
||||
exit
|
||||
}
|
||||
if `comptitems'>`nbitems' {
|
||||
di in error "Your partition describe more items than the number of items defined in the varlist."
|
||||
di in error "Correct your code."
|
||||
exit
|
||||
}
|
||||
|
||||
|
||||
forvalues i=1/`nbpart' {
|
||||
if "`details'"=="" {
|
||||
di in green "Estimation of the difficulty parameters of the dimension `i'."
|
||||
}
|
||||
*set trace on
|
||||
if `part`i''>1 {
|
||||
qui raschtest `set`i'',mml notest
|
||||
tempname beta`i' Varbeta`i'
|
||||
matrix `beta`i''=e(beta)
|
||||
matrix `Varbeta`i''=e(Varbeta)
|
||||
local sigma`i'=e(sigma)
|
||||
forvalues j=1/`part`i'' {
|
||||
local parambeta`=`firstpart`i''+`j'-1'=`beta`i''[1,`j']
|
||||
}
|
||||
}
|
||||
else {
|
||||
qui count
|
||||
local N=r(N)
|
||||
qui count if ``firstpart`i'''==1
|
||||
local pos=r(N)
|
||||
local parambeta`firstpart`i''=-log(`pos'/(`N'-`pos'))
|
||||
local sigma`i'=0
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if "`details'"=="" {
|
||||
di
|
||||
di in green "Estimation of the parameters of the distribution of the multidimensional latent trait."
|
||||
di in green "This process could be long to run. Be patient !"
|
||||
}
|
||||
|
||||
*set trace on
|
||||
tempfile savemmsrm
|
||||
qui save `savemmsrm'
|
||||
keep `varlist'
|
||||
|
||||
tempname rep id item offset
|
||||
forvalues i=1/`nbitems' {
|
||||
rename ``i'' `rep'`i'
|
||||
}
|
||||
|
||||
gen `id'=_n
|
||||
qui reshape long `rep', i(`id') j(`item')
|
||||
|
||||
gen `offset'=0
|
||||
label variable `offset' "offset"
|
||||
|
||||
forvalues i=1/`nbitems' {
|
||||
qui replace `offset'=-`parambeta`i'' if `item'==`i'
|
||||
}
|
||||
|
||||
local eqs
|
||||
forvalues i=1/`nbpart' {
|
||||
tempname B`i'
|
||||
gen `B`i''=0
|
||||
eq sc`i':`B`i''
|
||||
local eqs `eqs' sc`i'
|
||||
forvalues j=`firstpart`i''/`=`firstpart`i''+`part`i''-1' {
|
||||
qui replace `B`i''=1 if `item'==`j'
|
||||
}
|
||||
}
|
||||
|
||||
label variable `rep' "response"
|
||||
label variable `id' "identifiant"
|
||||
|
||||
tempname first
|
||||
local four=substr("`id'",1,3)
|
||||
matrix define `first'=(0,`sigma1',`sigma2',0)
|
||||
matrix colnames `first'=`rep':_cons `four'1_1:`B1' `four'1_2:`B2' `four'1_2_1:_cons
|
||||
|
||||
if "`trace'"!="" {
|
||||
local quigllamm
|
||||
}
|
||||
else {
|
||||
local quigllamm qui
|
||||
}
|
||||
|
||||
`quigllamm' gllamm `rep', from(`first') link(logit) fam(bin) i(`id') offset(`offset') nrf(`nbpart') eqs(`eqs') nip(5) dots `trace'
|
||||
local AIC=-2*e(ll)+2*(`nbitems'+`nbpart'*(`nbpart'+1)/2)
|
||||
local ll=e(ll)
|
||||
tempname cosig varsig L M
|
||||
matrix `cosig'=e(b)
|
||||
matrix `varsig'=e(V)
|
||||
matrix `L'=e(chol)
|
||||
matrix `M'=`L'*`L''
|
||||
|
||||
forvalues i=1/`nbpart'{
|
||||
}
|
||||
|
||||
if `nbpart'==2 {
|
||||
local var1=`M'[1,1]
|
||||
local var2=`M'[2,2]
|
||||
local sevar1=(sqrt(`varsig'[2,2])*2*sqrt(`var1'))
|
||||
local sevar2=.
|
||||
|
||||
local cov12=`M'[1,2]
|
||||
local secov=.
|
||||
|
||||
di
|
||||
di in green _col(4) "Log-likelihood:" in yellow %-12.4f `ll'
|
||||
di
|
||||
noi di in green _col(4) "Items" _col(16) "Parameters" _col(29) "std Err."
|
||||
di in green _col(4) "{hline 33}"
|
||||
forvalues i=1/`part1' {
|
||||
if `part1'!=1 {
|
||||
noi di in yellow _col(4) "``i''" _col(18) %8.5f `beta1'[1,`i'] _col(30) %6.5f sqrt(`Varbeta1'[`i',`i'])
|
||||
}
|
||||
else {
|
||||
noi di in yellow _col(4) "``i''" _col(18) %8.5f `parambeta`firstpart1'' _col(30) "."
|
||||
}
|
||||
}
|
||||
di in green _col(4) "{hline 33}"
|
||||
forvalues i=`firstpart2'/`=`part1'+`part2'' {
|
||||
if `part2'!=1 {
|
||||
noi di in yellow _col(4) "``i''" _col(18) %8.5f `beta2'[1,`=`i'-`part1''] _col(30) %6.5f sqrt(`Varbeta2'[`=`i'-`part1'',`=`i'-`part1''])
|
||||
}
|
||||
else {
|
||||
noi di in yellow _col(4) "``i''" _col(18) %8.5f `parambeta`firstpart2'' _col(30) "."
