Corrected code for getting logs
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@ -760,6 +760,113 @@ if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "
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//Variance et se mA
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matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
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*************************************************************
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***********************AFFICHAGE*****************************
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*************************************************************
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//Affichage modèle A
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di
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di as input "PROCESSING STEP A"
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di
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if "`detail'" != "" {
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/* Affichage des estimations des difficultés modèle A */
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di _col(5) as text "{ul:MODEL A:} Overall measurement non-invariance between groups"
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di
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di %~85s as text "Item difficulties: estimates (s.e.)"
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di _col(10) "{hline 65}"
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di _col(31) as text abbrev("`gp'",20) "=0" _col(57) abbrev("`gp'",20) "=1"
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di _col(10) "{hline 65}"
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forvalues j=1/`nbitems' {
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di as text _col(10) abbrev("``j''", 18)
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forvalues p=1/`nbdif_`j'' {
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di as text _col(10) "`p'" as result _col(30) %6.2f `delta`j'_`p'g0mA' %6.2f " (" %3.2f `delta`j'_`p'g0mA_se' ")" _col(56) %6.2f `delta`j'_`p'g1mA' " (" %3.2f `delta`j'_`p'g1mA_se' ")"
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}
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}
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di as text _col(10) "{hline 65}"
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/* Affichage des estimations sur le trait latent du modèle A */
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di
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di %~85s as text "Latent trait distribution"
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di _col(10) "{hline 65}"
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di _col(31) "Estimate" _col(57) "Standard error"
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di _col(10) "{hline 65}"
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di _col(10) "Variance" as result _col(31) %6.2f `=var_mA[1,1]' _col(55) %6.2f `=var_mA[2,1]'
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di _col(10) as text "Group effect" as result _col(31) "0 (constrained)"
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di _col(10) as text "{hline 65}"
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di
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di _col(10) as text "No group effect: equality of the latent trait means between groups"
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di _col(10) as text "All item difficulties are freely estimated in both groups"
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di
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}
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//*Affichage modèle B
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di
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di as input "PROCESSING STEP B"
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di
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/* Affichage des estimations des difficultés modèle B */
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if "`detail'" != "" {
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di _col(5) as text "{ul:MODEL B:} Overall measurement invariance between groups"
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di
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di %~85s as text "Item difficulties: estimates (s.e.)"
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di _col(10) "{hline 65}"
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di _col(31) abbrev("`gp'",20) "=0" _col(57) abbrev("`gp'",20) "=1"
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di _col(10) "{hline 65}"
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forvalues j=1/`nbitems' {
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di _col(10) as text "``j''"
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forvalues p=1/`nbdif_`j'' {
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di as text _col(10) "`p'" as result _col(30) %6.2f `delta`j'_`p'g0mB' " (" %3.2f `delta`j'_`p'g0mB_se' ")" _col(56) %6.2f `delta`j'_`p'g1mB' " (" %3.2f `delta`j'_`p'g1mB_se' ")"
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}
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}
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di _col(10) as text "{hline 65}"
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/* Affichage des estimations sur le trait latent du modèle B */
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di
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di %~85s as text "Latent trait distribution"
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di _col(10) "{hline 65}"
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di _col(28) "Estimate" _col(42) "Standard error" _col(62) "P-value"
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di _col(10) "{hline 65}"
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di _col(10) "Variance" as result _col(28) %6.2f `=var_mB[1,1]' _col(40) %6.2f `=var_mB[2,1]'
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di _col(10) as text "Group effect" as result _col(28) %6.2f `geffmB' _col(40) %6.2f `segeffmB' _col(62) %6.4f `gcmBp'
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di _col(10) as text "{hline 65}"
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di
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di _col(10) as text "Group effect estimated: mean of the latent trait of group 1 freely estimated"
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di _col(10) "Equality of the item difficulties between groups"
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di
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}
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*****************************************************
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* Modèle A vs Modèle B *
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*****************************************************
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qui lrtest modeldifA modeldifB
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local diftestp=r(p)
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local diftestchi=r(chi2)
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local diftestdf=r(df)
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//affichage lrtest
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di as input "LIKELIHOOD-RATIO TEST"
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di
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di %~60s "Model A vs Model B"
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di _col(10) "{hline 40}"
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di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
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di _col(10) as result %6.2f `diftestchi' _col(28) %2.0f `diftestdf' _col(40) %6.4f `diftestp'
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di _col(10) as text "{hline 40}"
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di
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if `diftestp'<0.05{
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di as result "DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS LIKELY"
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}
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else{
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di as result "NO DIFFERENCE BETWEEN GROUPS DETECTED"
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}
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*********************************
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*************MODEL C*************
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*********************************
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@ -787,15 +894,12 @@ if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "
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local nbsig=0
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local minpval=1
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local itemdif=0
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if "`detail'" != ""{
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di as text "Loop `boucle'"
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di as text _col(5) "Adjusted alpha: " %6.4f `pajust'
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di
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di as text _col(10) "{hline 65}"
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di as text _col(10) "Freed item" _col(31) "Chi-Square" _col(48) "DF" _col(57) "P-Value"
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di as text _col(10) "{hline 65}"
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}
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/*boucle de test*/
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forvalues j=1/`nbitems'{
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//if `nbdif_`j'' > 2 {
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@ -856,40 +960,32 @@ if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "
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local itemdif=`j'
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}
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}
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if "`detail'" != "" {
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di as text _col(10) abbrev("``j''",15) as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57) %6.4f test_dif_`boucle'[`j',3]
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}
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}
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}
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/*si nb de tests significatifs=0, on arrête*/
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if `nbsig'==0{
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local stop=1
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if `boucle' == 1 {
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if "`detail'" != "" {
