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Commit 56f7d898 authored by Sagebiel's avatar Sagebiel
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working version from old repo only for infoprocessing

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Designs/bayefficient.ngd 0 → 100644
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View file @ 56f7d898
Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
1 1 2 2 1 1 0 -2 0 0.6 0.05 0.6 0 0 1 -2 1 0.12 2
1 2 0.5 0.3 0 0 1 0 0 0.12 0.2 3 1 1 0 -5 1 0.6 3
1 3 1 1 1 0 1 -5 0 0.6 0.1 1 0 1 0 0 1 0.9 4
1 4 2 0.3 1 1 0 -5 1 0.12 0.05 3 0 0 1 0 0 1.2 2
1 5 0.05 0.3 1 0 1 -2 1 0.12 2 3 0 1 0 -2 0 1.2 3
1 6 0.1 0.6 1 0 0 0 0 0.06 1 3 0 1 1 -5 1 0.06 1
1 7 0.05 3 1 1 1 0 1 0.6 2 0.3 0 0 0 -5 0 0.9 3
1 8 0.1 2 0 1 1 0 1 0.12 1 0.6 1 0 0 -5 0 0.6 4
1 9 2 0.3 1 1 1 -2 0 0.36 0.05 3 0 0 0 -2 1 0.9 4
1 10 0.5 3 1 1 0 -2 0 0.06 0.1 0.3 0 0 1 -2 1 0.06 4
1 11 0.05 2 0 1 0 -5 0 0.9 2 0.6 1 0 1 0 1 0.12 4
1 12 0.5 0.6 1 0 1 -2 0 1.2 0.2 2 0 1 0 -2 1 0.06 1
1 13 0.2 1 0 1 1 -5 1 1.2 0.5 1 1 0 0 0 0 0.36 3
1 14 1 2 1 1 1 -2 1 0.06 0.1 0.6 0 0 0 -2 0 1.2 2
1 15 1 3 0 0 1 -5 1 0.9 0.1 0.3 1 1 0 0 0 0.6 1
1 16 2 2 1 0 0 0 1 0.6 0.05 0.3 0 1 1 -5 0 0.9 4
1 17 2 1 0 1 0 0 1 0.36 0.05 1 1 0 1 -5 0 0.6 2
1 18 0.2 0.6 1 0 1 -2 1 0.9 0.5 2 0 1 0 -2 0 0.36 4
1 19 0.1 3 1 0 0 0 0 0.9 1 0.3 0 1 1 -5 1 0.36 3
1 20 0.05 0.3 1 1 0 -5 1 0.36 2 2 0 0 1 0 0 0.9 1
1 21 0.2 1 0 0 1 -5 1 0.12 0.5 1 1 1 0 0 0 0.9 2
1 22 0.1 3 0 1 1 -2 0 0.6 1 0.3 1 0 0 -2 1 0.06 3
1 23 1 3 1 0 1 -5 0 1.2 0.2 0.3 0 1 0 0 1 0.12 1
1 24 2 0.6 0 0 0 -5 1 0.9 0.05 1 1 1 1 0 0 0.6 4
1 25 0.5 1 0 1 1 0 0 0.36 0.2 1 1 0 0 -5 1 1.2 2
1 26 0.2 1 1 1 0 -2 1 1.2 0.5 1 0 0 1 -2 0 0.36 1
1 27 0.2 0.6 0 1 1 -2 1 0.12 0.5 2 1 0 0 -2 0 0.12 1
1 28 1 2 0 0 1 0 0 1.2 0.1 0.6 1 1 0 -5 1 0.12 1
1 29 0.1 0.6 0 1 0 0 0 0.6 1 2 1 0 1 -5 1 0.36 3
1 30 0.5 0.3 0 0 0 0 0 0.36 0.2 3 1 1 1 -5 1 1.2 2
1 31 0.05 0.3 0 0 0 -5 0 0.9 2 2 1 1 1 0 1 0.6 3
1 32 0.1 0.3 1 1 1 -5 0 0.06 1 3 0 0 0 0 1 0.36 3
1 33 0.5 1 0 0 0 0 1 0.06 0.2 0.6 1 1 1 -5 0 1.2 2
1 34 1 2 0 1 0 -5 0 0.36 0.1 0.6 1 0 1 0 1 0.06 1
1 35 0.05 3 1 0 0 -2 1 0.06 2 0.3 0 1 1 -2 0 0.06 2
1 36 0.2 0.6 0 0 0 -2 1 1.2 0.5 2 1 1 1 -2 0 0.12 4
||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
;block = 4
;eff = (mnl,d,mean)
;rep = 1000
;bdraws = halton(1000)
;bseed = 2333344
;rseed = 2333344
;con
;model:
U(alt1) = asc_gemeinschaft[(n,0.6,0.3)]+b_groesse[(n,0.07,0.04)]*groesse[0.05,0.1,0.2,0.5,1,2]+b_entfernung[(n,-0.23,0.15)]*entfernung[0.3,0.6,1,2,3]+b_gemeinschaft[(n,0.13,0.07)]*gemeinschaft[0,1]+b_kultur[(n,0.16,0.08)]*kultur[0,1]+b_umweltbildung[(n,0.14,0.07)]*umweltbildung[0,1]+b_zugang[(n,0.07,0.04)]*zugang[-5,-2,0]+b_gestaltung[(n,0.39,0.2)]*gestaltung[0,1]+b_beitrag[(n,1.15,0.6)]*beitrag[0.06,0.12,0.36,0.6,0.9,1.2]/
U(alt2) = asc_klein[(n,0.39,0.2)] +b_groesse *groesse[0.05,0.1,0.2,0.5,1,2]+b_entfernung *entfernung[0.3,0.6,1,2,3]+b_gemeinschaft *gemeinschaft[0,1]+b_kultur *kultur[0,1]+b_umweltbildung *umweltbildung[0,1]+b_zugang *zugang[-5,-2,0]+b_gestaltung *gestaltung[0,1]+b_beitrag *beitrag[0.06,0.12,0.36,0.6,0.9,1.2]
$
\ No newline at end of file
Designs/efficient.ngd 0 → 100644
+ 57
− 0
View file @ 56f7d898
Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
1 1 0.2 1 0 0 1 -2 1 1.2 0.5 1 1 1 0 -2 0 0.12 2
1 2 0.5 3 0 1 1 0 1 0.12 0.2 0.3 1 0 0 -5 0 0.6 4
1 3 0.05 2 1 1 0 0 1 0.36 2 0.3 0 0 1 -5 0 0.9 2
1 4 0.5 1 1 0 1 -2 1 0.9 0.2 1 0 1 0 -2 0 0.12 3
1 5 0.1 2 1 1 1 0 0 0.12 1 0.6 0 0 0 -5 1 0.06 3
1 6 1 3 0 0 1 -5 0 1.2 0.1 0.3 1 1 0 0 1 0.06 2
1 7 0.2 0.6 0 0 0 -2 0 0.06 0.5 3 1 1 1 -2 1 0.06 4
1 8 0.1 1 1 1 0 0 0 0.9 1 1 0 0 1 -5 1 0.36 4
1 9 2 0.3 1 1 1 -5 0 0.36 0.05 3 0 0 0 0 1 0.9 1
1 10 2 3 1 0 0 -5 1 0.9 0.05 0.3 0 1 1 0 0 0.36 4
1 11 0.05 2 0 0 0 -5 0 1.2 2 0.6 1 1 1 0 1 0.12 1
1 12 0.1 0.3 0 1 0 -5 0 0.06 1 3 1 0 1 0 1 0.06 2
1 13 0.05 0.3 0 1 1 -5 1 0.36 2 2 1 0 0 0 0 1.2 4
1 14 0.05 3 0 0 1 0 1 0.6 2 0.3 1 1 0 -5 0 0.6 2
1 15 1 3 1 1 1 -5 0 0.6 0.1 0.3 0 0 0 0 1 0.06 3
1 16 2 0.6 0 0 0 -2 1 0.12 0.05 2 1 1 1 -2 0 0.9 3
1 17 0.2 1 0 0 1 -2 0 0.12 0.5 0.6 1 1 0 -2 1 0.9 3
1 18 2 1 0 1 0 0 1 0.12 0.05 1 1 0 1 -5 0 1.2 3
1 19 1 0.6 1 0 1 -5 1 0.6 0.1 1 0 1 0 0 0 0.36 3
1 20 0.1 0.3 1 0 0 0 1 0.06 1 3 0 1 1 -5 0 1.2 1
1 21 2 0.3 0 1 0 -5 1 0.12 0.05 2 1 0 1 0 0 0.6 2
1 22 0.1 1 1 0 1 -5 1 0.36 1 1 0 1 0 0 0 1.2 4
1 23 0.2 1 0 1 1 -2 1 0.9 0.5 1 1 0 0 -2 0 0.12 1
1 24 0.5 2 1 0 0 -2 0 0.36 0.1 0.6 0 1 1 -2 1 0.6 2
1 25 0.5 0.6 0 0 0 0 0 0.9 0.2 2 1 1 1 -5 1 0.12 3
1 26 1 0.3 1 0 1 0 0 0.06 0.1 3 0 1 0 -5 1 0.9 3
1 27 2 3 1 1 1 0 0 0.6 0.05 0.3 0 0 0 -5 1 0.6 1
1 28 0.1 2 0 1 0 -5 0 1.2 1 0.6 1 0 1 0 1 0.06 2
1 29 1 3 0 0 0 0 0 1.2 0.1 0.3 1 1 1 -5 1 0.36 2
1 30 0.05 2 1 1 0 -5 1 0.6 2 0.6 0 0 1 0 0 0.6 4
1 31 0.2 0.6 1 1 0 -2 0 1.2 0.5 2 0 0 1 -2 1 0.12 4
1 32 0.05 0.3 1 0 1 -2 0 0.36 2 3 0 1 0 -2 1 0.9 1
1 33 0.2 0.6 0 1 1 -2 1 0.6 0.5 2 1 0 0 -2 0 0.36 1
1 34 0.5 2 1 1 0 -2 1 0.06 0.2 0.6 0 0 1 -2 0 1.2 1
1 35 1 0.3 0 1 1 0 0 0.06 0.2 3 1 0 0 -5 1 1.2 4
1 36 0.5 0.6 1 0 0 -2 1 0.9 0.2 2 0 1 1 -2 0 0.36 1
||||||||||
design
;alts = alt1*, alt2*, alt3
;rows = 36
;block = 4
;eff = (mnl,d)
;rep = 1000
;bseed = 2333344
;rseed = 2333344
;con
;model:
U(alt1) = asc_gemeinschaft[0.6]+b_groesse[0.07]*groesse[0.05,0.1,0.2,0.5,1,2]+b_entfernung[-0.23]*entfernung[0.3,0.6,1,2,3]+b_gemeinschaft[0.13]*gemeinschaft[0,1]+b_kultur[0.16]*kultur[0,1]+b_umweltbildung[0.14]*umweltbildung[0,1]+b_zugang[0.07]*zugang[-5,-2,0]+b_gestaltung[0.39]*gestaltung[0,1]+b_beitrag[1.15]*beitrag[0.06,0.12,0.36,0.6,0.9,1.2]/
U(alt2) = asc_klein[0.39]+b_groesse[0.07]*groesse[0.05,0.1,0.2,0.5,1,2]+b_entfernung[-0.23]*entfernung[0.3,0.6,1,2,3]+b_gemeinschaft[0.13]*gemeinschaft[0,1]+b_kultur[0.16]*kultur[0,1]+b_umweltbildung[0.14]*umweltbildung[0,1]+b_zugang[0.07]*zugang[-5,-2,0]+b_gestaltung[0.39]*gestaltung[0,1]+b_beitrag[1.15]*beitrag[0.06,0.12,0.36,0.6,0.9,1.2]
$
\ No newline at end of file
designfiles/seagri_designs.ngs 0 → 100644
+ 457
− 0
View file @ 56f7d898
? Orthogonal design
design
;alts = alt1, alt2, alt3
;rows = 24
;block = 4
;orth = seq
;con
;model:
U(alt1) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30] /
U(alt2) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30]
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Initiative to join the scheme' /
1,3 = 'Source of funding for the compensation' /
1,4 = 'Impact of forest damage on the carbon amount' /
1,5 = 'Amount of carbon compensation ' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.initiator>' /
2,3 = '<alt1.funding>' /
2,4 = '<alt1.damage>' /
2,5 = '<alt1.compensation>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.initiator>' /
3,3 = '<alt2.funding>' /
3,4 = '<alt2.damage>' /
3,5 = '<alt2.compensation>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt1.damage(0='not taken into account' , 1= 'taken into account') /
alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt2.damage(0='not taken into account' , 1= 'taken into account') /
alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
$
? efficient design
design
;alts = alt1, alt2, alt3
;rows = 36
;block = 4
;eff = (mnl,d)
;rep = 1000
;bseed = 2333344