|
||||
}
|
||||
}
|
||||
di in green _col(4) "{hline 33}"
|
||||
forvalues i=1/`nbpart' {
|
||||
noi di in yellow _col(4) "Var`i'" _col(18) %8.5f `var`i'' /*_col(30) %6.5f `sevar`i''*/
|
||||
}
|
||||
di in green _col(4) in yellow "cov12" _col(18) %8.5f `cov12' /*_col(30) %6.5f `secov'*/
|
||||
di in green _col(4) "{hline 33}"
|
||||
}
|
||||
if "`trace'"=="" {
|
||||
di in green "Add the trace option to obtain the standard errors of the elements"
|
||||
di in green "of the covariance matrix of the latent traits"
|
||||
}
|
||||
|
||||
ereturn clear
|
||||
ereturn scalar AIC=`AIC'
|
||||
ereturn scalar ll=`ll'
|
||||
ereturn scalar dimension=`nbpart'
|
||||
forvalues i=1/`nbpart' {
|
||||
ereturn scalar nbitems`i'=`part`i''
|
||||
ereturn local set`i' `set`i''
|
||||
if `part`i''>1 {
|
||||
ereturn matrix beta`i'=`beta`i''
|
||||
ereturn matrix Varbeta`i' `Varbeta`i''
|
||||
}
|
||||
else {
|
||||
ereturn scalar beta`i'=`parambeta`firstpart`i'''
|
||||
}
|
||||
}
|
||||
|
||||
*ereturn matrix cosig=`cosig'
|
||||
*ereturn matrix varsig=`varsig'
|
||||
|
||||
tempname matrixsigma
|
||||
matrix `matrixsigma'=(`sigma1',`sigma2',`cov12')
|
||||
matrix colnames `matrixsigma'= sigma1 sigma2 cov12
|
||||
ereturn matrix sigma=`matrixsigma'
|
||||
|
||||
drop _all
|
||||
qui use `savemmsrm'
|
||||
end
|
||||
454
Modules/ado/personal/m/msp.ado
Normal file
454
Modules/ado/personal/m/msp.ado
Normal file
@ -0,0 +1,454 @@
|
||||
*! Version 8.1.1 26 September 2013
|
||||
*! Jean-Benoit Hardouin
|
||||
************************************************************************************************************
|
||||
* Stata program : msp
|
||||
* Mokken Scale Procedure
|
||||
* Release 8.1.1: (September 26, 2013) [Jean-Benoit Hardouin] /*correction of a rare bug */
|
||||
*
|
||||
* Historic :
|
||||
* Version 1 - lose version (August 20, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 2 (September 25, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 3 (December 1, 2003) [Jean-Benoit Hardouin]
|
||||
* Version 4 (January 20, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5 (March 22, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5.1 (May 1st, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6 : (July 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.1 : (September 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.2 : (January 22, 2006) [Jean-Benoit Hardouin] /*English improvements*/
|
||||
* Release 6.3 : (March 20, 2006) [Jean-Benoit Hardouin] /*A bug with temporary files */
|
||||
* Release 6.6: (Februar 16, 2007) [Jean-Benoit Hardouin] /*Tests of the loevinger H indices, adaptation for loevH 6.6, noadjust option, improvements*/
|
||||
* Release 8: (December 8, 2010) [Jean-Benoit Hardouin] /*Adaptation for loevh version 8*/
|
||||
* Release 8.1: (December 19, 2012) [Jean-Benoit Hardouin] /*correction of a bug with the notest option*/
|
||||
* Release 8.1.1: (September 26, 2013) [Jean-Benoit Hardouin] /*correction of a rare bug */
|
||||
*
|
||||
* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences
|
||||
* Department of Biostatistics - France
|
||||
* jean-benoit.hardouin@anaqol.org
|
||||
*
|
||||
* The Stata program loevh is needed. It can be downloaded on http://www.anaqol.org
|
||||
* News about this program :http://www.anaqol.org
|
||||
* FreeIRT Project website : http://www.freeirt.org
|
||||
*
|
||||
* Copyright 2002-2007, 2010, 2012 Jean-Benoit Hardouin
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation; either version 2 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||||
*
|
||||
************************************************************************************************************
|
||||
|
||||
program define msp , rclass
|
||||
version 7.0
|
||||
syntax varlist(min=2 numeric) [,c(real 0.3) noDETails KERnel(integer 0) noTEst p(real 0.05) PAIRWise MINValue(real 0) noBon noADJust]
|
||||
local nbitems : word count `varlist'
|
||||
tokenize `varlist'
|
||||
tempfile mspfile
|
||||
qui save "`mspfile'"
|
||||
|
||||
if "`pairwise'"=="" {
|
||||
forvalues j=1/`nbitems' {
|
||||
qui drop if ``j''==.
|
||||
}
|
||||
}
|
||||
if "`test'"!="" {
|
||||
local p=1
|
||||
local minvalue=-99
|
||||
local bon nobon
|
||||
}
|
||||
|
||||
|
||||
|
||||
qui loevh `varlist',`pairwise' pair `adjust'
|
||||
tempname pvalHjk loevHjk loevHjks loevHj loevH Hjk stopscale stop dim plusitems Hjkmax Hjmax Hmax nbitemsel
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHjks'=r(loevHjk)
|
||||
matrix `pvalHjk'=r(pvalHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
matrix `Hjk'=`loevHjk'
|
||||
|
||||
matrix define `dim'=J(1,`nbitems',0)
|
||||
|
||||
global scale=0
|
||||
scalar `stopscale'=0
|
||||
|
||||
while `stopscale'!=1 { /*WHILE IT IS POSSIBLE TO CONSTRUCT SCALES*/
|
||||
global scale=$scale+1
|
||||
local dimension=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp'
|
||||
global `scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
|
||||
/**********************************************************************************************************
|
||||
BEGINING OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
/****NONE KERNEL OR NOT THE FIRST SCALE****/
|
||||
if $scale>1|`kernel'==0 {
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
local nbitemsbon=0
|
||||
forvalues j1=1/`nbitems' {
|
||||
if `dim'[1,`j1']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
if `nbitemsbon'<=1 {
|
||||
local nbitemsbon=2
|
||||
}
|
||||
local kbon=`nbitemsbon'*(`nbitemsbon'-1)/2
|
||||
if "`bon'"==""&"`test'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`test'"!="" {
|
||||
local pbon=1
|
||||
local p=1
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j1=1/`nbitems' { /*WE SEARCH THE BEST PAIR OF ITEMS*/
|
||||
if `dim'[1,`j1']==0 {
|
||||
scalar `plusitems'=1
|
||||
forvalues j2=`=`j1'+1'/`nbitems' {
|
||||
if `dim'[1,`j2']==0 {
|
||||
if `Hjk'[`j1',`j2']>`Hjkmax'&`pvalHjk'[`j1',`j2']<=`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j1',`j2']
|
||||
global j1max=`j1'
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 { /*IF NONE PAIR OF ITEM VERIFY Hjk>0*/
|
||||
if `plusitems'==0 {
|
||||
if "`details'"=="" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None pair of items has a significantly positive Hjk coefficient.{p_end}"
|
||||
}
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE PAIR OF ITEM VERIFY Hjk>c*/
|
||||
if $scale==1 {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None pair of items verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None new scale can be constructed because none pair of items, among the remaining items, verifies Hjk>`c'{p_end}"
|
||||
}
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /*IF THERE IS AT LEAST ONE PAIR OF ITEM WHO VERIFY Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j1max' `$j2max'"
|
||||
global `scaletmpnum' "$j1max $j2max"
|
||||
if "`details'"=="" {
|
||||
di in green "{p}The two first items selected in the scale " in yellow "$scale " in green "are " in yellow "`$j1max' " in green "and " in yellow "`$j2max'" in green " (Hjk=" in yellow %6.4f `Hjkmax' in green "){p_end}"
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE TWO SELECTED ITEMS*/
|
||||
if ((`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if ((`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF ONE ITEM****/
|
||||
if $scale==1&`kernel'==1 {
|
||||
global j1max=1
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`1'" in green " is the kernel of the first scale"
|
||||
}
|
||||
local nbitemsbon=0
|
||||
forvalues i=2/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE ITEM OF THE KERNEL*/
|
||||
if (`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`nbitemsbon'
|
||||
if "`bon'"==""&"`test'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=2/`nbitems' {/*WE SEARCH THE BEST ITEM TO SELECT WITH THE KERNEL*/
|
||||
if `Hjk'[`j2',1]>`Hjkmax'&`pvalHjk'[`j2',1]<`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j2',1]
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 {/*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<*/
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None item associated to the item " in yellow "$j1 " in green "allows obtaining a significantly positive value for the Hjk coefficient.{p_end}"
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<c*/
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None index Hjk associated to the item " in yellow "$j1 " in green "verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /* IF AT LEAST ONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j2max' `$j1max'"
|
||||
global `scaletmpnum' "$j2max $j1max"
|
||||
if "`details'"=="" {
|
||||
di in green "The second item selected in the first scale is " in yellow "`$j2max' " in green "(Hjk=" in yellow %6.4f `Hjkmax' in green")"
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE NEW SELECTED ITEM*/
|
||||
if (`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>`p')&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF SEVERAL ITEMS****/
|
||||
if $scale==1&`kernel'>=2 {
|
||||
global scale1
|
||||
local scalenum1
|
||||
local kbon=1
|
||||
local pbon=`p'
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=1/`kernel' {
|
||||
global scale1 ``j2'' $scale1
|
||||
global scalenum1 $scalenum1 `j2'
|
||||
matrix `dim'[1,`j2']=1
|
||||
}
|
||||
if "`details'"=="" {
|
||||
di in green "{p}The kernel of the first scale is composed of the items " in yellow "$scale1{p_end}"
|
||||
}
|
||||
scalar `nbitemsel'=`kernel'
|
||||
forvalues j=1/`kernel' {
|
||||
forvalues i=1/`nbitems' { /* WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE ITEMS OF THE KERNEL*/
|
||||
if (`loevHjks'[`i',`j']<`minvalue'|`pvalHjk'[`i',`j']>`p')&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
local excluded `excluded' ``i''
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
scalar `stop'=0
|
||||
|
||||
/**********************************************************************************************************
|
||||
END OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
while `stop'!=1 { /*WHILE THE PROCEDURE TO CONSTRUCT THE ACTUAL SCALE IS NOT STOPPED*/
|
||||
|
||||
scalar `Hjmax'=-99
|
||||
scalar `Hmax'=-99
|
||||
global jmax=0
|
||||
|
||||
global stopmax=0
|
||||
local nbitemsbon=0
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`kbon'+`nbitemsbon'
|
||||
if "`bon'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
|
||||
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {/*IF THE ITEM J0 IS UNSELECTED*/
|
||||
global stopmax=1
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
qui loevh ``j0'' $`scaletmp' ,`pairwise' pair `adjust'
|
||||
tempname pvalHj0
|
||||
matrix `pvalHj0'=r(pvalHj)
|
||||
scalar `pvalHj0'=`pvalHj0'[1,1]
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
local nbitsc : word count $`scaletmp'
|
||||
local nbitsc=`nbitsc'+1
|
||||
if `loevHj'[1,1]>`c'&`pvalHj0'<`pbon' {/*IF THE ITEM J0 CAN BE SELECTED*/
|
||||
if `loevH'>`Hmax' {/*AND IF IT IS THE BEST ITEM (COMPARED TO THE PRECEEDING ITEMS)*/
|
||||
scalar `Hjmax'=`loevHj'[1,1]
|
||||
scalar `Hmax'=`loevH'
|
||||
global j="``j0''"
|
||||
global j0=`j0'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if $stopmax==1&`Hjmax'==-99 { /*IF THERE IS ITEMS REMAINING BUT NONE OF THEM CAN BE SELECTED*/
|
||||
|
||||
if "`details'"=="" {
|
||||
di in green "{p}None new item can be selected in the scale $scale because all the Hj are lesser than `c' or none new item has all the related Hjk coefficients significantly greater than 0{p_end}."