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di as text _col(10) "{hline 65}"
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di
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di as result "No significant test: no difference between groups detected, no DIF detected"
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di
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}
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}
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else {
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if "`detail'" != ""{
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di as text _col(10) "{hline 65}"
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di
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di as result "No other significant tests"
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di
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}
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}
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}
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else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
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matrix dif_rc[`itemdif',1]=`boucle'
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if "`detail'" != ""{
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di as text _col(10) "{hline 65}"
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di
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di as result "Difference between groups on ``itemdif'' at time 1"
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}
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if `nbmoda_`itemdif'' > 2 {
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if "`detail'" != "" {
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di
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di %~60s as text "Test of uniform difference"
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@ -897,7 +993,7 @@ if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "
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di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
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di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
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di _col(10) as text "{hline 40}"
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}
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if test_difu_`boucle'[`itemdif',3]<0.05{ /*DIF NU détectée*/
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matrix dif_rc[`itemdif',2]=0
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di
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@ -8,11 +8,11 @@
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*
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*
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*================================================================================================================================================
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adopath+"/home/corentin/Documents/These/Recherche/ROSALI-SIM/Modules/rosali_custom"
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adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/rosali_custom"
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local N = "50 100 200 300"
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local ss = "1 2 3 4 5 6 7 8 9 10 11 1 13 14 15 16 17 18 19 20"
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local ss = "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20"
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foreach s in `ss' {
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foreach Nnn in `N' {
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local Nn = `Nnn'
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@ -21,6 +21,7 @@ local N = "50 100 200 300"
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local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N`Nn'"
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}
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local path_res = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/N`Nn'"
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local path_log = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/log/"
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local scenarios = "A B C D E F G"
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if (`s' <= 4) {
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local scenarios = "A B C D E"
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@ -29,7 +30,7 @@ local N = "50 100 200 300"
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clear
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import delim "`path_data'/scenario_`s'`scen'_`Nn'.csv", encoding(ISO-8859-2) case(preserve) clear
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rename TT tt
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log using "`path_log'/log_`s'`scen'_`Nn'.log", replace
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if (`s'<=2) {
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local nbitems=4
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}
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@ -63,9 +64,9 @@ local N = "50 100 200 300"
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local nbdif=3
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}
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* taillemat = Maximum J*M cases pour les items par et J*M cases pour les dif par + J cases pour les DIF detect + nbdif cases pour dif réel
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local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbdif'+2
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local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbdif'+2+1
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if (mod(`s',2)==0) {
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local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbitems'+`nbdif'+2
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local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbitems'+`nbdif'+2+1
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}
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local colna=""
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forvalues i=1/`nbitems' {
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@ -86,16 +87,20 @@ local N = "50 100 200 300"
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forvalues i=1/`nbdif' {
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local colna = "`colna'"+"real_dif_`i' "
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}
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local colna = "`colna'" + "beta " + "se_beta"
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local colna = "`colna'" + "beta " + "se_beta " + "lrt_passed"
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mat outmat = J(1000,`taillemat',.)
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mat colnames outmat= `colna'
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di "Scenario `s'`scen' / N=`Nnn'"
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forvalues k=1/1000 {
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if (mod(`k',100)==0) {
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di "`k'/1000"
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}
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forvalues k=1/10 {
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di "###################################################################################"
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di "###################################################################################"
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di "###################################################################################"
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di "Scenario `s'`scen' N=`Nn' ########## `k'/1000"
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di "###################################################################################"
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di "###################################################################################"
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di "###################################################################################"
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preserve
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qui keep if replication==`k'
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@ -253,10 +258,10 @@ local N = "50 100 200 300"
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* ROSALI
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qui rosali_original item1-item`nbitems' item1-item`nbitems', group(tt)
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rosali_original item1-item`nbitems' item1-item`nbitems', group(tt)
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qui mat resmat=r(difitems)
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local nbitems2 = 2*`nbitems'
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mat lrt_passed = resmat[1,`nbitems2'+1]
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* Calculer le nbre d'items détectés
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local nbdetect = 0
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local stop = 0
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@ -329,7 +334,7 @@ local N = "50 100 200 300"
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local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent"
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}
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*calcul du modèle
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qui `mod'
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`mod'
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mat V=r(table)
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mat W=V[1..2,1...]
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@ -372,8 +377,10 @@ local N = "50 100 200 300"
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}
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qui mat outmat[`k',colnumb(outmat,"beta")]=W[1,colnumb(W,"THETA:tt")]
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qui mat outmat[`k',colnumb(outmat,"se_beta")]=W[2,colnumb(W,"THETA:tt")]
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qui mat outmat[`k',colnumb(outmat,"lrt_passed")]=lrt_passed[1,1]
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restore
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}
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log close
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putexcel set "`path_res'/`s'`scen'_`Nn'_original.xls", sheet("outmat") replace
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putexcel A1=matrix(outmat), colnames
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}
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