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246 lines
7.8 KiB
Plaintext
246 lines
7.8 KiB
Plaintext
*! Version 2.2 23october2013
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************************************************************************************************************
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* Stata program : pcm
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* Estimate the parameters of the Partial Credit Model
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* Version 1 : December 17, 2007
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* Version 2 : July 15, 2011
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* Version 2.1 : October 18th, 2011 : -fixedvar- option, new presentation
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* Version 2.2 : October 23rd, 2013 : correction of -fixedvar- option
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*
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* Jean-benoit Hardouin, EA4275 Biostatistics, Clinical Research and Subjective Measures in Health Sciences
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* Faculties of Pharmaceutical Sciences & Medicine - University of Nantes - France
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* jean-benoit.hardouin@univ-nantes.fr
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*
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* News about this program : http://www.anaqol.org
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* FreeIRT Project : http://www.freeirt.org
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*
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* Copyright 2007, 2011 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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program define pcm,eclass
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version 8.0
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syntax varlist(min=3 numeric) [if] [in] [,rsm fixed(string) fixedvar(real -1) fixedmu short COVariates(varname)]
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preserve
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tempfile pcmfile
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qui save `pcmfile',replace
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if "`fixedmu'"!=""&`fixedvar'!=-1&"`covariates'"=="" {
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di in red "You cannot fix in the same time the mean (fixedmu option) and the variance (fixedvar option) of the latent trait without covariables"
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error 184
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}
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if "`fixed'"!=""&"`fixedmu'"==""&`fixedvar'!=-1&"`covariates'"=="" {
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di in red "You cannot fix in the same time the difficulties (fixed option) and the variance (fixedvar option) of the latent trait without covariables"
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error 184
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}
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/*******************************************************************************
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ESTIMATION OF THE PARAMETERS
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********************************************************************************/
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marksample touse
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qui keep if `touse'
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qui count
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local N=r(N)
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tokenize `varlist'
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local nbitems : word count `varlist'
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if "`rsm'"=="" {
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di in gr "Model: " in ye "Partial Credit Model"
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}
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else {
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di in gr "Model: " in ye "Rating Scale Model"
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}
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tempname one var w id item it obs x chosen d score
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qui gen `one'=1
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qui gen `id'=_n
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local modamax=0
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forvalues i=1/`nbitems' {
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qui rename ``i'' `var'`i'
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qui su `var'`i'
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local moda`i'=`r(max)'
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if `modamax'<`r(max)' {
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local modamax=r(max)
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}
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}
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qui genscore `var'1-`var'`nbitems' ,score(`score')
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qui collapse (sum) `w'=`one',by(`var'1-`var'`nbitems' `covariates')
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qui gen `id'=_n
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qui reshape long `var',i(`id') j(`item')
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qui drop if `var'==.
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qui gen `obs'=_n
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qui expand `=`modamax'+1'
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qui sort `id' `item' `obs'
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by `obs', sort: gen `x'=_n-1
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qui gen `chosen'=`var'==`x'
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qui tab `item', gen(`it')
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forvalues i=1/`nbitems' {
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forvalues g=1/`modamax' {
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qui gen `d'`i'_`g'=-1*`it'`i'*(`x'>=`g')
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}
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}
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qui rename `w' `w'2
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bysort `id':egen score=sum(`x'*`chosen')
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qui su score
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local maxscore=r(max)
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if "`covariates'"!="" {
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qui gen covw=`covariates'*`x'
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local listcov covw
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}
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else {
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local listcov
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}
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if `fixedvar'!=-1 {
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local tmp=sqrt(`fixedvar')
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constraint 1 `x'=`tmp'
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local listconstr "constraints(1)"
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}
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if "`rsm'"=="" {
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if "`fixed'"!="" {
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qui gen offset=0
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local l=1
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forvalues i=1/`nbitems' {
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forvalues mi=1/`moda`i'' {
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qui replace offset=offset+`fixed'[1,`l']*`d'`i'_`mi'
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local ++l
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}
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}
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if "`fixedmu'"!="" {
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local mu
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}
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else {
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local mu "`x'"
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}
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eq slope:`x'