|
||||
}
|
||||
scalar `stop'=1
|
||||
continue,break
|
||||
}
|
||||
if $stopmax==0 { /*IF THERE IS NO MORE ITEM REMAINING*/
|
||||
if "`details'"=="" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
scalar `stopscale'=1
|
||||
scalar `stop'=1
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
scalar `stopscale'=0
|
||||
}
|
||||
}
|
||||
*global scale=$scale-1
|
||||
continue,break
|
||||
}
|
||||
|
||||
|
||||
if `stop'!=1 { /*IF THE PROCEDURE IS NOT STOPPED*/
|
||||
matrix `dim'[1,$j0]=$scale
|
||||
local `scaletmp'="scale$scale"
|
||||
local `scaletmpnum'="scalenum$scale"
|
||||
global `scaletmp' $j $`scaletmp'
|
||||
global `scaletmpnum' $j0 $`scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`$j0' " in green "is selected in the scale " in yellow "$scale" _col(50) in green "Hj=" in yellow %6.4f `Hjmax' _col(65) in green "H=" in yellow %6.4f `Hmax' ""
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
if `dim'[1,`i']==0 { /*WE EXCLUDE ITEMS WHO HAVE A NEGATIVE Hjk WITH THE NEW SELECTED ITEM*/
|
||||
if `loevHjks'[`i',$j0]<`minvalue'|`pvalHjk'[`i',$j0]>`p' {
|
||||
matrix `dim'[1,`i']=-1
|
||||
local excluded `excluded' ``i''
|
||||
}
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
}
|
||||
}
|
||||
di
|
||||
local scaleencours="scale$scale"
|
||||
local scalenumencours="scalenum$scale"
|
||||
local nbitemscale : word count $`scaleencours'
|
||||
return scalar nbitems`dimension'=`nbitemscale'
|
||||
if `nbitemscale'>0 { /* IF AT LEAST TWO ITEMS HAVE BEEN SELECTED*/
|
||||
if "`details'"!="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
loevh $`scaleencours',`pairwise' `adjust'
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
return scalar H`dimension'=`loevH'
|
||||
return local scale`dimension' $`scaleencours'
|
||||
return local scalenum`dimension' $`scalenumencours'
|
||||
local j=`nbitemscale'
|
||||
di
|
||||
}
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
matrix `dim'[1,`i']=0
|
||||
}
|
||||
}
|
||||
|
||||
local restnbitems=0
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {
|
||||
local restnbitems=`restnbitems'+1
|
||||
local restitem ``j0''
|
||||
}
|
||||
}
|
||||
|
||||
if `restnbitems'==1 { /*IF THERE IS ONLY ONE ITEM REMAINING*/
|
||||
di
|
||||
di in green "{p}There is only one item remaining (" in yellow "`restitem'" in green ").{p_end}"
|
||||
local stopscale=1
|
||||
return local lastitem "`restitem'"
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return scalar dim=$scale
|
||||
matrix colnames `dim'=`varlist'
|
||||
return matrix selection=`dim'
|
||||
|
||||
qui use "`mspfile'",clear
|
||||
end
|
||||
94
Modules/ado/personal/m/msp.hlp
Normal file
94
Modules/ado/personal/m/msp.hlp
Normal file
@ -0,0 +1,94 @@
|
||||
{smcl}
|
||||
{* 8december2010}{...}
|
||||
{hline}
|
||||
help for {hi:msp}
|
||||
{hline}
|
||||
|
||||
{title:Mokken Scale Procedure}
|
||||
|
||||
{p 8 14 2}{cmd:msp} {it:varlist} [{cmd:,} {cmdab:nodet:ails}
|
||||
{cmdab:ker:nel}({it:#}) {cmdab:c}({it:#}) {cmdab:note:st} {cmdab:p}({it:#})
|
||||
{cmdab:pairw:ise} {cmdab:minv:alue}({it:#}) {cmdab:nob:on} {cmdab:noadj:ust}]
|
||||
|
||||
|
||||
{p 8 14 2}{it:varlist} is a list of two existing dichotomous or polytomous variables or more.
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 8 2}{cmd:msp} selects scales of dichotomous or polytomous items with the Mokken Scale Procedure (MSP) defined by Mokken (1971) and by Hemker and al. (1995).
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 8 2}{cmd:nodetails} does not display informations during the algorithm of selection.
|
||||
|
||||
{p 4 8 2}{cmd:kernel} defines a kernel for the first scale, defined as the first # items of {it:varlist}.
|
||||
|
||||
{p 4 8 2}{cmd:c} defines the level of the threshold {it:c}. The level of {it:c} is fixed, by default, at 0.3.
|
||||
|
||||
{p 4 8 2}{cmd:notest} selects the scales whithout tests among the Loevinger H coefficient (Hjk>0): the new item with the maximal value for Hj>c is always selected.
|
||||
|
||||
{p 4 8 2}{cmd:p} defines the significance level for the tests. The level of {it:p} is fixed, by default, at 0.05.
|
||||
|
||||
{p 4 8 2}{cmd:pairwise} does no more delete all the individuals who have one or more missing values among all the items of {it:varlist} but, only these ones who have, for each pair of items, one or more missing values to these two items.
|
||||
|
||||
{p 4 8 2}{cmd:minvalue} defines the minimum value of the coefficient Hjk for a pair of item composing a scale. This minimal value is fixed by default to 0.
|
||||
|
||||
{p 4 8 2}{cmd:nobon} does no more correct the significance level of the test by the Bonferroni correction.
|
||||
|
||||
{p 4 8 2}{cmd:noadjust} approximates the tests statistics like the MSP program (Molenaar et al. (2000)).
|
||||
|
||||
{title:Remarks}
|
||||
|
||||
{p 4 8 2}For detailed informations on the Mokken scale procedure, see Mokken (1971), Hemker and al. (1995) or the manual of the MSP software (2000).
|
||||
|
||||
{p 4 8 2}{cmd:msp} runs with dichotomous or polytomous items.
|
||||
|
||||
{p 4 8 2}{cmd:msp} necessitates the program {help loevh} to compute.