;rseed = 2333344
;con
;model:
U(alt1) = b0[-0.4] + b1.dummy[0.1|-0.1] * Initiator[1,2,0] + b2.dummy[0.1|-0.1] * Funding[1,2,0] + b3[0.2] * Damage [0,1] + b4[0.015] * Compensation[2,5,10,15,20,30] /
U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Initiative to join the scheme' /
1,3 = 'Source of funding for the compensation' /
1,4 = 'Impact of forest damage on the carbon amount' /
1,5 = 'Amount of carbon compensation ' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.initiator>' /
2,3 = '<alt1.funding>' /
2,4 = '<alt1.damage>' /
2,5 = '<alt1.compensation>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.initiator>' /
3,3 = '<alt2.funding>' /
3,4 = '<alt2.damage>' /
3,5 = '<alt2.compensation>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt1.damage(0='not taken into account' , 1= 'taken into account') /
alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt2.damage(0='not taken into account' , 1= 'taken into account') /
alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
$
? Bayesian efficient design
design
;alts = alt1, alt2, alt3
;rows = 36
;block = 4
;eff = (mnl,d,mean)
;rep = 1000
;bdraws = halton(1000)
;bseed = 2333344
;rseed = 2333344
;con
;model:
U(alt1) = b0[(n,-0.4,0.3)] + b1.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Initiator[1,2,0] + b2.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Funding[1,2,0] + b3[(u,0,0.6)] * Damage [0,1] + b4[(u,0.05,0.2)] * Compensation[2,5,10,15,20,30] /
U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Initiative to join the scheme' /
1,3 = 'Source of funding for the compensation' /
1,4 = 'Impact of forest damage on the carbon amount' /
1,5 = 'Amount of carbon compensation ' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.initiator>' /
2,3 = '<alt1.funding>' /
2,4 = '<alt1.damage>' /
2,5 = '<alt1.compensation>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.initiator>' /
3,3 = '<alt2.funding>' /
3,4 = '<alt2.damage>' /
3,5 = '<alt2.compensation>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt1.damage(0='not taken into account' , 1= 'taken into account') /
alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
alt2.damage(0='not taken into account' , 1= 'taken into account') /
alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
$
U(alt1) = bm0 + b1 * Origin[0,1] + b2 * Eco[0,1] + bp1[0.02] * Pm[25:75:10]/
U(alt2) = bv0 + b1 * Origin + b2 * Eco + bp2 * Pv[15:85:10]/
U(alt3) = bb0 + b1 * Origin + b2 * Eco + bp3 * Pb[5:45:10]
??? Designs aus anderen projekten als vorlage Baysian Efficient Design 1
design
;alts = alt1, alt2, alt3, alt4
;rows = 24
;block = 4
;eff = (mnl,d,mean)
;rep = 1000
;bdraws = halton(1000)
;con
;model:
U(alt1) = b1[(n,-1.2,0.1)] + b2[(n,0.1,0.02)] * B[0,1] + b3[(n,0.4,0.1)] * C[0,1] + b4[(n,0.3,0.1)] * D[0,1] + b5[(n,0.02,0.001)] * P[35:83:6]/
U(alt2) = b6[(n,-1.4,0.1)] + b2 * B + b3 * C + b4 * D + b5 * P
?U(alt3) =
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Results' /
1,3 = 'Beratung' /
1,4 = 'Partner' /
1,5 = 'Kompensation' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.b>' /
2,3 = '<alt1.c>' /
2,4 = '<alt1.d>' /
2,5 = '<alt1.p>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.b>' /
3,3 = '<alt2.c>' /
3,4 = '<alt2.d>' /
3,5 = '<alt2.p>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.b(0=Results, 1=Action) /
alt1.c(0=Keine Beratung, 1=Mit Beratung) /
alt1.d(0=Keine Partner, 1=Mit Partner) /
alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
alt2.b(0=Results, 1=Action) /
alt2.c(0=Keine Beratung, 1=Mit Beratung) /
alt2.d(0=Keine Partner, 1=Mit Partner) /
alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
$
?Efficient Design
design
;alts = alt1*, alt2*, alt3
;rows = 36
;block = 3
;eff = (mnl,d)
;rep = 1000
;con
;model:
U(alt1) = b1[-1.2] + b2[0.1] * B[0,1] + b3[0.4] * C[0,1] + b4[0.3] * D[0,1] + b5[0.02] * P[35:83:6]/
U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
?U(alt3) =
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Results' /
1,3 = 'Beratung' /
1,4 = 'Partner' /
1,5 = 'Kompensation' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.b>' /
2,3 = '<alt1.c>' /
2,4 = '<alt1.d>' /
2,5 = '<alt1.p>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.b>' /
3,3 = '<alt2.c>' /
3,4 = '<alt2.d>' /
3,5 = '<alt2.p>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.b(0=Results, 1=Action) /
alt1.c(0=Keine Beratung, 1=Mit Beratung) /
alt1.d(0=Keine Partner, 1=Mit Partner) /
alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
alt2.b(0=Results, 1=Action) /
alt2.c(0=Keine Beratung, 1=Mit Beratung) /
alt2.d(0=Keine Partner, 1=Mit Partner) /
alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
$
? Orthogonal Design
design
;alts = alt1*, alt2*, alt3
;rows = 36
;block = 3
;orth = seq
;con
;model:
U(alt1) = b1 + b2 * B[0,1] + b3 * C[0,1] + b4 * D[0,1] + b5 * P[35:83:6]/
U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
;formatTitle = 'Scenario <scenarionumber>'
;formatTableDimensions = 3, 6
;formatTable:
1,1 = '' /
1,2 = 'Results' /
1,3 = 'Beratung' /
1,4 = 'Partner' /
1,5 = 'Kompensation' /
1,6 = 'Choice question&:' /
2,1 = 'alt1' /
2,2 = '<alt1.b>' /
2,3 = '<alt1.c>' /
2,4 = '<alt1.d>' /
2,5 = '<alt1.p>' /
2,6 = '' /
3,1 = 'alt2' /
3,2 = '<alt2.b>' /
3,3 = '<alt2.c>' /
3,4 = '<alt2.d>' /
3,5 = '<alt2.p>' /
3,6 = ''
;formatTableStyle:
1,1 = 'default' /
1,2 = 'headingattribute' /
1,3 = 'headingattribute' /
1,4 = 'headingattribute' /
1,5 = 'headingattribute' /
1,6 = 'headingattribute' /
2,1 = 'heading1' /
2,2 = 'body1' /
2,3 = 'body1' /
2,4 = 'body1' /
2,5 = 'body1' /
2,6 = 'choice1' /
3,1 = 'heading2' /
3,2 = 'body2' /
3,3 = 'body2' /
3,4 = 'body2' /
3,5 = 'body2' /
3,6 = 'choice2'
;formatStyleSheet = Default.css
;formatAttributes:
alt1.b(0=Results, 1=Action) /
alt1.c(0=Keine Beratung, 1=Mit Beratung) /
alt1.d(0=Keine Partner, 1=Mit Partner) /
alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
alt2.b(0=Results, 1=Action) /
alt2.c(0=Keine Beratung, 1=Mit Beratung) /
alt2.d(0=Keine Partner, 1=Mit Partner) /
alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
$
functions.R 0 → 100644
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− 0
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sim_choice <- function(designfile, no_sim=10, respondents=330, mnl_U,utils=u ) {
require("tictoc")
require("readr")
require("psych")
require("dplyr")
require("evd")
require("tidyr")
require("kableExtra")
require("gridExtra")
require("stringr")
require("mixl")
require("furrr")
require("purrr")
require("ggplot2")
require("formula.tools")
by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
# //! cur_data_all may get deprecated in favor of pick
# pick(everything()) %>%
cur_data_all() %>%
transmute(!!lhs(equation) := !!rhs(equation) )
}
simulate_choices <- function(data=database) { #the part in dataset that needs to be repeated in each run
# browser()
n=seq_along(1:length(utils))
nas=n
print("DANGER")
print(nas)
data <- data %>%
group_by(ID) %>%
mutate(
across(.cols=n,.fns = ~ rgumbel(setpp,loc=0, scale=1), .names = "{'e'}_{n}" ),
across(starts_with("V_"), .names = "{'U'}_{n}") + across(starts_with("e_")) ) %>% ungroup() %>%
mutate(CHOICE=max.col(.[,grep("U_",names(.))])
