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194 lines
6.3 KiB
Plaintext
194 lines
6.3 KiB
Plaintext
*! version 1 january 25th, 2010
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*! Jean-Benoit Hardouin
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************************************************************************************************************
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* raschpower: Estimation of the power of the Wald test in order to compare the means of the latent trait in two groups of individuals
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*
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* Version 1 : January 25, 2010 (Jean-Benoit Hardouin)
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*
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* Jean-benoit Hardouin, Faculty of Pharmaceutical Sciences - 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 2010 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 raschpower1,rclass
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syntax [varlist] [, n0(int 100) n1(int 100) gamma(real .5) d(string) var(real 1) nodes(int 12)]
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if "`d'"=="" {
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tempname d
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matrix `d'=[-1\-.5\0\.5\1]
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}
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/*tempname abs weight
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ghquadm `nodes' `abs' `weight'
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matrix `abs'=`abs''
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matrix `weight'=`weight''
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matrix `abs'=`abs'*sqrt(`var')
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*matrix list `abs'
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matrix `abs'=[5.50090170446774,4.27182584793228,3.22370982877010,2.25946445100080,1.34037519715162,0.444403001944139,-5.50090170446774,-4.27182584793228,-3.22370982877010,-2.25946445100080,-1.34037519715162,-0.444403001944139]
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matrix `abs'=`abs''*sqrt(`var')
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matrix `weight'=[0.000000375975985,0.000121250244966,0.005523056331147,0.072984713184739,0.368391758069477,0.806292983509187,0.000000375975985,0.000121250244966,0.005523056331147,0.072984713184739,0.368391758069477,0.806292983509187]
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matrix `weight'=`weight''
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*matrix list `abs'
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*/
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local nbitems=rowsof(`d')
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di in gr "Number of individuals in the first group: " in ye `n0'
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di in gr "Number of individuals in the second group: " in ye `n1'
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di in green "Group effect: " in ye `gamma'
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di in gr "Variance of the latent trait: " in ye `var'
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di in gr "Number of items: " in ye `nbitems'
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di in green "Difficulties parameters of the items: " _c
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tempname dd
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matrix `dd'=`d''
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matrix list `dd',noblank nohalf nonames noheader
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clear
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local temp=2^(`nbitems')
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qui range x 0 `=`temp'-1' `temp'
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qui g t=x
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loc i=1
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qui count if t>0
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loc z=r(N)
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qui while `z'>0 {
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qui g item`i'=mod(t,2^`i')==2^`=`i'-1'
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qui replace t=t-item`i'*2^`=`i'-1'
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qui count if t>0
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loc z=r(N)
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loc i=`i'+1
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}
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drop t
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qui expand 2
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qui gen group=0 in 1/`temp'
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qui replace group=1 in `=`temp'+1'/`=2*`temp''
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qui gen mean=(-1)^(1-group)*`gamma'*sqrt(`var')/2
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qui gen proba=1
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forvalues i=1/`nbitems' {
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qui gen eps`i'=exp(mean-`d'[`i',1])
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qui replace proba=proba*eps`i'^item`i'/(1+eps`i')
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}
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qui gen eff=.
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forvalues i=0/1 {
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qui replace eff=proba*`n`i'' if group==`i'
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}
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forvalues i=1/`nbitems' {
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qui gen f`i'=eps`i'^item`i'/(1+eps`i')
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qui gen fp`i'=(item`i'*eps`i'^item`i'+(item`i'-1)*eps`i'^(item`i'+1))/(1+eps`i')^2
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qui gen fpp`i'=((item`i'^2*eps`i'^item`i'+(item`i'^2-1)*eps`i'^(item`i'+1))*(1+eps`i')^2-2*eps`i'*(1+eps`i')*(item`i'*eps`i'^item`i'+(item`i'-1)*eps`i'^(item`i'+1)))/(1+eps`i')^4
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}
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qui replace eff=proba
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keep item* eff group proba
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local p1=1/`n1'
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local p0=1/`n0'
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qui gen eff2=.
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qui replace eff2=floor(eff/`p1') if group==1
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qui replace eff2=floor(eff/`p0') if group==0
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qui replace eff=eff-eff2*(`p1'*group+`p0'*(1-group))
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qui su eff2 if group==1
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local aff1=r(sum)
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qui su eff2 if group==0
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local aff0=r(sum)
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*di "Nombre de patients affectes : `aff1' dans groupe 1 (sur `n1') et `aff0' dans le groupe 0 (sur `n0')"
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local unaff1=`n1'-`aff1'
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local unaff0=`n0'-`aff0'
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*di "Nombre de patients non affectes : `unaff1' dans groupe 1 (sur `n1') et `unaff0' dans le groupe 0 (sur `n0')"
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qui gsort + group - eff
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qui replace eff2=eff2+1 in 1/`unaff0'
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qui gsort - group - eff
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qui replace eff2=eff2+1 in 1/`unaff1'
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*list eff eff2 group proba
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qui drop if eff2==0
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qui expand eff2
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qui gen i=_n
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tempname diff
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matrix `diff'=`d''
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/***************************A REVOIR
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forvalues i=1/`nbitems' {
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qui su item`i'
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local var=r(Var)
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if `var'==0 {
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qui drop item`i'
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}
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}
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****************************FIN A REVOIR*/
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*irtpoly item*, fixedvar(1) rasch fixed(`diff') covariablemean(group) sasout
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qui drop proba eff eff2
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qui reshape long item, i(i)
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qui rename item rep
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qui rename _j item
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qui gen offset=0
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forvalues i=1/`nbitems' {
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qui replace offset=-`diff'[1,`i'] if item==`i'
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}
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constraint 1 _cons=0
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qui gen groupc=group-.5
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xtlogit rep groupc ,nocons i(i) offset(offset) constraint(1)
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tempname b V
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matrix `b'=e(b)
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matrix `V'=e(V)
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local gammaest=`b'[1,1]
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local se=`V'[1,1]^.5
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di
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di
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di in gr "{hline 76}"
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di _col(50) "Estimation with the "
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di _col(40) "Cramer-Rao bound" _col(60) "classical formula"
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di in gr "{hline 76}"
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di in green "Estimated value of the group effect" _col(49) in ye %7.2f `gammaest'
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di in green "Standard Error of this estimation" _col(49) in ye %7.2f `se'
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di in green "Variance if this estimation" _col(46) in ye %10.4f `=`se'^2'
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local power=1-normal(1.96-`gamma'/`se')
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local clpower=normal(sqrt(`n0'*`gamma'^2/2)-1.96)
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di in green "Estimated value of the power" _col(50) in ye %6.4f `power' _col(71) in ye %6.4f `clpower'
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local clnsn=2/`gamma'^2*(1.96-invnorm(1-`power'))^2
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di in green "Number of patients for a power of" %6.2f `=`power'*100' "%" _col(49) in ye `n0' "/" `n1' _col(62) in ye %7.2f `clnsn' "/" %7.2f `clnsn'
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di in green "Ratio of the number of patients" in ye %6.2f _col(55)`=(`n0'+`n1')/(2*`clnsn')'
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di in gr "{hline 76}"
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return scalar EstGamma=`gammaest'
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return scalar CRbound=`=`se'^2'
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return scalar CRPower=`power'
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return scalar ClPower=`clpower'
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return scalar ClSS=`clnsn'
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return scalar Ratio=`=`n0'/`clnsn''
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end
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