|
||||
|
||||
{title:Results}
|
||||
|
||||
{p 4 8 2}{it:r(dim)} saves the number of built scales.
|
||||
|
||||
{p 4 8 2}{it:r(selection)} saves a vector which contains, for each item, the scale where it is selected (0 if the item is unselected).
|
||||
|
||||
{p 4 8 2}{it:r(H#)} saves the Loevinger's H coefficient of the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(nbitems#)} saves the number of items selected in the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(scale#)} saves the names of the items selected in the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(scalenum#)} saves the numbers of the items selected in the #th scale.
|
||||
|
||||
{title:Example}
|
||||
|
||||
{inp:. msp itemA1-itemA7}
|
||||
|
||||
{inp:. msp itemA* itemB* , nodetails kernel(2)}
|
||||
|
||||
{inp:. msp item* , c(0.2) nob pairwise}
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{p 4 8 2}Hemker B. T., Sijtsma K. and Molenaar I. W., Selection of unidimensional scales from a multidimensional item bank in the polytomous Mokken IRT
|
||||
model, {it: Applied Psychological Measurement}, vol.19(4), 1995, pp. 337-352.
|
||||
|
||||
{p 4 8 2}Mokken R. J., A theory and procedure of scale analysis. New-York/Berlin : De Guyter, 1971.
|
||||
|
||||
{p 4 8 2}Molenaar I. W., Sijtsma K. and Boer P. User's manual of MSP5 for Windows. Groningen (The Netherlands): iec ProGAMMA, 2000.
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 4 13 2} help for {help loevh} (if installed), help for {help mokken} (if installed)
|
||||
|
||||
{title:Author}
|
||||
|
||||
{p 4 8 2}Jean-Benoit Hardouin, PhD, assistant professor{p_end}
|
||||
{p 4 8 2}EA 4275 "Team of Biostatistics, Clinical Research and Subjective Measures in Health Sciences"{p_end}
|
||||
{p 4 8 2}University of Nantes - Faculty of Pharmaceutical Sciences{p_end}
|
||||
{p 4 8 2}1, rue Gaston Veil - BP 53508{p_end}
|
||||
{p 4 8 2}44035 Nantes Cedex 1 - FRANCE{p_end}
|
||||
{p 4 8 2}Email:
|
||||
{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}{p_end}
|
||||
{p 4 8 2}Websites {browse "http://www.anaqol.org":AnaQol}
|
||||
and {browse "http://www.freeirt.org":FreeIRT}
|
||||
447
Modules/ado/personal/m/msp66.ado
Normal file
447
Modules/ado/personal/m/msp66.ado
Normal file
@ -0,0 +1,447 @@
|
||||
*! Version 6.6 16 Februar 2007
|
||||
*! Jean-Benoit Hardouin
|
||||
************************************************************************************************************
|
||||
* Stata program : msp
|
||||
* Mokken Scale Procedure
|
||||
* Release 6.6: Februar 16, 2007 /*Tests of the loevinger H indices, adaptation for loevH 6.6, noadjust option, improvements*/
|
||||
*
|
||||
* Historic :
|
||||
* Version 1 - lose version (August 20, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 2 (September 25, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 3 (December 1, 2003) [Jean-Benoit Hardouin]
|
||||
* Version 4 (January 20, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5 (March 22, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5.1 (May 1st, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6 : (July 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.1 : (September 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.2 : (January 22, 2006) [Jean-Benoit Hardouin] /*English improvements*/
|
||||
* Release 6.3 : (March 20, 2006) [Jean-Benoit Hardouin] /*A bug with temporary files */
|
||||
*
|
||||
* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences
|
||||
* Department of Biostatistics - France
|
||||
* jean-benoit.hardouin@anaqol.org
|
||||
*
|
||||
* The Stata program loevH is needed. It can be downloaded on http://www.anaqol.org
|
||||
* News about this program :http://www.anaqol.org
|
||||
* FreeIRT Project website : http://www.freeirt.org
|
||||
*
|
||||
* Copyright 2002-2007 Jean-Benoit Hardouin
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation; either version 2 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||||
*
|
||||
************************************************************************************************************
|
||||
|
||||
program define msp , rclass
|
||||
version 7.0
|
||||
syntax varlist(min=2 numeric) [,c(real 0.3) noDETails KERnel(integer 0) noTEst p(real 0.05) PAIRWise MINValue(real 0) noBon noADJust]
|
||||
local nbitems : word count `varlist'
|
||||
tokenize `varlist'
|
||||
tempfile mspfile
|
||||
qui save "`mspfile'"
|
||||
|
||||
if "`pairwise'"=="" {
|
||||
forvalues j=1/`nbitems' {
|
||||
qui drop if ``j''==.
|
||||
}
|
||||
}
|
||||
if "`test'"!="" {
|
||||
local p=1
|
||||
local minvalue=-99
|
||||
local bon nobon
|
||||
}
|
||||
|
||||
|
||||
|
||||
qui loevH `varlist',`pairwise' pair `adjust'
|
||||
tempname pvalHjk loevHjk loevHjks loevHj loevH Hjk stopscale stop dim plusitems Hjkmax Hjmax Hmax nbitemsel
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHjks'=r(loevHjk)
|
||||
matrix `pvalHjk'=r(pvalHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
matrix `Hjk'=`loevHjk'
|
||||
|
||||
matrix define `dim'=J(1,`nbitems',0)
|
||||
|
||||
global scale=0
|
||||
scalar `stopscale'=0
|
||||
|
||||
while `stopscale'!=1 { /*WHILE IT IS POSSIBLE TO CONSTRUCT SCALES*/
|
||||
global scale=$scale+1
|
||||
local dimension=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp'
|
||||
global `scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
|
||||
|
||||
/**********************************************************************************************************
|
||||
BEGINING OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
/****NONE KERNEL OR NOT THE FIRST SCALE****/
|
||||
if $scale>1|`kernel'==0 {
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
local nbitemsbon=0
|
||||
forvalues j1=1/`nbitems' {
|
||||
if `dim'[1,`j1']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
if `nbitemsbon'<=1 {
|
||||
local nbitemsbon=2
|
||||
}
|
||||
local kbon=`nbitemsbon'*(`nbitemsbon'-1)/2
|
||||
if "`bon'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j1=1/`nbitems' { /*WE SEARCH THE BEST PAIR OF ITEMS*/
|
||||
if `dim'[1,`j1']==0 {
|
||||
scalar `plusitems'=1
|
||||
forvalues j2=`=`j1'+1'/`nbitems' {
|
||||
if `dim'[1,`j2']==0 {
|
||||
if `Hjk'[`j1',`j2']>`Hjkmax'&`pvalHjk'[`j1',`j2']<=`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j1',`j2']
|
||||
global j1max=`j1'
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 { /*IF NONE PAIR OF ITEM VERIFY Hjk>0*/
|
||||
if `plusitems'==0 {
|
||||
if "`details'"=="" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None pair of items has a significantly positive Hjk coefficient.{p_end}"
|
||||
}
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE PAIR OF ITEM VERIFY Hjk>c*/
|
||||
if $scale==1 {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None pair of items verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None new scale can be constructed because none pair of items, among the remaining items, verifies Hjk>`c'{p_end}"
|
||||
}
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /*IF THERE IS AT LEAST ONE PAIR OF ITEM WHO VERIFY Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j1max' `$j2max'"
|
||||
global `scaletmpnum' "$j1max $j2max"
|
||||
if "`details'"=="" {
|
||||
di in green "{p}The two first items selected in the scale " in yellow "$scale " in green "are " in yellow "`$j1max' " in green "and " in yellow "`$j2max'" in green " (Hjk=" in yellow %6.4f `Hjkmax' in green "){p_end}"
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE TWO SELECTED ITEMS*/
|
||||