) %>%
as.data.frame()
return(data)
}
estimate_sim <- function(run=1) { #start loop
cat(run)
database <- simulate_choices()
model<-mixl::estimate(model_spec,start_values = est, availabilities = availabilities, data= database,)
return(model)
}
designs_all <- list()
design <- read_delim(designfile,delim = "\t",
escape_double = FALSE,
trim_ws = TRUE ,
col_select = c(-Design, -starts_with("...")) ,
name_repair = "universal") %>%
filter(!is.na(Choice.situation))
nsets<-nrow(design)
nblocks<-max(design$Block)
setpp <- nsets/nblocks # Choice Sets per respondent; in this 'no blocks' design everyone sees all 24 sets
#respondents <- replications*nblocks
replications <- respondents/nblocks
#browser()
database<- design %>%
arrange(Block,Choice.situation) %>%
slice(rep(row_number(), replications)) %>% ## replicate design according to number of replications
mutate(RID = rep(1:respondents, each=setpp)) %>% # create Respondent ID.
relocate(RID,`Choice.situation`) %>%
mutate( map_dfc(utils,by_formula) #our functions to create utility variables. They need to be entered as a formula list as an argument
# !!lhs(utils[["v1"]]) := !!rhs(utils[["v1"]]) , #Utility of alternative 1
# !!lhs(utils[["v2"]]) := !!rhs(utils[["v2"]]) ,
# !!lhs(utils[["v3"]]) := !!rhs(utils[["v3"]]),
# hgf = basc + btilapia*alt2.tilapia + bcichlids * alt2.cichlids + btoxin * alt2.toxin + bkisumu * alt2.origin + bprice * alt2.price #Utility of alternative 2
) %>% # utility of opt out, set to zero
rename(ID ="RID") %>%
rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
as.data.frame()
database <- simulate_choices()
model_spec <- mixl::specify_model(mnl_U, database, disable_multicore=F)
est=setNames(rep(0,length(model_spec$beta_names)), model_spec$beta_names)
availabilities <- mixl::generate_default_availabilities(
database, model_spec$num_utility_functions)
plan(multisession, workers = 8)
output<- 1:no_sim %>% map(estimate_sim)
coefs<-map(1:length(output),~summary(output[[.]])[["coefTable"]][c(1,8)] %>%
tibble::rownames_to_column() %>%
pivot_wider(names_from = rowname, values_from = c(est, rob_pval0)) ) %>%
bind_rows(.id = "run")
output[["summary"]] <-describe(coefs[,-1], fast = TRUE)
output[["coefs"]] <-coefs
pvals <- output[["coefs"]] %>% select(starts_with("rob_pval0"))
output[["power"]] <- 100*table(apply(pvals,1, function(x) all(x<0.05)))/nrow(pvals)
output[["metainfo"]] <- c(Path = designfile, NoSim = no_sim, NoResp =respondents)
print(kable(output[["summary"]],digits = 2, format = "rst"))
print(output[["power"]])
return(output)
}
plot_multi_histogram <- function(df, feature, label_column) { #function to create nice multi histograms, taken somewhere from the web
plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
#geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
geom_density(alpha=0.5) +
geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", linewidth=1) + ## this makes a vertical line of the mean
labs(x=feature, y = "Density")
plt + guides(fill=guide_legend(title=label_column))
}
\ No newline at end of file
modeloutput/modelclogitbase_OLD1_estimates.csv 0 → 100644
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− 0
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"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
modeloutput/modelclogitbase_OLD1_model.rds 0 → 100644
+ 0
− 0
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File added
modeloutput/modelclogitbase_OLD1_output.txt 0 → 100644
+ 375
− 0
View file @ 56f7d898
Model run by dj44vuri using Apollo 0.2.9 on R 4.3.1 for Windows.
www.ApolloChoiceModelling.com
Model name : modelclogitbase
Model description : Conditional Logit in preference space without Interaction Terms
Model run at : 2023-07-27 15:02:52.724953
Estimation method : bfgs
Model diagnosis : successful convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definitive
maximum eigenvalue : -221.009149
Number of individuals : 1686
Number of rows in database : 13488
Number of modelled outcomes : 13488
Number of cores used : 1
Model without mixing
LL(start) : -15096.79
LL at equal shares, LL(0) : -14818.08
LL at observed shares, LL(C) : -14788.08
LL(final) : -14140.77
Rho-squared vs equal shares : 0.0457
Adj.Rho-squared vs equal shares : 0.045
Rho-squared vs observed shares : 0.0438
Adj.Rho-squared vs observed shares : 0.0432
AIC : 28301.54
BIC : 28376.64
Estimated parameters : 10
Time taken (hh:mm:ss) : 00:00:6.88
pre-estimation : 00:00:1.79
estimation : 00:00:2.51
initial estimation : 00:00:2.31
estimation after rescaling : 00:00:0.2
post-estimation : 00:00:2.59
Iterations : 19
initial estimation : 18
estimation after rescaling : 1
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_gemeinschaft 0.59703 0.045306 13.178 0.062635 9.532
asc_klein 0.39320 0.045483 8.645 0.063976 6.146
b_groesse 0.07265 0.018682 3.889 0.017676 4.110
b_entfernung -0.22544 0.013890 -16.230 0.014598 -15.444
b_gemeinschaft 0.12696 0.022157 5.730 0.020623 6.156
b_kultur 0.15736 0.021823 7.211 0.024106 6.528
b_umweltbildung 0.14462 0.022491 6.430 0.021733 6.654
b_zugang 0.07087 0.006484 10.930 0.006862 10.328
b_gestaltung 0.38512 0.022122 17.409 0.024494 15.723
b_beitrag 1.15467 0.040731 28.349 0.044438 25.984
Overview of choices for MNL model component :
alt1 alt2 alt3
Times available 13488.00 13488.00 13488.00
Times chosen 4897.00 4172.00 4419.00
Percentage chosen overall 36.31 30.93 32.76
Percentage chosen when available 36.31 30.93 32.76
Classical covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.002053 0.001814 -1.6387e-04 -3.1367e-04
asc_klein 0.001814 0.002069 -1.5603e-04 -3.0430e-04
b_groesse -1.6387e-04 -1.5603e-04 3.4904e-04 -1.202e-05
b_entfernung -3.1367e-04 -3.0430e-04 -1.202e-05 1.9293e-04
b_gemeinschaft -2.0216e-04 -2.8784e-04 2.470e-05 -6.303e-06
b_kultur -2.6580e-04 -2.4705e-04 -2.023e-05 -2.996e-06
b_umweltbildung -2.1667e-04 -2.4558e-04 -1.281e-05 -1.625e-07
b_zugang 1.0691e-04 9.287e-05 2.746e-06 -1.227e-05
b_gestaltung -2.0075e-04 -1.9965e-04 -9.919e-06 2.088e-06
b_beitrag 8.5045e-04 8.1749e-04 7.331e-05 -1.0993e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -2.0216e-04 -2.6580e-04 -2.1667e-04 1.0691e-04
asc_klein -2.8784e-04 -2.4705e-04 -2.4558e-04 9.287e-05
b_groesse 2.470e-05 -2.023e-05 -1.281e-05 2.746e-06
b_entfernung -6.303e-06 -2.996e-06 -1.625e-07 -1.227e-05
b_gemeinschaft 4.9093e-04 1.591e-05 -3.279e-05 5.371e-06
b_kultur 1.591e-05 4.7623e-04 2.616e-05 2.690e-06
b_umweltbildung -3.279e-05 2.616e-05 5.0584e-04 1.146e-05
b_zugang 5.371e-06 2.690e-06 1.146e-05 4.204e-05
b_gestaltung -5.820e-06 -1.460e-05 3.842e-05 1.075e-05
b_beitrag -3.994e-06 -9.985e-06 1.0352e-04 5.433e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.0075e-04 8.5045e-04
asc_klein -1.9965e-04 8.1749e-04
b_groesse -9.919e-06 7.331e-05
b_entfernung 2.088e-06 -1.0993e-04
b_gemeinschaft -5.820e-06 -3.994e-06
b_kultur -1.460e-05 -9.985e-06
b_umweltbildung 3.842e-05 1.0352e-04
b_zugang 1.075e-05 5.433e-05
b_gestaltung 4.8939e-04 1.4109e-04
b_beitrag 1.4109e-04 0.001659
Robust covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.003923 0.003495 3.434e-05 -4.2294e-04
asc_klein 0.003495 0.004093 2.363e-05 -4.1771e-04
b_groesse 3.434e-05 2.363e-05 3.1245e-04 -3.722e-05
b_entfernung -4.2294e-04 -4.1771e-04 -3.722e-05 2.1309e-04
b_gemeinschaft -1.6873e-04 -2.9513e-04 5.110e-05 2.639e-05
b_kultur -3.5157e-04 -3.1605e-04 -7.567e-05 -1.475e-06
b_umweltbildung -3.2896e-04 -3.5905e-04 -7.914e-05 2.515e-05
b_zugang 1.1863e-04 1.0116e-04 2.347e-05 -1.619e-05
b_gestaltung -2.8127e-04 -2.6983e-04 1.694e-05 -5.223e-07
b_beitrag 0.001198 0.001162 6.508e-05 -1.3369e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -1.6873e-04 -3.5157e-04 -3.2896e-04 1.1863e-04
asc_klein -2.9513e-04 -3.1605e-04 -3.5905e-04 1.0116e-04
b_groesse 5.110e-05 -7.567e-05 -7.914e-05 2.347e-05
b_entfernung 2.639e-05 -1.475e-06 2.515e-05 -1.619e-05
b_gemeinschaft 4.2531e-04 -4.435e-05 -5.416e-05 1.428e-05
b_kultur -4.435e-05 5.8109e-04 1.5797e-04 -2.759e-06
b_umweltbildung -5.416e-05 1.5797e-04 4.7231e-04 2.230e-06
b_zugang 1.428e-05 -2.759e-06 2.230e-06 4.709e-05
b_gestaltung 8.182e-06 1.882e-05 5.443e-05 2.065e-05
b_beitrag -4.080e-05 7.814e-05 4.488e-05 5.799e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.8127e-04 0.001198
asc_klein -2.6983e-04 0.001162
b_groesse 1.694e-05 6.508e-05
b_entfernung -5.223e-07 -1.3369e-04
b_gemeinschaft 8.182e-06 -4.080e-05
b_kultur 1.882e-05 7.814e-05
b_umweltbildung 5.443e-05 4.488e-05