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noi di "gllamm `x' `listcov' `mu',offset(offset) `listconstr' nocons i(`id') eqs(slope) link(mlogit) expand(`obs' `chosen' o) weight(`w') adapt trace"
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gllamm `x' `listcov' `mu',offset(offset) `listconstr' nocons i(`id') eqs(slope) link(mlogit) expand(`obs' `chosen' o) weight(`w') adapt trace
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}
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else if "`short'"!="" {
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eq slope:`x'
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qui gllamm `x' `d'1_1-`d'`nbitems'_`modamax',i(`id') eqs(slope) link(mlogit) expand(`obs' `chosen' o) weight(`w') adapt trace nocons init
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tempname bsave Vsave
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matrix `bsave'=e(b)
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matrix `Vsave'=e(V)
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restore
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qui pcm `varlist' , fixed(`bsave')
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}
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else {
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di "no short"
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eq slope:`x'
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qui gen i=`id'
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constraint 1 `x'=1
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gllamm `x' `d'1_1-`d'`nbitems'_`modamax' `listcov',i(i) `listconstr' eqs(slope) link(mlogit) expand(`obs' `chosen' o) weight(`w') adapt trace nocons
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}
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}
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else {
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tempname step n
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forvalues i=2/`modamax' {
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qui gen `step'`i'=-1*(`x'>=`i')
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}
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forvalues i=1/`nbitems' {
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qui gen `n'`var'`i'=(-1)*(`it'`i')*(`x')
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}
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qui sort `id' `item' `x'
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eq slope:`x'
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gllamm `x' `n'`var'1-`n'`var'`nbitems' `step'2-`step'`modamax' `listcov', i(`id') `listconstr' eqs(slope) link(mlogit) expand(`obs' `chosen' o) weight(`w') adapt trace nocons
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}
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tempname b V chol
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matrix b=e(b)
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matrix V=e(V)
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local ll=e(ll)
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matrix chol=e(chol)
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if "`rsm'"=="" {
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di
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di in gr "Number of observations: " in ye `N'
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di in gr "Number of items: " in ye `nbitems'
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di in gr "Number of parameters: " in ye `=`nbitems'*`modamax'+1'
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di in gr "Log-likelihood: " in ye `ll'
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di
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di
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di in gr "{hline 100}"
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di in gr "Item" _col(10) "Modality" _col(20) "Parameter" _col(30) "Std Error"
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di in gr "{hline 100}"
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if "`fixed'"=="" {
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forvalues i=1/`nbitems' {
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forvalues j=1/`modamax' {
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if `j'==1 {
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di in ye "``i''" _cont
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}
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local k=(`i'-1)*`modamax'+`j'
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if "`short'"!="" {
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di in ye _col(17) `j' _col(20) %9.6f `bsave'[1,`k'] in ye _col(30) %9.6f (`Vsave'[`k',`k'])^.5
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}
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else {
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di in ye _col(17) `j' _col(20) %9.6f b[1,`k'] in ye _col(30) %9.6f (V[`k',`k'])^.5
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}
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}
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di in gr "{dup 100:-}"
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}
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}
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else {
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forvalues i=1/`nbitems' {
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forvalues j=1/`modamax' {
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if `j'==1 {
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di in ye "``i''" _cont
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}
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local k=(`i'-1)*`modamax'+`j'
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di in ye _col(17) `j' _col(20) %9.6f `fixed'[1,`k'] in ye _col(32) "(fixed)"
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}
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di in gr "{dup 100:-}"
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}
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}
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if "`fixed'"==""&"`short'"=="" {
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local k=`nbitems'*`modamax'+1
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}
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else if "`fixed'"!=""&"`fixedmu'"=="" {
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di in ye "Mu" in ye _col(20) %9.6f b[1,1] _col(29) %10.6f (V[1,1])^.5
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local k=2
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}
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else if "`fixed'"!=""&"`fixedmu'"!="" {
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di in ye "Mu" in ye _col(20) %9.6f 0 _col(32) %10.6f "(fixed)"
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local k=1
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}
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else {
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local k=1
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}
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if "`covariates'"!="" {
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di in ye "`covariates'" in ye _col(20) %9.6f b[1,`k'] _col(29) %10.6f (V[`k',`k'])^.5
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local k=`k'+1
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}
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if `fixedvar'==-1 {
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di in ye "Sigma" in ye _col(20) %9.6f b[1,`k'] _col(29) %10.6f (V[`k',`k'])^.5
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di in ye "Variance" in ye _col(20) %9.6f b[1,`k']^2 _col(29) %10.6f 2*(V[`k',`k'])^.5*b[1,`k']
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}
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else {
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di in ye "Sigma" in ye _col(20) %9.6f `fixedvar'^.5 _col(32) %10.6f "(fixed)"
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di in ye "Variance" in ye _col(20) %9.6f `fixedvar' _col(32) %10.6f "(fixed)"
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}
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di in gr "{hline 100}"
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di
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di
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}
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end
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