if (`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>.05)&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if (`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>.05)&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF ONE ITEM****/
|
||||
if $scale==1&`kernel'==1 {
|
||||
global j1max=1
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`1'" in green " is the kernel of the first scale"
|
||||
}
|
||||
local nbitemsbon=0
|
||||
forvalues i=2/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE ITEM OF THE KERNEL*/
|
||||
if (`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>.05)&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`nbitemsbon'
|
||||
if "`bon'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=2/`nbitems' {/*WE SEARCH THE BEST ITEM TO SELECT WITH THE KERNEL*/
|
||||
if `Hjk'[`j2',1]>`Hjkmax'&`pvalHjk'[`j2',1]<`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j2',1]
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 {/*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<*/
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None item associated to the item " in yellow "$j1 " in green "allows obtaining a significantly positive value for the Hjk coefficient.{p_end}"
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<c*/
|
||||
if "`details'"=="" {
|
||||
di as green "{p}None index Hjk associated to the item " in yellow "$j1 " in green "verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /* IF AT LEAST ONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j2max' `$j1max'"
|
||||
global `scaletmpnum' "$j2max $j1max"
|
||||
if "`details'"=="" {
|
||||
di in green "The second item selected in the first scale is " in yellow "`$j2max' " in green "(Hjk=" in yellow %6.4f `Hjkmax' in green")"
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE NEW SELECTED ITEM*/
|
||||
if (`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>.05)&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF SEVERAL ITEMS****/
|
||||
if $scale==1&`kernel'>=2 {
|
||||
global scale1
|
||||
local scalenum1
|
||||
local kbon=1
|
||||
local pbon=`p'
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=1/`kernel' {
|
||||
global scale1 ``j2'' $scale1
|
||||
global scalenum1 $scalenum1 `j2'
|
||||
matrix `dim'[1,`j2']=1
|
||||
}
|
||||
if "`details'"=="" {
|
||||
di in green "{p}The kernel of the first scale is composed of the items " in yellow "$scale1{p_end}"
|
||||
}
|
||||
scalar `nbitemsel'=`kernel'
|
||||
forvalues j=1/`kernel' {
|
||||
forvalues i=1/`nbitems' { /* WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE ITEMS OF THE KERNEL*/
|
||||
if (`loevHjks'[`i',`j']<`minvalue'|`pvalHjk'[`i',`j']>.05)&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
local excluded `excluded' ``i''
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
scalar `stop'=0
|
||||
|
||||
/**********************************************************************************************************
|
||||
END OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
while `stop'!=1 { /*WHILE THE PROCEDURE TO CONSTRUCT THE ACTUAL SCALE IS NOT STOPPED*/
|
||||
|
||||
scalar `Hjmax'=-99
|
||||
scalar `Hmax'=-99
|
||||
global jmax=0
|
||||
|
||||
global stopmax=0
|
||||
local nbitemsbon=0
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`kbon'+`nbitemsbon'
|
||||
if "`bon'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
|
||||
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {/*IF THE ITEM J0 IS UNSELECTED*/
|
||||
global stopmax=1
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
qui loevH ``j0'' $`scaletmp' ,`pairwise' pair `adjust'
|
||||
tempname pvalHj0
|
||||
matrix `pvalHj0'=r(pvalHj)
|
||||
scalar `pvalHj0'=`pvalHj0'[1,1]
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
local nbitsc : word count $`scaletmp'
|
||||
local nbitsc=`nbitsc'+1
|
||||
if `loevHj'[1,1]>`c'&`pvalHj0'<`pbon' {/*IF THE ITEM J0 CAN BE SELECTED*/
|
||||
if `loevH'>`Hmax' {/*AND IF IT IS THE BEST ITEM (COMPARED TO THE PRECEEDING ITEMS)*/
|
||||
scalar `Hjmax'=`loevHj'[1,1]
|
||||
scalar `Hmax'=`loevH'
|
||||
global j="``j0''"
|
||||
global j0=`j0'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if $stopmax==1&`Hjmax'==-99 { /*IF THERE IS ITEMS REMAINING BUT NONE OF THEM CAN BE SELECTED*/
|
||||
|
||||
if "`details'"=="" {
|
||||
di in green "{p}None new item can be selected in the scale $scale because all the Hj are lesser than `c' or none new item has all the related Hjk coefficients significantly greater than 0{p_end}."
|
||||
}
|
||||
scalar `stop'=1
|
||||
continue,break
|
||||
}
|
||||
if $stopmax==0 { /*IF THERE IS NO MORE ITEM REMAINING*/
|
||||
if "`details'"=="" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
scalar `stopscale'=1
|
||||
scalar `stop'=1
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
scalar `stopscale'=0
|
||||
}
|
||||
}
|
||||
*global scale=$scale-1
|
||||
continue,break
|
||||
}
|
||||
|
||||
|
||||
if `stop'!=1 { /*IF THE PROCEDURE IS NOT STOPPED*/
|
||||
matrix `dim'[1,$j0]=$scale
|
||||
local `scaletmp'="scale$scale"
|
||||
local `scaletmpnum'="scalenum$scale"
|
||||
global `scaletmp' $j $`scaletmp'
|
||||
global `scaletmpnum' $j0 $`scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`$j0' " in green "is selected in the scale " in yellow "$scale" _col(50) in green "Hj=" in yellow %6.4f `Hjmax' _col(65) in green "H=" in yellow %6.4f `Hmax' ""
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
if `dim'[1,`i']==0 { /*WE EXCLUDE ITEMS WHO HAVE A NEGATIVE Hjk WITH THE NEW SELECTED ITEM*/
|
||||
if `loevHjks'[`i',$j0]<`minvalue'|`pvalHjk'[`i',$j0]>.05 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
local excluded `excluded' ``i''
|
||||
}
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
}
|
||||
}
|
||||
di
|
||||
local scaleencours="scale$scale"
|
||||
local scalenumencours="scalenum$scale"
|
||||
local nbitemscale : word count $`scaleencours'
|
||||
return scalar nbitems`dimension'=`nbitemscale'
|
||||
if `nbitemscale'>0 { /* IF AT LEAST TWO ITEMS HAVE BEEN SELECTED*/
|
||||
if "`details'"!="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
loevH $`scaleencours',`pairwise' `adjust'
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
return scalar H`dimension'=`loevH'
|
||||
return local scale`dimension' $`scaleencours'
|
||||
return local scalenum`dimension' $`scalenumencours'
|
||||
local j=`nbitemscale'
|
||||
di
|
||||
}
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
matrix `dim'[1,`i']=0
|
||||
}
|
||||
}
|
||||
|
||||
local restnbitems=0
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {
|
||||
local restnbitems=`restnbitems'+1
|
||||
local restitem ``j0''
|
||||
}
|
||||
}
|
||||
|
||||
if `restnbitems'==1 { /*IF THERE IS ONLY ONE ITEM REMAINING*/
|
||||
di
|
||||
di in green "{p}There is only one item remaining (" in yellow "`restitem'" in green ").{p_end}"
|
||||
local stopscale=1
|
||||
return local lastitem "`restitem'"
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return scalar dim=$scale
|
||||
matrix colnames `dim'=`varlist'
|
||||
return matrix selection=`dim'
|
||||
|
||||
qui use "`mspfile'",clear
|
||||
end
|
||||
91
Modules/ado/personal/m/msp66.hlp
Normal file
91
Modules/ado/personal/m/msp66.hlp
Normal file
@ -0,0 +1,91 @@
|
||||
{smcl}
|
||||
{* 22january2006}{...}
|
||||
{hline}
|
||||
help for {hi:msp}
|
||||
{hline}
|
||||
|
||||
{title:Mokken Scale Procedure}
|
||||
|
||||
{p 8 14 2}{cmd:msp} {it:varlist} [{cmd:,} {cmdab:nodet:ails}
|
||||
{cmdab:ker:nel}({it:#}) {cmdab:c}({it:#}) {cmdab:note:st} {cmdab:p}({it:#})
|
||||
{cmdab:pairw:ise} {cmdab:minv:alue}({it:#}) {cmdab:nob:on} {cmdab:noadj:ust}]
|
||||
|
||||
|
||||
{p 8 14 2}{it:varlist} is a list of two existing dichotomous or polytomous variables or more.