b_zugang 2.065e-05 5.799e-05
b_gestaltung 5.9997e-04 6.392e-05
b_beitrag 6.392e-05 0.001975
Classical correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.0000 0.8803 -0.19360 -0.498445
asc_klein 0.8803 1.0000 -0.18363 -0.481678
b_groesse -0.1936 -0.1836 1.00000 -0.046311
b_entfernung -0.4984 -0.4817 -0.04631 1.000000
b_gemeinschaft -0.2014 -0.2856 0.05966 -0.020479
b_kultur -0.2688 -0.2489 -0.04963 -0.009884
b_umweltbildung -0.2126 -0.2401 -0.03049 -5.2023e-04
b_zugang 0.3639 0.3149 0.02267 -0.136294
b_gestaltung -0.2003 -0.1984 -0.02400 0.006795
b_beitrag 0.4609 0.4413 0.09633 -0.194317
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.201388 -0.268838 -0.21263 0.36394
asc_klein -0.285625 -0.248906 -0.24007 0.31491
b_groesse 0.059660 -0.049629 -0.03049 0.02267
b_entfernung -0.020479 -0.009884 -5.2023e-04 -0.13629
b_gemeinschaft 1.000000 0.032902 -0.06580 0.03739
b_kultur 0.032902 1.000000 0.05329 0.01901
b_umweltbildung -0.065797 0.053294 1.00000 0.07859
b_zugang 0.037389 0.019014 0.07859 1.00000
b_gestaltung -0.011875 -0.030242 0.07721 0.07495
b_beitrag -0.004425 -0.011234 0.11301 0.20571
b_gestaltung b_beitrag
asc_gemeinschaft -0.200300 0.460867
asc_klein -0.198426 0.441279
b_groesse -0.024000 0.096334
b_entfernung 0.006795 -0.194317
b_gemeinschaft -0.011875 -0.004425
b_kultur -0.030242 -0.011234
b_umweltbildung 0.077213 0.113009
b_zugang 0.074949 0.205711
b_gestaltung 1.000000 0.156582
b_beitrag 0.156582 1.000000
Robust correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.00000 0.87208 0.03102 -0.462580
asc_klein 0.87208 1.00000 0.02090 -0.447283
b_groesse 0.03102 0.02090 1.00000 -0.144228
b_entfernung -0.46258 -0.44728 -0.14423 1.000000
b_gemeinschaft -0.13063 -0.22368 0.14018 0.087646
b_kultur -0.23285 -0.20494 -0.17759 -0.004193
b_umweltbildung -0.24167 -0.25824 -0.20602 0.079269
b_zugang 0.27600 0.23043 0.19348 -0.161624
b_gestaltung -0.18333 -0.17219 0.03913 -0.001461
b_beitrag 0.43033 0.40877 0.08285 -0.206098
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.13063 -0.232852 -0.24167 0.27600
asc_klein -0.22368 -0.204937 -0.25824 0.23043
b_groesse 0.14018 -0.177587 -0.20602 0.19348
b_entfernung 0.08765 -0.004193 0.07927 -0.16162
b_gemeinschaft 1.00000 -0.089205 -0.12085 0.10090
b_kultur -0.08921 1.000000 0.30154 -0.01668
b_umweltbildung -0.12085 0.301544 1.00000 0.01495
b_zugang 0.10090 -0.016679 0.01495 1.00000
b_gestaltung 0.01620 0.031878 0.10226 0.12286
b_beitrag -0.04451 0.072940 0.04647 0.19015
b_gestaltung b_beitrag
asc_gemeinschaft -0.183334 0.43033
asc_klein -0.172191 0.40877
b_groesse 0.039126 0.08285
b_entfernung -0.001461 -0.20610
b_gemeinschaft 0.016198 -0.04451
b_kultur 0.031878 0.07294
b_umweltbildung 0.102258 0.04647
b_zugang 0.122864 0.19015
b_gestaltung 1.000000 0.05873
b_beitrag 0.058727 1.00000
20 worst outliers in terms of lowest average per choice prediction:
ID Avg prob per choice
1863 0.2412086
1735 0.2484631
10182 0.2524603
10214 0.2544820
1807 0.2562983
1315 0.2565500
1074 0.2566505
1784 0.2566725
1205 0.2569582
1812 0.2569582
867 0.2575401
10892 0.2600787
1670 0.2606873
10581 0.2606901
10020 0.2630297
10744 0.2633822
10311 0.2639950
1579 0.2642995
151 0.2656306
1947 0.2666865
Changes in parameter estimates from starting values:
Initial Estimate Difference
asc_gemeinschaft 0.61000 0.59703 -0.01297
asc_klein 0.43000 0.39320 -0.03680
b_groesse 0.00000 0.07265 0.07265
b_entfernung -0.17000 -0.22544 -0.05544
b_gemeinschaft 0.06000 0.12696 0.06696
b_kultur 0.06000 0.15736 0.09736
b_umweltbildung 0.13000 0.14462 0.01462
b_zugang 0.05000 0.07087 0.02087
b_gestaltung 0.31000 0.38512 0.07512
b_beitrag -0.12000 1.15467 1.27467
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "modelclogitbase"
modelDescr "Conditional Logit in preference space without Interaction Terms"
indivID "ID"
mixing "FALSE"
HB "FALSE"
nCores "1"
outputDirectory "modeloutput/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling in estimation
---------------------
Value
asc_gemeinschaft 0.59702793
asc_klein 0.39319962
b_groesse 0.07265481
b_entfernung 0.22543850
b_gemeinschaft 0.12696058
b_kultur 0.15735945
b_umweltbildung 0.14461548
b_zugang 0.07087200
b_gestaltung 0.38512237
b_beitrag 1.15466844
Scaling used in computing Hessian
---------------------------------
Value
asc_gemeinschaft 0.59702783
asc_klein 0.39319969
b_groesse 0.07265481
b_entfernung 0.22543845
b_gemeinschaft 0.12696059
b_kultur 0.15735944
b_umweltbildung 0.14461547
b_zugang 0.07087200
b_gestaltung 0.38512245
b_beitrag 1.15466801
apollo_probabilities
----------------------
function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
### List of utilities (later integrated in mnl_settings below)
V = list()
V[['alt1']] =
asc_gemeinschaft +
b_groesse * GROESSE.1 +
b_entfernung * ENTFERNUNG.1 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
b_kultur * KULTURVERANSTALTUNGEN.1 +
b_umweltbildung * UMWELTBILDUNG.1 +
b_zugang * ZUGANG.1 +
b_gestaltung * GESTALTUNG.1 -
b_beitrag*BEITRAG.1
V[['alt2']] =
asc_klein +
b_groesse * GROESSE.2 +
b_entfernung * ENTFERNUNG.2 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
b_kultur * KULTURVERANSTALTUNGEN.2 +
b_umweltbildung * UMWELTBILDUNG.2 +
b_zugang * ZUGANG.2 +
b_gestaltung * GESTALTUNG.2 -
b_beitrag* BEITRAG.2
V[['alt3']] = 0
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = 1, # all alternatives are available in every choice
choiceVar = choice,
V = V#, # tell function to use list vector defined above
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
modeloutput/modelclogitbase_OLD2_estimates.csv 0 → 100644
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− 0
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"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
modeloutput/modelclogitbase_OLD2_model.rds 0 → 100644
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modeloutput/modelclogitbase_OLD2_output.txt 0 → 100644
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View file @ 56f7d898
Model run by dj44vuri using Apollo 0.2.9 on R 4.3.1 for Windows.
www.ApolloChoiceModelling.com
Model name : modelclogitbase
Model description : Conditional Logit in preference space without Interaction Terms
Model run at : 2023-07-27 15:03:37.628167
Estimation method : bfgs
Model diagnosis : successful convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definitive
maximum eigenvalue : -221.009149
Number of individuals : 1686
Number of rows in database : 13488
Number of modelled outcomes : 13488
Number of cores used : 1
Model without mixing
LL(start) : -15096.79
LL at equal shares, LL(0) : -14818.08
LL at observed shares, LL(C) : -14788.08
LL(final) : -14140.77
Rho-squared vs equal shares : 0.0457
Adj.Rho-squared vs equal shares : 0.045
Rho-squared vs observed shares : 0.0438
Adj.Rho-squared vs observed shares : 0.0432
AIC : 28301.54
BIC : 28376.64
Estimated parameters : 10
Time taken (hh:mm:ss) : 00:00:5.96
pre-estimation : 00:00:1.67
estimation : 00:00:1.69
initial estimation : 00:00:1.54
estimation after rescaling : 00:00:0.14
post-estimation : 00:00:2.6
Iterations : 19
initial estimation : 18
estimation after rescaling : 1
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_gemeinschaft 0.59703 0.045306 13.178 0.062635 9.532
asc_klein 0.39320 0.045483 8.645 0.063976 6.146
b_groesse 0.07265 0.018682 3.889 0.017676 4.110
b_entfernung -0.22544 0.013890 -16.230 0.014598 -15.444
b_gemeinschaft 0.12696 0.022157 5.730 0.020623 6.156
b_kultur 0.15736 0.021823 7.211 0.024106 6.528
b_umweltbildung 0.14462 0.022491 6.430 0.021733 6.654
b_zugang 0.07087 0.006484 10.930 0.006862 10.328
b_gestaltung 0.38512 0.022122 17.409 0.024494 15.723
b_beitrag 1.15467 0.040731 28.349 0.044438 25.984
Overview of choices for MNL model component :
alt1 alt2 alt3
Times available 13488.00 13488.00 13488.00
Times chosen 4897.00 4172.00 4419.00
Percentage chosen overall 36.31 30.93 32.76
Percentage chosen when available 36.31 30.93 32.76
Classical covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.002053 0.001814 -1.6387e-04 -3.1367e-04
asc_klein 0.001814 0.002069 -1.5603e-04 -3.0430e-04
b_groesse -1.6387e-04 -1.5603e-04 3.4904e-04 -1.202e-05
b_entfernung -3.1367e-04 -3.0430e-04 -1.202e-05 1.9293e-04
b_gemeinschaft -2.0216e-04 -2.8784e-04 2.470e-05 -6.303e-06
b_kultur -2.6580e-04 -2.4705e-04 -2.023e-05 -2.996e-06
b_umweltbildung -2.1667e-04 -2.4558e-04 -1.281e-05 -1.625e-07
b_zugang 1.0691e-04 9.287e-05 2.746e-06 -1.227e-05
