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************************************************************************************************************
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* Stata program : mmsrm
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* Estimate the parameters of the Multidimensional Marginally Sufficient Rasch Model (MMSRM)
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* Release 2 : May 26, 2004
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*
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* Historic :
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* Version 1 (May 14, 2004) [Jean-Benoit Hardouin]
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*
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* Jean-benoit Hardouin, Regional Health Observatory of Orl<72>ans - France
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* jean-benoit.hardouin@neuf.fr
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*
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* Use the Stata programs raschtest and gammasym who can be download on http://anaqol.free.fr
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* Use the Stata program gllamm who can be obtained by : ssc install gllamm
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* News about this program :http://anaqol.free.fr
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*
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* All the necessary programs on the FreeIRT Project website : http://freeirt.free.fr
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*
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* Copyright 2004 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 mmsrm,eclass
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version 8.0
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syntax varlist(min=3 numeric) [, PARTition(numlist) NODETails TRAce]
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preserve
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local nbitems : word count `varlist'
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if "`part'"=="" {
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local part=`nbitems'
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}
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local nbpart:word count `partition'
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if `nbpart'== 1 {
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raschtest `varlist', notest mml
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}
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else if `nbpart'>2 {
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di in red "The mmsrm module do not run models with more than two dimensions for the moment."
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}
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if `nbpart'!=2 {
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exit
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}
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local comptitems=0
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tokenize `varlist'
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forvalues i=1/`nbpart' {
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local firstpart`i'=`comptitems'+1
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local part`i': word `i' of `partition'
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local set`i'
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local comptitems=`comptitems'+`part`i''
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forvalues j=`firstpart`i''/`comptitems' {
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local set`i' "`set`i'' ``j''"
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}
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}
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if `comptitems'<`nbitems' {
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di in error "Your partition describe less items than the number of items defined in the varlist."
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di in error "Correct your code."
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exit
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}
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if `comptitems'>`nbitems' {
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di in error "Your partition describe more items than the number of items defined in the varlist."
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di in error "Correct your code."
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exit
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}
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forvalues i=1/`nbpart' {
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if "`details'"=="" {
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di in green "Estimation of the difficulty parameters of the dimension `i'."
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}
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*set trace on
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if `part`i''>1 {
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qui raschtest `set`i'',mml notest
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tempname beta`i' Varbeta`i'
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matrix `beta`i''=e(beta)
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matrix `Varbeta`i''=e(Varbeta)
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local sigma`i'=e(sigma)
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forvalues j=1/`part`i'' {
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local parambeta`=`firstpart`i''+`j'-1'=`beta`i''[1,`j']
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}
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}
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else {
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qui count
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local N=r(N)
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qui count if ``firstpart`i'''==1
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local pos=r(N)
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local parambeta`firstpart`i''=-log(`pos'/(`N'-`pos'))
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local sigma`i'=0
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}
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}
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if "`details'"=="" {
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di
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di in green "Estimation of the parameters of the distribution of the multidimensional latent trait."
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di in green "This process could be long to run. Be patient !"
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}
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*set trace on
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tempfile savemmsrm
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qui save `savemmsrm'
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keep `varlist'
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tempname rep id item offset
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forvalues i=1/`nbitems' {
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rename ``i'' `rep'`i'
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}
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gen `id'=_n
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qui reshape long `rep', i(`id') j(`item')
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gen `offset'=0
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label variable `offset' "offset"
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forvalues i=1/`nbitems' {
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qui replace `offset'=-`parambeta`i'' if `item'==`i'
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}
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local eqs
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forvalues i=1/`nbpart' {
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tempname B`i'
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gen `B`i''=0
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eq sc`i':`B`i''
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local eqs `eqs' sc`i'
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forvalues j=`firstpart`i''/`=`firstpart`i''+`part`i''-1' {
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qui replace `B`i''=1 if `item'==`j'
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}
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}
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label variable `rep' "response"
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label variable `id' "identifiant"
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tempname first
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local four=substr("`id'",1,3)
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matrix define `first'=(0,`sigma1',`sigma2',0)
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matrix colnames `first'=`rep':_cons `four'1_1:`B1' `four'1_2:`B2' `four'1_2_1:_cons
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if "`trace'"!="" {
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local quigllamm
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}
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else {
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local quigllamm qui
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}
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`quigllamm' gllamm `rep', from(`first') link(logit) fam(bin) i(`id') offset(`offset') nrf(`nbpart') eqs(`eqs') nip(5) dots `trace'
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local AIC=-2*e(ll)+2*(`nbitems'+`nbpart'*(`nbpart'+1)/2)
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local ll=e(ll)
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tempname cosig varsig L M
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matrix `cosig'=e(b)
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matrix `varsig'=e(V)
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matrix `L'=e(chol)
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matrix `M'=`L'*`L''
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forvalues i=1/`nbpart'{
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}
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if `nbpart'==2 {
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local var1=`M'[1,1]
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local var2=`M'[2,2]
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local sevar1=(sqrt(`varsig'[2,2])*2*sqrt(`var1'))
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local sevar2=.
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local cov12=`M'[1,2]
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local secov=.
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di
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di in green _col(4) "Log-likelihood:" in yellow %-12.4f `ll'
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di
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noi di in green _col(4) "Items" _col(16) "Parameters" _col(29) "std Err."
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di in green _col(4) "{hline 33}"
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forvalues i=1/`part1' {
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if `part1'!=1 {
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noi di in yellow _col(4) "``i''" _col(18) %8.5f `beta1'[1,`i'] _col(30) %6.5f sqrt(`Varbeta1'[`i',`i'])
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}
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else {
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noi di in yellow _col(4) "``i''" _col(18) %8.5f `parambeta`firstpart1'' _col(30) "."
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}
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}
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di in green _col(4) "{hline 33}"
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forvalues i=`firstpart2'/`=`part1'+`part2'' {
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if `part2'!=1 {
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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''])
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}
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else {
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noi di in yellow _col(4) "``i''" _col(18) %8.5f `parambeta`firstpart2'' _col(30) "."
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}
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}
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di in green _col(4) "{hline 33}"
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forvalues i=1/`nbpart' {
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noi di in yellow _col(4) "Var`i'" _col(18) %8.5f `var`i'' /*_col(30) %6.5f `sevar`i''*/
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}
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di in green _col(4) in yellow "cov12" _col(18) %8.5f `cov12' /*_col(30) %6.5f `secov'*/
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di in green _col(4) "{hline 33}"
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}
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if "`trace'"=="" {
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di in green "Add the trace option to obtain the standard errors of the elements"
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di in green "of the covariance matrix of the latent traits"
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}
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ereturn clear
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ereturn scalar AIC=`AIC'
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ereturn scalar ll=`ll'
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ereturn scalar dimension=`nbpart'
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forvalues i=1/`nbpart' {
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ereturn scalar nbitems`i'=`part`i''
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ereturn local set`i' `set`i''
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if `part`i''>1 {
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ereturn matrix beta`i'=`beta`i''
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ereturn matrix Varbeta`i' `Varbeta`i''
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}
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else {
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ereturn scalar beta`i'=`parambeta`firstpart`i'''
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}
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}
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*ereturn matrix cosig=`cosig'
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*ereturn matrix varsig=`varsig'
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tempname matrixsigma
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matrix `matrixsigma'=(`sigma1',`sigma2',`cov12')
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matrix colnames `matrixsigma'= sigma1 sigma2 cov12
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ereturn matrix sigma=`matrixsigma'
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drop _all
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qui use `savemmsrm'
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end
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