|
||||
|
||||
{title:Description}
|
||||
|
||||
{p 4 8 2}{cmd:msp} selects scales of dichotomous or polytomous items with the Mokken Scale Procedure (MSP) defined by Mokken (1971) and by Hemker and al. (1995).
|
||||
|
||||
{title:Options}
|
||||
|
||||
{p 4 8 2}{cmd:nodetails} does not display informations during the algorithm of selection.
|
||||
|
||||
{p 4 8 2}{cmd:kernel} defines a kernel for the first scale, defined as the first # items of {it:varlist}.
|
||||
|
||||
{p 4 8 2}{cmd:c} defines the level of the threshold {it:c}. The level of {it:c} is fixed, by default, at 0.3.
|
||||
|
||||
{p 4 8 2}{cmd:notest} selects the scales whithout tests among the Loevinger H coefficient (Hjk>0): the new item with the maximal value for Hj>c is always selected.
|
||||
|
||||
{p 4 8 2}{cmd:p} defines the significance level for the tests. The level of {it:p} is fixed, by default, at 0.05.
|
||||
|
||||
{p 4 8 2}{cmd:pairwise} does no more delete all the individuals who have one or more missing values among all the items of {it:varlist} but, only these ones who have, for each pair of items, one or more missing values to these two items.
|
||||
|
||||
{p 4 8 2}{cmd:minvalue} defines the minimum value of the coefficient Hjk for a pair of item composing a scale. This minimal value is fixed by default to 0.
|
||||
|
||||
{p 4 8 2}{cmd:nobon} does no more correct the significance level of the test by the Bonferroni correction.
|
||||
|
||||
{p 4 8 2}{cmd:noadjust} approximates the tests statistics like the MSP program (Molenaar et al. (2000)).
|
||||
|
||||
{title:Remarks}
|
||||
|
||||
{p 4 8 2}For detailed informations on the Mokken scale procedure, see Mokken (1971), Hemker and al. (1995) or the manual of the MSP software (2000).
|
||||
|
||||
{p 4 8 2}{cmd:msp} runs with dichotomous or polytomous items.
|
||||
|
||||
{p 4 8 2}{cmd:msp} necessitates the program {help loevH} to compute.
|
||||
|
||||
{title:Results}
|
||||
|
||||
{p 4 8 2}{it:r(dim)} saves the number of built scales.
|
||||
|
||||
{p 4 8 2}{it:r(selection)} saves a vector which contains, for each item, the scale where it is selected (0 if the item is unselected).
|
||||
|
||||
{p 4 8 2}{it:r(H#)} saves the Loevinger's H coefficient of the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(nbitems#)} saves the number of items selected in the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(scale#)} saves the names of the items selected in the #th scale.
|
||||
|
||||
{p 4 8 2}{it:r(scalenum#)} saves the numbers of the items selected in the #th scale.
|
||||
|
||||
{title:Example}
|
||||
|
||||
{inp:. msp itemA1-itemA7}
|
||||
|
||||
{inp:. msp itemA* itemB* , nodetails kernel(2)}
|
||||
|
||||
{inp:. msp item* , c(0.2) nob pairwise}
|
||||
|
||||
|
||||
{title:References}
|
||||
|
||||
{p 4 8 2}Hemker B. T., Sijtsma K. and Molenaar I. W., Selection of unidimensional scales from a multidimensional item bank in the polytomous Mokken IRT
|
||||
model, {it: Applied Psychological Measurement}, vol.19(4), 1995, pp. 337-352.
|
||||
|
||||
{p 4 8 2}Mokken R. J., A theory and procedure of scale analysis. New-York/Berlin : De Guyter, 1971.
|
||||
|
||||
{p 4 8 2}Molenaar I. W., Sijtsma K. and Boer P. User's manual of MSP5 for Windows. Groningen (The Netherlands): iec ProGAMMA, 2000.
|
||||
|
||||
{title:Also see}
|
||||
|
||||
{p 4 13 2} help for {help loevh} (if installed), help for {help mokken} (if installed)
|
||||
|
||||
{title:Author}
|
||||
|
||||
{p 4 8 2} Jean-Benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences -
|
||||
Department of Biostatistics. 1, rue Gaston Veil - BP 53508 - 44035 Nantes Cedex 1 - FRANCE. You can contact the
|
||||
author at
|
||||
{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}
|
||||
and visit the websites {browse "http://www.anaqol.org":AnaQol}
|
||||
and {browse "http://www.freeirt.org":FreeIRT}
|
||||
516
Modules/ado/personal/m/msp82.ado
Normal file
516
Modules/ado/personal/m/msp82.ado
Normal file
@ -0,0 +1,516 @@
|
||||
*! Version 8.2 April 30, 2014
|
||||
*! Jean-Benoit Hardouin, Bastien Perrot
|
||||
************************************************************************************************************
|
||||
* Stata program : msp
|
||||
* Mokken Scale Procedure
|
||||
* Release 8.2: (April 30, 2014) [Jean-Benoit Hardouin, Bastien Perrot] /*HTML option, correction of a bug when kernel=1, IF option */
|
||||
*
|
||||
* Historic :
|
||||
* Version 1 - lose version (August 20, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 2 (September 25, 2002) [Jean-Benoit Hardouin]
|
||||
* Version 3 (December 1, 2003) [Jean-Benoit Hardouin]
|
||||
* Version 4 (January 20, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5 (March 22, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 5.1 (May 1st, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6 : (July 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.1 : (September 5, 2004) [Jean-Benoit Hardouin]
|
||||
* Version 6.2 : (January 22, 2006) [Jean-Benoit Hardouin] /*English improvements*/
|
||||
* Release 6.3 : (March 20, 2006) [Jean-Benoit Hardouin] /*A bug with temporary files */
|
||||
* Release 6.6: (Februar 16, 2007) [Jean-Benoit Hardouin] /*Tests of the loevinger H indices, adaptation for loevH 6.6, noadjust option, improvements*/
|
||||
* Release 8: (December 8, 2010) [Jean-Benoit Hardouin] /*Adaptation for loevh version 8*/
|
||||
* Release 8.1: (December 19, 2012) [Jean-Benoit Hardouin] /*correction of a bug with the notest option*/
|
||||
* Release 8.1.1: (September 26, 2013) [Jean-Benoit Hardouin] /*correction of a rare bug */
|
||||
* Release 8.2: (April 30, 2014) [Jean-Benoit Hardouin, Bastien Perrot] /*HTML option, correction of a bug when kernel=1, IF option */
|
||||
*
|
||||
* Jean-benoit Hardouin, University of Nantes - Faculty of Pharmaceutical Sciences
|
||||
* Department of Biostatistics - France
|
||||
* jean-benoit.hardouin@anaqol.org
|
||||
*
|
||||
* The Stata program loevh is needed. It can be downloaded on http://www.anaqol.org
|
||||
* News about this program :http://www.anaqol.org
|
||||
* FreeIRT Project website : http://www.freeirt.org
|
||||
*
|
||||
* Copyright 2002-2007, 2010, 2012 Jean-Benoit Hardouin
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation; either version 2 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
|
||||
*
|
||||
************************************************************************************************************
|
||||
|
||||
program define msp82 , rclass
|
||||
version 7.0
|
||||
syntax varlist(min=2 numeric) [if] [,c(real 0.3) noDETails KERnel(integer 0) noTEst p(real 0.05) PAIRWise MINValue(real 0) noBon noADJust HTML(string)]
|
||||
local nbitems : word count `varlist'
|
||||
tokenize `varlist'
|
||||
tempfile mspfile
|
||||
qui save "`mspfile'"
|
||||
|
||||
if "`if'"!="" {
|
||||
qui keep `if'
|
||||
}
|
||||
|
||||
if "`pairwise'"=="" {
|
||||
forvalues j=1/`nbitems' {
|
||||
qui drop if ``j''==.