b_gestaltung -2.0075e-04 -1.9965e-04 -9.919e-06 2.088e-06
b_beitrag 8.5045e-04 8.1749e-04 7.331e-05 -1.0993e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -2.0216e-04 -2.6580e-04 -2.1667e-04 1.0691e-04
asc_klein -2.8784e-04 -2.4705e-04 -2.4558e-04 9.287e-05
b_groesse 2.470e-05 -2.023e-05 -1.281e-05 2.746e-06
b_entfernung -6.303e-06 -2.996e-06 -1.625e-07 -1.227e-05
b_gemeinschaft 4.9093e-04 1.591e-05 -3.279e-05 5.371e-06
b_kultur 1.591e-05 4.7623e-04 2.616e-05 2.690e-06
b_umweltbildung -3.279e-05 2.616e-05 5.0584e-04 1.146e-05
b_zugang 5.371e-06 2.690e-06 1.146e-05 4.204e-05
b_gestaltung -5.820e-06 -1.460e-05 3.842e-05 1.075e-05
b_beitrag -3.994e-06 -9.985e-06 1.0352e-04 5.433e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.0075e-04 8.5045e-04
asc_klein -1.9965e-04 8.1749e-04
b_groesse -9.919e-06 7.331e-05
b_entfernung 2.088e-06 -1.0993e-04
b_gemeinschaft -5.820e-06 -3.994e-06
b_kultur -1.460e-05 -9.985e-06
b_umweltbildung 3.842e-05 1.0352e-04
b_zugang 1.075e-05 5.433e-05
b_gestaltung 4.8939e-04 1.4109e-04
b_beitrag 1.4109e-04 0.001659
Robust covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.003923 0.003495 3.434e-05 -4.2294e-04
asc_klein 0.003495 0.004093 2.363e-05 -4.1771e-04
b_groesse 3.434e-05 2.363e-05 3.1245e-04 -3.722e-05
b_entfernung -4.2294e-04 -4.1771e-04 -3.722e-05 2.1309e-04
b_gemeinschaft -1.6873e-04 -2.9513e-04 5.110e-05 2.639e-05
b_kultur -3.5157e-04 -3.1605e-04 -7.567e-05 -1.475e-06
b_umweltbildung -3.2896e-04 -3.5905e-04 -7.914e-05 2.515e-05
b_zugang 1.1863e-04 1.0116e-04 2.347e-05 -1.619e-05
b_gestaltung -2.8127e-04 -2.6983e-04 1.694e-05 -5.223e-07
b_beitrag 0.001198 0.001162 6.508e-05 -1.3369e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -1.6873e-04 -3.5157e-04 -3.2896e-04 1.1863e-04
asc_klein -2.9513e-04 -3.1605e-04 -3.5905e-04 1.0116e-04
b_groesse 5.110e-05 -7.567e-05 -7.914e-05 2.347e-05
b_entfernung 2.639e-05 -1.475e-06 2.515e-05 -1.619e-05
b_gemeinschaft 4.2531e-04 -4.435e-05 -5.416e-05 1.428e-05
b_kultur -4.435e-05 5.8109e-04 1.5797e-04 -2.759e-06
b_umweltbildung -5.416e-05 1.5797e-04 4.7231e-04 2.230e-06
b_zugang 1.428e-05 -2.759e-06 2.230e-06 4.709e-05
b_gestaltung 8.182e-06 1.882e-05 5.443e-05 2.065e-05
b_beitrag -4.080e-05 7.814e-05 4.488e-05 5.799e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.8127e-04 0.001198
asc_klein -2.6983e-04 0.001162
b_groesse 1.694e-05 6.508e-05
b_entfernung -5.223e-07 -1.3369e-04
b_gemeinschaft 8.182e-06 -4.080e-05
b_kultur 1.882e-05 7.814e-05
b_umweltbildung 5.443e-05 4.488e-05
b_zugang 2.065e-05 5.799e-05
b_gestaltung 5.9997e-04 6.392e-05
b_beitrag 6.392e-05 0.001975
Classical correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.0000 0.8803 -0.19360 -0.498445
asc_klein 0.8803 1.0000 -0.18363 -0.481678
b_groesse -0.1936 -0.1836 1.00000 -0.046311
b_entfernung -0.4984 -0.4817 -0.04631 1.000000
b_gemeinschaft -0.2014 -0.2856 0.05966 -0.020479
b_kultur -0.2688 -0.2489 -0.04963 -0.009884
b_umweltbildung -0.2126 -0.2401 -0.03049 -5.2023e-04
b_zugang 0.3639 0.3149 0.02267 -0.136294
b_gestaltung -0.2003 -0.1984 -0.02400 0.006795
b_beitrag 0.4609 0.4413 0.09633 -0.194317
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.201388 -0.268838 -0.21263 0.36394
asc_klein -0.285625 -0.248906 -0.24007 0.31491
b_groesse 0.059660 -0.049629 -0.03049 0.02267
b_entfernung -0.020479 -0.009884 -5.2023e-04 -0.13629
b_gemeinschaft 1.000000 0.032902 -0.06580 0.03739
b_kultur 0.032902 1.000000 0.05329 0.01901
b_umweltbildung -0.065797 0.053294 1.00000 0.07859
b_zugang 0.037389 0.019014 0.07859 1.00000
b_gestaltung -0.011875 -0.030242 0.07721 0.07495
b_beitrag -0.004425 -0.011234 0.11301 0.20571
b_gestaltung b_beitrag
asc_gemeinschaft -0.200300 0.460867
asc_klein -0.198426 0.441279
b_groesse -0.024000 0.096334
b_entfernung 0.006795 -0.194317
b_gemeinschaft -0.011875 -0.004425
b_kultur -0.030242 -0.011234
b_umweltbildung 0.077213 0.113009
b_zugang 0.074949 0.205711
b_gestaltung 1.000000 0.156582
b_beitrag 0.156582 1.000000
Robust correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.00000 0.87208 0.03102 -0.462580
asc_klein 0.87208 1.00000 0.02090 -0.447283
b_groesse 0.03102 0.02090 1.00000 -0.144228
b_entfernung -0.46258 -0.44728 -0.14423 1.000000
b_gemeinschaft -0.13063 -0.22368 0.14018 0.087646
b_kultur -0.23285 -0.20494 -0.17759 -0.004193
b_umweltbildung -0.24167 -0.25824 -0.20602 0.079269
b_zugang 0.27600 0.23043 0.19348 -0.161624
b_gestaltung -0.18333 -0.17219 0.03913 -0.001461
b_beitrag 0.43033 0.40877 0.08285 -0.206098
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.13063 -0.232852 -0.24167 0.27600
asc_klein -0.22368 -0.204937 -0.25824 0.23043
b_groesse 0.14018 -0.177587 -0.20602 0.19348
b_entfernung 0.08765 -0.004193 0.07927 -0.16162
b_gemeinschaft 1.00000 -0.089205 -0.12085 0.10090
b_kultur -0.08921 1.000000 0.30154 -0.01668
b_umweltbildung -0.12085 0.301544 1.00000 0.01495
b_zugang 0.10090 -0.016679 0.01495 1.00000
b_gestaltung 0.01620 0.031878 0.10226 0.12286
b_beitrag -0.04451 0.072940 0.04647 0.19015
b_gestaltung b_beitrag
asc_gemeinschaft -0.183334 0.43033
asc_klein -0.172191 0.40877
b_groesse 0.039126 0.08285
b_entfernung -0.001461 -0.20610
b_gemeinschaft 0.016198 -0.04451
b_kultur 0.031878 0.07294
b_umweltbildung 0.102258 0.04647
b_zugang 0.122864 0.19015
b_gestaltung 1.000000 0.05873
b_beitrag 0.058727 1.00000
20 worst outliers in terms of lowest average per choice prediction:
ID Avg prob per choice
1863 0.2412086
1735 0.2484631
10182 0.2524603
10214 0.2544820
1807 0.2562983
1315 0.2565500
1074 0.2566505
1784 0.2566725
1205 0.2569582
1812 0.2569582
867 0.2575401
10892 0.2600787
1670 0.2606873
10581 0.2606901
10020 0.2630297
10744 0.2633822
10311 0.2639950
1579 0.2642995
151 0.2656306
1947 0.2666865
Changes in parameter estimates from starting values:
Initial Estimate Difference
asc_gemeinschaft 0.61000 0.59703 -0.01297
asc_klein 0.43000 0.39320 -0.03680
b_groesse 0.00000 0.07265 0.07265
b_entfernung -0.17000 -0.22544 -0.05544
b_gemeinschaft 0.06000 0.12696 0.06696
b_kultur 0.06000 0.15736 0.09736
b_umweltbildung 0.13000 0.14462 0.01462
b_zugang 0.05000 0.07087 0.02087
b_gestaltung 0.31000 0.38512 0.07512
b_beitrag -0.12000 1.15467 1.27467
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "modelclogitbase"
modelDescr "Conditional Logit in preference space without Interaction Terms"
indivID "ID"
mixing "FALSE"
HB "FALSE"
nCores "1"
outputDirectory "modeloutput/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling in estimation
---------------------
Value
asc_gemeinschaft 0.59702793
asc_klein 0.39319962
b_groesse 0.07265481
b_entfernung 0.22543850
b_gemeinschaft 0.12696058
b_kultur 0.15735945
b_umweltbildung 0.14461548
b_zugang 0.07087200
b_gestaltung 0.38512237
b_beitrag 1.15466844
Scaling used in computing Hessian
---------------------------------
Value
asc_gemeinschaft 0.59702783
asc_klein 0.39319969
b_groesse 0.07265481
b_entfernung 0.22543845
b_gemeinschaft 0.12696059
b_kultur 0.15735944
b_umweltbildung 0.14461547
b_zugang 0.07087200
b_gestaltung 0.38512245
b_beitrag 1.15466801
apollo_probabilities
----------------------
function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
### List of utilities (later integrated in mnl_settings below)
V = list()
V[['alt1']] =
asc_gemeinschaft +
b_groesse * GROESSE.1 +
b_entfernung * ENTFERNUNG.1 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
b_kultur * KULTURVERANSTALTUNGEN.1 +
b_umweltbildung * UMWELTBILDUNG.1 +
b_zugang * ZUGANG.1 +
b_gestaltung * GESTALTUNG.1 -
b_beitrag*BEITRAG.1
V[['alt2']] =
asc_klein +
b_groesse * GROESSE.2 +
b_entfernung * ENTFERNUNG.2 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
b_kultur * KULTURVERANSTALTUNGEN.2 +
b_umweltbildung * UMWELTBILDUNG.2 +
b_zugang * ZUGANG.2 +
b_gestaltung * GESTALTUNG.2 -
b_beitrag* BEITRAG.2
V[['alt3']] = 0
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = 1, # all alternatives are available in every choice
choiceVar = choice,
V = V#, # tell function to use list vector defined above
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
modeloutput/modelclogitbase_OLD3_estimates.csv 0 → 100644
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"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
modeloutput/modelclogitbase_OLD3_model.rds 0 → 100644
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modeloutput/modelclogitbase_OLD3_output.txt 0 → 100644
+ 375
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Model run by dj44vuri using Apollo 0.2.9 on R 4.3.1 for Windows.
www.ApolloChoiceModelling.com
Model name : modelclogitbase
Model description : Conditional Logit in preference space without Interaction Terms
Model run at : 2023-07-27 15:42:38.094111
Estimation method : bfgs
Model diagnosis : successful convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definitive
maximum eigenvalue : -221.009149
Number of individuals : 1686
Number of rows in database : 13488
Number of modelled outcomes : 13488
Number of cores used : 1
Model without mixing
LL(start) : -15096.79
LL at equal shares, LL(0) : -14818.08
LL at observed shares, LL(C) : -14788.08
LL(final) : -14140.77
Rho-squared vs equal shares : 0.0457
Adj.Rho-squared vs equal shares : 0.045
Rho-squared vs observed shares : 0.0438
Adj.Rho-squared vs observed shares : 0.0432
AIC : 28301.54
BIC : 28376.64
Estimated parameters : 10
Time taken (hh:mm:ss) : 00:00:6.19
pre-estimation : 00:00:1.82
estimation : 00:00:1.73
initial estimation : 00:00:1.57
estimation after rescaling : 00:00:0.16
post-estimation : 00:00:2.64
Iterations : 19
initial estimation : 18
estimation after rescaling : 1
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_gemeinschaft 0.59703 0.045306 13.178 0.062635 9.532
asc_klein 0.39320 0.045483 8.645 0.063976 6.146
b_groesse 0.07265 0.018682 3.889 0.017676 4.110
b_entfernung -0.22544 0.013890 -16.230 0.014598 -15.444
b_gemeinschaft 0.12696 0.022157 5.730 0.020623 6.156
b_kultur 0.15736 0.021823 7.211 0.024106 6.528
b_umweltbildung 0.14462 0.022491 6.430 0.021733 6.654
b_zugang 0.07087 0.006484 10.930 0.006862 10.328
b_gestaltung 0.38512 0.022122 17.409 0.024494 15.723
b_beitrag 1.15467 0.040731 28.349 0.044438 25.984
Overview of choices for MNL model component :
alt1 alt2 alt3
Times available 13488.00 13488.00 13488.00
Times chosen 4897.00 4172.00 4419.00
Percentage chosen overall 36.31 30.93 32.76
Percentage chosen when available 36.31 30.93 32.76
Classical covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.002053 0.001814 -1.6387e-04 -3.1367e-04
asc_klein 0.001814 0.002069 -1.5603e-04 -3.0430e-04
b_groesse -1.6387e-04 -1.5603e-04 3.4904e-04 -1.202e-05
b_entfernung -3.1367e-04 -3.0430e-04 -1.202e-05 1.9293e-04
b_gemeinschaft -2.0216e-04 -2.8784e-04 2.470e-05 -6.303e-06
b_kultur -2.6580e-04 -2.4705e-04 -2.023e-05 -2.996e-06
b_umweltbildung -2.1667e-04 -2.4558e-04 -1.281e-05 -1.625e-07
b_zugang 1.0691e-04 9.287e-05 2.746e-06 -1.227e-05
b_gestaltung -2.0075e-04 -1.9965e-04 -9.919e-06 2.088e-06
b_beitrag 8.5045e-04 8.1749e-04 7.331e-05 -1.0993e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -2.0216e-04 -2.6580e-04 -2.1667e-04 1.0691e-04
asc_klein -2.8784e-04 -2.4705e-04 -2.4558e-04 9.287e-05
b_groesse 2.470e-05 -2.023e-05 -1.281e-05 2.746e-06
b_entfernung -6.303e-06 -2.996e-06 -1.625e-07 -1.227e-05
b_gemeinschaft 4.9093e-04 1.591e-05 -3.279e-05 5.371e-06
b_kultur 1.591e-05 4.7623e-04 2.616e-05 2.690e-06
b_umweltbildung -3.279e-05 2.616e-05 5.0584e-04 1.146e-05
b_zugang 5.371e-06 2.690e-06 1.146e-05 4.204e-05
b_gestaltung -5.820e-06 -1.460e-05 3.842e-05 1.075e-05
b_beitrag -3.994e-06 -9.985e-06 1.0352e-04 5.433e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.0075e-04 8.5045e-04
asc_klein -1.9965e-04 8.1749e-04
b_groesse -9.919e-06 7.331e-05
b_entfernung 2.088e-06 -1.0993e-04
b_gemeinschaft -5.820e-06 -3.994e-06
b_kultur -1.460e-05 -9.985e-06
b_umweltbildung 3.842e-05 1.0352e-04
b_zugang 1.075e-05 5.433e-05
b_gestaltung 4.8939e-04 1.4109e-04
b_beitrag 1.4109e-04 0.001659
Robust covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.003923 0.003495 3.434e-05 -4.2294e-04
asc_klein 0.003495 0.004093 2.363e-05 -4.1771e-04
b_groesse 3.434e-05 2.363e-05 3.1245e-04 -3.722e-05
b_entfernung -4.2294e-04 -4.1771e-04 -3.722e-05 2.1309e-04
b_gemeinschaft -1.6873e-04 -2.9513e-04 5.110e-05 2.639e-05
b_kultur -3.5157e-04 -3.1605e-04 -7.567e-05 -1.475e-06
b_umweltbildung -3.2896e-04 -3.5905e-04 -7.914e-05 2.515e-05
b_zugang 1.1863e-04 1.0116e-04 2.347e-05 -1.619e-05
b_gestaltung -2.8127e-04 -2.6983e-04 1.694e-05 -5.223e-07
b_beitrag 0.001198 0.001162 6.508e-05 -1.3369e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -1.6873e-04 -3.5157e-04 -3.2896e-04 1.1863e-04
asc_klein -2.9513e-04 -3.1605e-04 -3.5905e-04 1.0116e-04
b_groesse 5.110e-05 -7.567e-05 -7.914e-05 2.347e-05
b_entfernung 2.639e-05 -1.475e-06 2.515e-05 -1.619e-05
b_gemeinschaft 4.2531e-04 -4.435e-05 -5.416e-05 1.428e-05
b_kultur -4.435e-05 5.8109e-04 1.5797e-04 -2.759e-06
b_umweltbildung -5.416e-05 1.5797e-04 4.7231e-04 2.230e-06
b_zugang 1.428e-05 -2.759e-06 2.230e-06 4.709e-05
b_gestaltung 8.182e-06 1.882e-05 5.443e-05 2.065e-05
b_beitrag -4.080e-05 7.814e-05 4.488e-05 5.799e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.8127e-04 0.001198
asc_klein -2.6983e-04 0.001162
b_groesse 1.694e-05 6.508e-05
b_entfernung -5.223e-07 -1.3369e-04
b_gemeinschaft 8.182e-06 -4.080e-05
b_kultur 1.882e-05 7.814e-05
b_umweltbildung 5.443e-05 4.488e-05
b_zugang 2.065e-05 5.799e-05
b_gestaltung 5.9997e-04 6.392e-05
b_beitrag 6.392e-05 0.001975
Classical correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.0000 0.8803 -0.19360 -0.498445
asc_klein 0.8803 1.0000 -0.18363 -0.481678
b_groesse -0.1936 -0.1836 1.00000 -0.046311
b_entfernung -0.4984 -0.4817 -0.04631 1.000000
b_gemeinschaft -0.2014 -0.2856 0.05966 -0.020479
b_kultur -0.2688 -0.2489 -0.04963 -0.009884
b_umweltbildung -0.2126 -0.2401 -0.03049 -5.2023e-04
b_zugang 0.3639 0.3149 0.02267 -0.136294
b_gestaltung -0.2003 -0.1984 -0.02400 0.006795
b_beitrag 0.4609 0.4413 0.09633 -0.194317
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.201388 -0.268838 -0.21263 0.36394
asc_klein -0.285625 -0.248906 -0.24007 0.31491
b_groesse 0.059660 -0.049629 -0.03049 0.02267
b_entfernung -0.020479 -0.009884 -5.2023e-04 -0.13629
b_gemeinschaft 1.000000 0.032902 -0.06580 0.03739
b_kultur 0.032902 1.000000 0.05329 0.01901
b_umweltbildung -0.065797 0.053294 1.00000 0.07859
b_zugang 0.037389 0.019014 0.07859 1.00000
b_gestaltung -0.011875 -0.030242 0.07721 0.07495
b_beitrag -0.004425 -0.011234 0.11301 0.20571
b_gestaltung b_beitrag
asc_gemeinschaft -0.200300 0.460867
asc_klein -0.198426 0.441279
b_groesse -0.024000 0.096334
b_entfernung 0.006795 -0.194317
b_gemeinschaft -0.011875 -0.004425
b_kultur -0.030242 -0.011234
b_umweltbildung 0.077213 0.113009
b_zugang 0.074949 0.205711
b_gestaltung 1.000000 0.156582
b_beitrag 0.156582 1.000000
Robust correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.00000 0.87208 0.03102 -0.462580
asc_klein 0.87208 1.00000 0.02090 -0.447283
b_groesse 0.03102 0.02090 1.00000 -0.144228
b_entfernung -0.46258 -0.44728 -0.14423 1.000000
b_gemeinschaft -0.13063 -0.22368 0.14018 0.087646
b_kultur -0.23285 -0.20494 -0.17759 -0.004193
b_umweltbildung -0.24167 -0.25824 -0.20602 0.079269
b_zugang 0.27600 0.23043 0.19348 -0.161624
b_gestaltung -0.18333 -0.17219 0.03913 -0.001461
b_beitrag 0.43033 0.40877 0.08285 -0.206098
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.13063 -0.232852 -0.24167 0.27600
asc_klein -0.22368 -0.204937 -0.25824 0.23043
b_groesse 0.14018 -0.177587 -0.20602 0.19348
b_entfernung 0.08765 -0.004193 0.07927 -0.16162
b_gemeinschaft 1.00000 -0.089205 -0.12085 0.10090
b_kultur -0.08921 1.000000 0.30154 -0.01668
b_umweltbildung -0.12085 0.301544 1.00000 0.01495
b_zugang 0.10090 -0.016679 0.01495 1.00000
b_gestaltung 0.01620 0.031878 0.10226 0.12286
b_beitrag -0.04451 0.072940 0.04647 0.19015
b_gestaltung b_beitrag
asc_gemeinschaft -0.183334 0.43033
asc_klein -0.172191 0.40877
b_groesse 0.039126 0.08285
b_entfernung -0.001461 -0.20610
b_gemeinschaft 0.016198 -0.04451
b_kultur 0.031878 0.07294
b_umweltbildung 0.102258 0.04647
b_zugang 0.122864 0.19015
b_gestaltung 1.000000 0.05873
b_beitrag 0.058727 1.00000
20 worst outliers in terms of lowest average per choice prediction:
ID Avg prob per choice
1863 0.2412086
1735 0.2484631
10182 0.2524603
10214 0.2544820
1807 0.2562983
1315 0.2565500
1074 0.2566505
1784 0.2566725
1205 0.2569582
1812 0.2569582
867 0.2575401
10892 0.2600787
1670 0.2606873
10581 0.2606901
10020 0.2630297
10744 0.2633822
10311 0.2639950
1579 0.2642995
151 0.2656306
1947 0.2666865
Changes in parameter estimates from starting values:
Initial Estimate Difference
asc_gemeinschaft 0.61000 0.59703 -0.01297
asc_klein 0.43000 0.39320 -0.03680
b_groesse 0.00000 0.07265 0.07265
b_entfernung -0.17000 -0.22544 -0.05544
b_gemeinschaft 0.06000 0.12696 0.06696
b_kultur 0.06000 0.15736 0.09736
b_umweltbildung 0.13000 0.14462 0.01462
b_zugang 0.05000 0.07087 0.02087
b_gestaltung 0.31000 0.38512 0.07512
b_beitrag -0.12000 1.15467 1.27467
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "modelclogitbase"
modelDescr "Conditional Logit in preference space without Interaction Terms"
indivID "ID"
mixing "FALSE"
HB "FALSE"
nCores "1"
outputDirectory "modeloutput/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling in estimation
---------------------
Value
asc_gemeinschaft 0.59702793
asc_klein 0.39319962
b_groesse 0.07265481
b_entfernung 0.22543850
b_gemeinschaft 0.12696058
b_kultur 0.15735945
b_umweltbildung 0.14461548
b_zugang 0.07087200
b_gestaltung 0.38512237
b_beitrag 1.15466844
Scaling used in computing Hessian
---------------------------------
Value
asc_gemeinschaft 0.59702783
asc_klein 0.39319969
b_groesse 0.07265481
b_entfernung 0.22543845
b_gemeinschaft 0.12696059
b_kultur 0.15735944
b_umweltbildung 0.14461547
b_zugang 0.07087200
b_gestaltung 0.38512245
b_beitrag 1.15466801
apollo_probabilities
----------------------
function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
### List of utilities (later integrated in mnl_settings below)
V = list()
V[['alt1']] =
asc_gemeinschaft +
b_groesse * GROESSE.1 +
b_entfernung * ENTFERNUNG.1 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
b_kultur * KULTURVERANSTALTUNGEN.1 +
b_umweltbildung * UMWELTBILDUNG.1 +
b_zugang * ZUGANG.1 +
b_gestaltung * GESTALTUNG.1 -
b_beitrag*BEITRAG.1
V[['alt2']] =
asc_klein +
b_groesse * GROESSE.2 +
b_entfernung * ENTFERNUNG.2 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
b_kultur * KULTURVERANSTALTUNGEN.2 +
b_umweltbildung * UMWELTBILDUNG.2 +
b_zugang * ZUGANG.2 +
b_gestaltung * GESTALTUNG.2 -
b_beitrag* BEITRAG.2
V[['alt3']] = 0
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = 1, # all alternatives are available in every choice
choiceVar = choice,
V = V#, # tell function to use list vector defined above
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
modeloutput/modelclogitbase_OLD4_estimates.csv 0 → 100644
+ 11
− 0
View file @ 56f7d898
"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
modeloutput/modelclogitbase_OLD4_model.rds 0 → 100644
+ 0
− 0
View file @ 56f7d898
File added
modeloutput/modelclogitbase_OLD4_output.txt 0 → 100644
+ 375
− 0
View file @ 56f7d898
Model run by dj44vuri using Apollo 0.2.9 on R 4.3.1 for Windows.