|
||||
}
|
||||
}
|
||||
if "`test'"!="" {
|
||||
local p=1
|
||||
local minvalue=-99
|
||||
local bon nobon
|
||||
}
|
||||
|
||||
if "`html'"!="" {
|
||||
di "<!-- SphereCalc start of response -->"
|
||||
di "<pre>"
|
||||
}
|
||||
|
||||
|
||||
qui loevh `varlist',`pairwise' pair `adjust' html(`html')
|
||||
tempname pvalHjk loevHjk loevHjks loevHj loevH Hjk stopscale stop dim plusitems Hjkmax Hjmax Hmax nbitemsel
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHjks'=r(loevHjk)
|
||||
matrix `pvalHjk'=r(pvalHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
matrix `Hjk'=`loevHjk'
|
||||
|
||||
matrix define `dim'=J(1,`nbitems',0)
|
||||
|
||||
global scale=0
|
||||
scalar `stopscale'=0
|
||||
|
||||
while `stopscale'!=1 { /*WHILE IT IS POSSIBLE TO CONSTRUCT SCALES*/
|
||||
global scale=$scale+1
|
||||
local dimension=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp'
|
||||
global `scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
|
||||
/**********************************************************************************************************
|
||||
BEGINING OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
/****NONE KERNEL OR NOT THE FIRST SCALE****/
|
||||
if $scale>1|`kernel'==0 {
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
local nbitemsbon=0
|
||||
forvalues j1=1/`nbitems' {
|
||||
if `dim'[1,`j1']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
if `nbitemsbon'<=1 {
|
||||
local nbitemsbon=2
|
||||
}
|
||||
local kbon=`nbitemsbon'*(`nbitemsbon'-1)/2
|
||||
if "`bon'"==""&"`test'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`test'"!="" {
|
||||
local pbon=1
|
||||
local p=1
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j1=1/`nbitems' { /*WE SEARCH THE BEST PAIR OF ITEMS*/
|
||||
if `dim'[1,`j1']==0 {
|
||||
scalar `plusitems'=1
|
||||
forvalues j2=`=`j1'+1'/`nbitems' {
|
||||
if `dim'[1,`j2']==0 {
|
||||
if `Hjk'[`j1',`j2']>`Hjkmax'&`pvalHjk'[`j1',`j2']<=`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j1',`j2']
|
||||
global j1max=`j1'
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 { /*IF NONE PAIR OF ITEM VERIFY Hjk>0*/
|
||||
if `plusitems'==0 {
|
||||
if "`details'"=="" {
|
||||
if "`html" == "" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
else {
|
||||
di in green "There is no more items remaining."
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
if "`html" == "" {
|
||||
di as green "{p}None pair of items has a significantly positive Hjk coefficient.{p_end}"
|
||||
}
|
||||
else {
|
||||
di as green "None pair of items has a significantly positive Hjk coefficient."
|
||||
}
|
||||
}
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE PAIR OF ITEM VERIFY Hjk>c*/
|
||||
if $scale==1 {
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di as green "{p}None pair of items verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
else {
|
||||
di as green "None pair of items verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed."
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di as green "{p}None new scale can be constructed because none pair of items, among the remaining items, verifies Hjk>`c'{p_end}"
|
||||
}
|
||||
else {
|
||||
di as green "None new scale can be constructed because none pair of items, among the remaining items, verifies Hjk>`c'"
|
||||
}
|
||||
}
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /*IF THERE IS AT LEAST ONE PAIR OF ITEM WHO VERIFY Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j1max' `$j2max'"
|
||||
global `scaletmpnum' "$j1max $j2max"
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di in green "{p}The two first items selected in the scale " in yellow "$scale " in green "are " in yellow "`$j1max' " in green "and " in yellow "`$j2max'" in green " (Hjk=" in yellow %6.4f `Hjkmax' in green "){p_end}"
|
||||
}
|
||||
else {
|
||||
di in green "The two first items selected in the scale " in yellow "$scale " in green "are " in yellow "`$j1max' " in green "and " in yellow "`$j2max'" in green " (Hjk=" in yellow %6.4f `Hjkmax' in green ")"
|
||||
}
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE TWO SELECTED ITEMS*/
|
||||
if ((`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if ((`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF ONE ITEM****/
|
||||
if $scale==1&`kernel'==1 {
|
||||
global j1max=1
|
||||
scalar `plusitems'=0
|
||||
scalar `Hjkmax'=-99
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`1'" in green " is the kernel of the first scale"
|
||||
}
|
||||
local nbitemsbon=0
|
||||
forvalues i=2/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE ITEM OF THE KERNEL*/
|
||||
if ((`loevHjks'[`i',$j1max]<`minvalue'|`pvalHjk'[`i',$j1max]>`p')&`dim'[1,`i']==0) {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`nbitemsbon'
|
||||
if "`bon'"==""&"`test'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=2/`nbitems' {/*WE SEARCH THE BEST ITEM TO SELECT WITH THE KERNEL*/
|
||||
if `Hjk'[`j2',1]>`Hjkmax'&`pvalHjk'[`j2',1]<`pbon' {
|
||||
scalar `Hjkmax'=`Hjk'[`j2',1]
|
||||
global j2max=`j2'
|
||||
}
|
||||
}
|
||||
|
||||
if `Hjkmax'==-99 {/*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<*/
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di as green "{p}None item associated to the item " in yellow "$j1 " in green "allows obtaining a significantly positive value for the Hjk coefficient.{p_end}"
|
||||
}
|
||||
else {
|
||||
di as green "None item associated to the item " in yellow "$j1 " in green "allows obtaining a significantly positive value for the Hjk coefficient."
|
||||
}
|
||||
}
|
||||
continue, break
|
||||
}
|
||||
|
||||
if `Hjkmax'<=`c' { /*IF NONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk<c*/
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di as green "{p}None index Hjk associated to the item " in yellow "$j1 " in green "verifies Hjk>`c', the maximum value of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed.{p_end}"
|
||||
}
|
||||
else {
|
||||
di "None index Hjk associated to the item " in yellow "$j1 " in green "verifies Hjk>`c', the maximum value"
|
||||
di "of these coefficients is " %6.4f `Hjkmax' ". None scale can be constructed."