www.ApolloChoiceModelling.com
Model name : modelclogitbase
Model description : Conditional Logit in preference space without Interaction Terms
Model run at : 2023-07-27 15:45:23.313356
Estimation method : bfgs
Model diagnosis : successful convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definitive
maximum eigenvalue : -221.009149
Number of individuals : 1686
Number of rows in database : 13488
Number of modelled outcomes : 13488
Number of cores used : 1
Model without mixing
LL(start) : -15096.79
LL at equal shares, LL(0) : -14818.08
LL at observed shares, LL(C) : -14788.08
LL(final) : -14140.77
Rho-squared vs equal shares : 0.0457
Adj.Rho-squared vs equal shares : 0.045
Rho-squared vs observed shares : 0.0438
Adj.Rho-squared vs observed shares : 0.0432
AIC : 28301.54
BIC : 28376.64
Estimated parameters : 10
Time taken (hh:mm:ss) : 00:00:5.99
pre-estimation : 00:00:1.7
estimation : 00:00:1.71
initial estimation : 00:00:1.53
estimation after rescaling : 00:00:0.18
post-estimation : 00:00:2.58
Iterations : 19
initial estimation : 18
estimation after rescaling : 1
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_gemeinschaft 0.59703 0.045306 13.178 0.062635 9.532
asc_klein 0.39320 0.045483 8.645 0.063976 6.146
b_groesse 0.07265 0.018682 3.889 0.017676 4.110
b_entfernung -0.22544 0.013890 -16.230 0.014598 -15.444
b_gemeinschaft 0.12696 0.022157 5.730 0.020623 6.156
b_kultur 0.15736 0.021823 7.211 0.024106 6.528
b_umweltbildung 0.14462 0.022491 6.430 0.021733 6.654
b_zugang 0.07087 0.006484 10.930 0.006862 10.328
b_gestaltung 0.38512 0.022122 17.409 0.024494 15.723
b_beitrag 1.15467 0.040731 28.349 0.044438 25.984
Overview of choices for MNL model component :
alt1 alt2 alt3
Times available 13488.00 13488.00 13488.00
Times chosen 4897.00 4172.00 4419.00
Percentage chosen overall 36.31 30.93 32.76
Percentage chosen when available 36.31 30.93 32.76
Classical covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.002053 0.001814 -1.6387e-04 -3.1367e-04
asc_klein 0.001814 0.002069 -1.5603e-04 -3.0430e-04
b_groesse -1.6387e-04 -1.5603e-04 3.4904e-04 -1.202e-05
b_entfernung -3.1367e-04 -3.0430e-04 -1.202e-05 1.9293e-04
b_gemeinschaft -2.0216e-04 -2.8784e-04 2.470e-05 -6.303e-06
b_kultur -2.6580e-04 -2.4705e-04 -2.023e-05 -2.996e-06
b_umweltbildung -2.1667e-04 -2.4558e-04 -1.281e-05 -1.625e-07
b_zugang 1.0691e-04 9.287e-05 2.746e-06 -1.227e-05
b_gestaltung -2.0075e-04 -1.9965e-04 -9.919e-06 2.088e-06
b_beitrag 8.5045e-04 8.1749e-04 7.331e-05 -1.0993e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -2.0216e-04 -2.6580e-04 -2.1667e-04 1.0691e-04
asc_klein -2.8784e-04 -2.4705e-04 -2.4558e-04 9.287e-05
b_groesse 2.470e-05 -2.023e-05 -1.281e-05 2.746e-06
b_entfernung -6.303e-06 -2.996e-06 -1.625e-07 -1.227e-05
b_gemeinschaft 4.9093e-04 1.591e-05 -3.279e-05 5.371e-06
b_kultur 1.591e-05 4.7623e-04 2.616e-05 2.690e-06
b_umweltbildung -3.279e-05 2.616e-05 5.0584e-04 1.146e-05
b_zugang 5.371e-06 2.690e-06 1.146e-05 4.204e-05
b_gestaltung -5.820e-06 -1.460e-05 3.842e-05 1.075e-05
b_beitrag -3.994e-06 -9.985e-06 1.0352e-04 5.433e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.0075e-04 8.5045e-04
asc_klein -1.9965e-04 8.1749e-04
b_groesse -9.919e-06 7.331e-05
b_entfernung 2.088e-06 -1.0993e-04
b_gemeinschaft -5.820e-06 -3.994e-06
b_kultur -1.460e-05 -9.985e-06
b_umweltbildung 3.842e-05 1.0352e-04
b_zugang 1.075e-05 5.433e-05
b_gestaltung 4.8939e-04 1.4109e-04
b_beitrag 1.4109e-04 0.001659
Robust covariance matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 0.003923 0.003495 3.434e-05 -4.2294e-04
asc_klein 0.003495 0.004093 2.363e-05 -4.1771e-04
b_groesse 3.434e-05 2.363e-05 3.1245e-04 -3.722e-05
b_entfernung -4.2294e-04 -4.1771e-04 -3.722e-05 2.1309e-04
b_gemeinschaft -1.6873e-04 -2.9513e-04 5.110e-05 2.639e-05
b_kultur -3.5157e-04 -3.1605e-04 -7.567e-05 -1.475e-06
b_umweltbildung -3.2896e-04 -3.5905e-04 -7.914e-05 2.515e-05
b_zugang 1.1863e-04 1.0116e-04 2.347e-05 -1.619e-05
b_gestaltung -2.8127e-04 -2.6983e-04 1.694e-05 -5.223e-07
b_beitrag 0.001198 0.001162 6.508e-05 -1.3369e-04
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -1.6873e-04 -3.5157e-04 -3.2896e-04 1.1863e-04
asc_klein -2.9513e-04 -3.1605e-04 -3.5905e-04 1.0116e-04
b_groesse 5.110e-05 -7.567e-05 -7.914e-05 2.347e-05
b_entfernung 2.639e-05 -1.475e-06 2.515e-05 -1.619e-05
b_gemeinschaft 4.2531e-04 -4.435e-05 -5.416e-05 1.428e-05
b_kultur -4.435e-05 5.8109e-04 1.5797e-04 -2.759e-06
b_umweltbildung -5.416e-05 1.5797e-04 4.7231e-04 2.230e-06
b_zugang 1.428e-05 -2.759e-06 2.230e-06 4.709e-05
b_gestaltung 8.182e-06 1.882e-05 5.443e-05 2.065e-05
b_beitrag -4.080e-05 7.814e-05 4.488e-05 5.799e-05
b_gestaltung b_beitrag
asc_gemeinschaft -2.8127e-04 0.001198
asc_klein -2.6983e-04 0.001162
b_groesse 1.694e-05 6.508e-05
b_entfernung -5.223e-07 -1.3369e-04
b_gemeinschaft 8.182e-06 -4.080e-05
b_kultur 1.882e-05 7.814e-05
b_umweltbildung 5.443e-05 4.488e-05
b_zugang 2.065e-05 5.799e-05
b_gestaltung 5.9997e-04 6.392e-05
b_beitrag 6.392e-05 0.001975
Classical correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.0000 0.8803 -0.19360 -0.498445
asc_klein 0.8803 1.0000 -0.18363 -0.481678
b_groesse -0.1936 -0.1836 1.00000 -0.046311
b_entfernung -0.4984 -0.4817 -0.04631 1.000000
b_gemeinschaft -0.2014 -0.2856 0.05966 -0.020479
b_kultur -0.2688 -0.2489 -0.04963 -0.009884
b_umweltbildung -0.2126 -0.2401 -0.03049 -5.2023e-04
b_zugang 0.3639 0.3149 0.02267 -0.136294
b_gestaltung -0.2003 -0.1984 -0.02400 0.006795
b_beitrag 0.4609 0.4413 0.09633 -0.194317
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.201388 -0.268838 -0.21263 0.36394
asc_klein -0.285625 -0.248906 -0.24007 0.31491
b_groesse 0.059660 -0.049629 -0.03049 0.02267
b_entfernung -0.020479 -0.009884 -5.2023e-04 -0.13629
b_gemeinschaft 1.000000 0.032902 -0.06580 0.03739
b_kultur 0.032902 1.000000 0.05329 0.01901
b_umweltbildung -0.065797 0.053294 1.00000 0.07859
b_zugang 0.037389 0.019014 0.07859 1.00000
b_gestaltung -0.011875 -0.030242 0.07721 0.07495
b_beitrag -0.004425 -0.011234 0.11301 0.20571
b_gestaltung b_beitrag
asc_gemeinschaft -0.200300 0.460867
asc_klein -0.198426 0.441279
b_groesse -0.024000 0.096334
b_entfernung 0.006795 -0.194317
b_gemeinschaft -0.011875 -0.004425
b_kultur -0.030242 -0.011234
b_umweltbildung 0.077213 0.113009
b_zugang 0.074949 0.205711
b_gestaltung 1.000000 0.156582
b_beitrag 0.156582 1.000000
Robust correlation matrix:
asc_gemeinschaft asc_klein b_groesse b_entfernung
asc_gemeinschaft 1.00000 0.87208 0.03102 -0.462580
asc_klein 0.87208 1.00000 0.02090 -0.447283
b_groesse 0.03102 0.02090 1.00000 -0.144228
b_entfernung -0.46258 -0.44728 -0.14423 1.000000
b_gemeinschaft -0.13063 -0.22368 0.14018 0.087646
b_kultur -0.23285 -0.20494 -0.17759 -0.004193
b_umweltbildung -0.24167 -0.25824 -0.20602 0.079269
b_zugang 0.27600 0.23043 0.19348 -0.161624
b_gestaltung -0.18333 -0.17219 0.03913 -0.001461