|
||||
}
|
||||
}
|
||||
scalar `stop'=1
|
||||
scalar `stopscale'=1
|
||||
continue, break
|
||||
}
|
||||
else { /* IF AT LEAST ONE ITEM CAN BE SELECTED WITH THE KERNEL Hjk>c*/
|
||||
matrix `dim'[1,$j1max]=$scale
|
||||
matrix `dim'[1,$j2max]=$scale
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
global `scaletmp' "`$j2max' `$j1max'"
|
||||
global `scaletmpnum' "$j2max $j1max"
|
||||
if "`details'"=="" {
|
||||
di in green "The second item selected in the first scale is " in yellow "`$j2max' " in green "(Hjk=" in yellow %6.4f `Hjkmax' in green")"
|
||||
}
|
||||
scalar `nbitemsel'=2
|
||||
}
|
||||
forvalues i=1/`nbitems' { /*WE EXCLUDE THE ITEM WHICH VERIFY Hjk<0 WITH THE NEW SELECTED ITEM*/
|
||||
if (`loevHjks'[`i',$j2max]<`minvalue'|`pvalHjk'[`i',$j2max]>`p')&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
/****FIRST SCALE, KERNEL OF SEVERAL ITEMS****/
|
||||
if $scale==1&`kernel'>=2 {
|
||||
global scale1
|
||||
local scalenum1
|
||||
local kbon=1
|
||||
local pbon=`p'
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
forvalues j2=1/`kernel' {
|
||||
global scale1 ``j2'' $scale1
|
||||
global scalenum1 $scalenum1 `j2'
|
||||
matrix `dim'[1,`j2']=1
|
||||
}
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di in green "{p}The kernel of the first scale is composed of the items " in yellow "$scale1{p_end}"
|
||||
}
|
||||
else {
|
||||
di in green "The kernel of the first scale is composed of the items " in yellow "$scale1"
|
||||
}
|
||||
}
|
||||
scalar `nbitemsel'=`kernel'
|
||||
forvalues j=1/`kernel' {
|
||||
forvalues i=1/`nbitems' { /* WE EXCLUDE THE ITEMS WHICH VERIFY Hjk<0 WITH THE ITEMS OF THE KERNEL*/
|
||||
if (`loevHjks'[`i',`j']<`minvalue'|`pvalHjk'[`i',`j']>`p')&`dim'[1,`i']==0 {
|
||||
matrix `dim'[1,`i']=-1
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
local excluded `excluded' ``i''
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
scalar `stop'=0
|
||||
|
||||
/**********************************************************************************************************
|
||||
END OF THE INITIAL STEP
|
||||
**********************************************************************************************************/
|
||||
|
||||
while `stop'!=1 { /*WHILE THE PROCEDURE TO CONSTRUCT THE ACTUAL SCALE IS NOT STOPPED*/
|
||||
|
||||
scalar `Hjmax'=-99
|
||||
scalar `Hmax'=-99
|
||||
global jmax=0
|
||||
|
||||
global stopmax=0
|
||||
local nbitemsbon=0
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==0 {
|
||||
local nbitemsbon=`nbitemsbon'+1
|
||||
}
|
||||
}
|
||||
local kbon=`kbon'+`nbitemsbon'
|
||||
if "`bon'"=="" {
|
||||
local pbon=`p'/`kbon'
|
||||
}
|
||||
else {
|
||||
local pbon=`p'
|
||||
}
|
||||
if "`details'"==""&"`test'"=="" {
|
||||
di in green "Significance level: " in yellow %8.6f `pbon'
|
||||
}
|
||||
|
||||
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {/*IF THE ITEM J0 IS UNSELECTED*/
|
||||
global stopmax=1
|
||||
local scaletmp="scale$scale"
|
||||
local scaletmpnum="scalenum$scale"
|
||||
qui loevh ``j0'' $`scaletmp' ,`pairwise' pair `adjust' html(`html')
|
||||
tempname pvalHj0
|
||||
matrix `pvalHj0'=r(pvalHj)
|
||||
scalar `pvalHj0'=`pvalHj0'[1,1]
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
local nbitsc : word count $`scaletmp'
|
||||
local nbitsc=`nbitsc'+1
|
||||
if `loevHj'[1,1]>`c'&`pvalHj0'<`pbon' {/*IF THE ITEM J0 CAN BE SELECTED*/
|
||||
if `loevH'>`Hmax' {/*AND IF IT IS THE BEST ITEM (COMPARED TO THE PRECEEDING ITEMS)*/
|
||||
scalar `Hjmax'=`loevHj'[1,1]
|
||||
scalar `Hmax'=`loevH'
|
||||
global j="``j0''"
|
||||
global j0=`j0'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if $stopmax==1&`Hjmax'==-99 { /*IF THERE IS ITEMS REMAINING BUT NONE OF THEM CAN BE SELECTED*/
|
||||
|
||||
if "`details'"=="" {
|
||||
if "`html'" == "" {
|
||||
di in green "{p}None new item can be selected in the scale $scale because all the Hj are less than `c' or none new item has all the related Hjk coefficients significantly greater than 0{p_end}."
|
||||
}
|
||||
else {
|
||||
di "None new item can be selected in the scale $scale because all the Hj are less than `c' or none new item"
|
||||
di "has all the related Hjk coefficients significantly greater than 0."
|
||||
}
|
||||
}
|
||||
scalar `stop'=1
|
||||
continue,break
|
||||
}
|
||||
if $stopmax==0 { /*IF THERE IS NO MORE ITEM REMAINING*/
|
||||
if "`details'"=="" {
|
||||
di in green "{p}There is no more items remaining.{p_end}"
|
||||
}
|
||||
scalar `stopscale'=1
|
||||
scalar `stop'=1
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
scalar `stopscale'=0
|
||||
}
|
||||
}
|
||||
*global scale=$scale-1
|
||||
continue,break
|
||||
}
|
||||
|
||||
|
||||
if `stop'!=1 { /*IF THE PROCEDURE IS NOT STOPPED*/
|
||||
matrix `dim'[1,$j0]=$scale
|
||||
local `scaletmp'="scale$scale"
|
||||
local `scaletmpnum'="scalenum$scale"
|
||||
global `scaletmp' $j $`scaletmp'
|
||||
global `scaletmpnum' $j0 $`scaletmpnum'
|
||||
if "`details'"=="" {
|
||||
di in green "The item " in yellow "`$j0' " in green "is selected in the scale " in yellow "$scale" _col(50) in green "Hj=" in yellow %6.4f `Hjmax' _col(65) in green "H=" in yellow %6.4f `Hmax' ""
|
||||
}
|
||||
local excluded
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']==-1 {
|
||||
matrix `dim'[1,`i']=-2
|
||||
}
|
||||
if `dim'[1,`i']==0 { /*WE EXCLUDE ITEMS WHO HAVE A NEGATIVE Hjk WITH THE NEW SELECTED ITEM*/
|
||||
if `loevHjks'[`i',$j0]<`minvalue'|`pvalHjk'[`i',$j0]>`p' {
|
||||
matrix `dim'[1,`i']=-1
|
||||
local excluded `excluded' ``i''
|
||||
}
|
||||
}
|
||||
}
|
||||
if "`excluded'"!=""&"`details'"=="" {
|
||||
di in green "The following items are excluded at this step: " in yellow "`excluded'"
|
||||
}
|
||||
}
|
||||
}
|
||||
di
|
||||
local scaleencours="scale$scale"
|
||||
local scalenumencours="scalenum$scale"
|
||||
local nbitemscale : word count $`scaleencours'
|
||||
return scalar nbitems`dimension'=`nbitemscale'
|
||||
if `nbitemscale'>0 { /* IF AT LEAST TWO ITEMS HAVE BEEN SELECTED*/
|
||||
if "`details'"!="" {
|
||||
di
|
||||
di in yellow "Scale: $scale"
|
||||
di "{hline 10}"
|
||||
}
|
||||
|
||||
loevh $`scaleencours',`pairwise' `adjust' html(`html')
|
||||
matrix `loevHjk'=r(loevHjk)
|
||||
matrix `loevHj'=r(loevHj)
|
||||
scalar `loevH'=r(loevH)
|
||||
|
||||
return scalar H`dimension'=`loevH'
|
||||
return local scale`dimension' $`scaleencours'
|
||||
return local scalenum`dimension' $`scalenumencours'
|
||||
local j=`nbitemscale'
|
||||
di
|
||||
}
|
||||
forvalues i=1/`nbitems' {
|
||||
if `dim'[1,`i']<0 {
|
||||
matrix `dim'[1,`i']=0
|
||||
}
|
||||
}
|
||||
|
||||
local restnbitems=0
|
||||
forvalues j0=1/`nbitems' {
|
||||
if `dim'[1,`j0']==0 {
|
||||
local restnbitems=`restnbitems'+1
|
||||
local restitem ``j0''
|
||||
}
|
||||
}
|
||||
|
||||
if `restnbitems'==1 { /*IF THERE IS ONLY ONE ITEM REMAINING*/
|
||||
di
|
||||
if "`html'" == ""{
|
||||
di in green "{p}There is only one item remaining (" in yellow "`restitem'" in green ").{p_end}"
|
||||
}
|
||||
else {
|
||||
di in green "There is only one item remaining (" in yellow "`restitem'" in green ")."
|
||||
}
|
||||
local stopscale=1
|
||||
return local lastitem "`restitem'"
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return scalar dim=$scale
|
||||
matrix colnames `dim'=`varlist'
|
||||
return matrix selection=`dim'
|
||||
|
||||
qui use "`mspfile'",clear
|
||||
end
|
||||
Reference in New Issue
Block a user