b_beitrag 0.43033 0.40877 0.08285 -0.206098
b_gemeinschaft b_kultur b_umweltbildung b_zugang
asc_gemeinschaft -0.13063 -0.232852 -0.24167 0.27600
asc_klein -0.22368 -0.204937 -0.25824 0.23043
b_groesse 0.14018 -0.177587 -0.20602 0.19348
b_entfernung 0.08765 -0.004193 0.07927 -0.16162
b_gemeinschaft 1.00000 -0.089205 -0.12085 0.10090
b_kultur -0.08921 1.000000 0.30154 -0.01668
b_umweltbildung -0.12085 0.301544 1.00000 0.01495
b_zugang 0.10090 -0.016679 0.01495 1.00000
b_gestaltung 0.01620 0.031878 0.10226 0.12286
b_beitrag -0.04451 0.072940 0.04647 0.19015
b_gestaltung b_beitrag
asc_gemeinschaft -0.183334 0.43033
asc_klein -0.172191 0.40877
b_groesse 0.039126 0.08285
b_entfernung -0.001461 -0.20610
b_gemeinschaft 0.016198 -0.04451
b_kultur 0.031878 0.07294
b_umweltbildung 0.102258 0.04647
b_zugang 0.122864 0.19015
b_gestaltung 1.000000 0.05873
b_beitrag 0.058727 1.00000
20 worst outliers in terms of lowest average per choice prediction:
ID Avg prob per choice
1863 0.2412086
1735 0.2484631
10182 0.2524603
10214 0.2544820
1807 0.2562983
1315 0.2565500
1074 0.2566505
1784 0.2566725
1205 0.2569582
1812 0.2569582
867 0.2575401
10892 0.2600787
1670 0.2606873
10581 0.2606901
10020 0.2630297
10744 0.2633822
10311 0.2639950
1579 0.2642995
151 0.2656306
1947 0.2666865
Changes in parameter estimates from starting values:
Initial Estimate Difference
asc_gemeinschaft 0.61000 0.59703 -0.01297
asc_klein 0.43000 0.39320 -0.03680
b_groesse 0.00000 0.07265 0.07265
b_entfernung -0.17000 -0.22544 -0.05544
b_gemeinschaft 0.06000 0.12696 0.06696
b_kultur 0.06000 0.15736 0.09736
b_umweltbildung 0.13000 0.14462 0.01462
b_zugang 0.05000 0.07087 0.02087
b_gestaltung 0.31000 0.38512 0.07512
b_beitrag -0.12000 1.15467 1.27467
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "modelclogitbase"
modelDescr "Conditional Logit in preference space without Interaction Terms"
indivID "ID"
mixing "FALSE"
HB "FALSE"
nCores "1"
outputDirectory "modeloutput/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling in estimation
---------------------
Value
asc_gemeinschaft 0.59702793
asc_klein 0.39319962
b_groesse 0.07265481
b_entfernung 0.22543850
b_gemeinschaft 0.12696058
b_kultur 0.15735945
b_umweltbildung 0.14461548
b_zugang 0.07087200
b_gestaltung 0.38512237
b_beitrag 1.15466844
Scaling used in computing Hessian
---------------------------------
Value
asc_gemeinschaft 0.59702783
asc_klein 0.39319969
b_groesse 0.07265481
b_entfernung 0.22543845
b_gemeinschaft 0.12696059
b_kultur 0.15735944
b_umweltbildung 0.14461547
b_zugang 0.07087200
b_gestaltung 0.38512245
b_beitrag 1.15466801
apollo_probabilities
----------------------
function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
### List of utilities (later integrated in mnl_settings below)
V = list()
V[['alt1']] =
asc_gemeinschaft +
b_groesse * GROESSE.1 +
b_entfernung * ENTFERNUNG.1 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
b_kultur * KULTURVERANSTALTUNGEN.1 +
b_umweltbildung * UMWELTBILDUNG.1 +
b_zugang * ZUGANG.1 +
b_gestaltung * GESTALTUNG.1 -
b_beitrag*BEITRAG.1
V[['alt2']] =
asc_klein +
b_groesse * GROESSE.2 +
b_entfernung * ENTFERNUNG.2 +
b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
b_kultur * KULTURVERANSTALTUNGEN.2 +
b_umweltbildung * UMWELTBILDUNG.2 +
b_zugang * ZUGANG.2 +
b_gestaltung * GESTALTUNG.2 -
b_beitrag* BEITRAG.2
V[['alt3']] = 0
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = 1, # all alternatives are available in every choice
choiceVar = choice,
V = V#, # tell function to use list vector defined above
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
modeloutput/modelclogitbase_OLD5_estimates.csv 0 → 100644
+ 11
− 0
View file @ 56f7d898
"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
modeloutput/modelclogitbase_OLD5_model.rds 0 → 100644
+ 0
− 0
View file @ 56f7d898
File added
modeloutput/modelclogitbase_OLD5_output.txt 0 → 100644
+ 369
− 0
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Model run by dj44vuri using Apollo 0.2.9 on R 4.3.1 for Windows.
www.ApolloChoiceModelling.com
Model name : modelclogitbase
Model description : Conditional Logit in preference space without Interaction Terms
Model run at : 2023-07-27 15:47:46.710214
Estimation method : bfgs
Model diagnosis : successful convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definitive
maximum eigenvalue : -221.009149
Number of individuals : 1686
Number of rows in database : 13488
Number of modelled outcomes : 13488
Number of cores used : 1
Model without mixing
LL(start) : -15096.79
LL at equal shares, LL(0) : -14818.08
LL at observed shares, LL(C) : -14788.08
LL(final) : -14140.77
Rho-squared vs equal shares : 0.0457
Adj.Rho-squared vs equal shares : 0.045
Rho-squared vs observed shares : 0.0438
Adj.Rho-squared vs observed shares : 0.0432
AIC : 28301.54
BIC : 28376.64
Estimated parameters : 10
Time taken (hh:mm:ss) : 00:00:6.18
pre-estimation : 00:00:2.07
estimation : 00:00:2.02
initial estimation : 00:00:1.86
estimation after rescaling : 00:00:0.15
post-estimation : 00:00:2.09
Iterations : 19
initial estimation : 18
estimation after rescaling : 1
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_gemeinschaft 0.59703 0.045306 13.178 0.062635 9.532
asc_klein 0.39320 0.045483 8.645 0.063976 6.146
b_groesse 0.07265 0.018682 3.889 0.017676 4.110
b_entfernung -0.22544 0.013890 -16.230 0.014598 -15.444
b_gemeinschaft 0.12696 0.022157 5.730 0.020623 6.156
b_kultur 0.15736 0.021823 7.211 0.024106 6.528
b_umweltbildung 0.14462 0.022491 6.430 0.021733 6.654
b_zugang 0.07087 0.006484 10.930 0.006862 10.328
b_gestaltung 0.38512 0.022122 17.409 0.024494 15.723
b_beitrag 1.15467 0.040731 28.349 0.044438 25.984
Overview of choices for MNL model component :
alt1 alt2 alt3
Times available 13488.00 13488.00 13488.00
Times chosen 4897.00 4172.00 4419.00
Percentage chosen overall 36.31 30.93 32.76
Percentage chosen when available 36.31 30.93 32.76
Classical covariance matrix:
asc_gemeinschaft asc_klein b_groesse
asc_gemeinschaft 0.002053 0.001814 -1.6387e-04
asc_klein 0.001814 0.002069 -1.5603e-04
b_groesse -1.6387e-04 -1.5603e-04 3.4904e-04
b_entfernung -3.1367e-04 -3.0430e-04 -1.202e-05
b_gemeinschaft -2.0216e-04 -2.8784e-04 2.470e-05
b_kultur -2.6580e-04 -2.4705e-04 -2.023e-05
b_umweltbildung -2.1667e-04 -2.4558e-04 -1.281e-05
b_zugang 1.0691e-04 9.287e-05 2.746e-06
b_gestaltung -2.0075e-04 -1.9965e-04 -9.919e-06
b_beitrag 8.5045e-04 8.1749e-04 7.331e-05
b_entfernung b_gemeinschaft b_kultur
asc_gemeinschaft -3.1367e-04 -2.0216e-04 -2.6580e-04
asc_klein -3.0430e-04 -2.8784e-04 -2.4705e-04
b_groesse -1.202e-05 2.470e-05 -2.023e-05
b_entfernung 1.9293e-04 -6.303e-06 -2.996e-06
b_gemeinschaft -6.303e-06 4.9093e-04 1.591e-05
b_kultur -2.996e-06 1.591e-05 4.7623e-04
b_umweltbildung -1.625e-07 -3.279e-05 2.616e-05
b_zugang -1.227e-05 5.371e-06 2.690e-06
b_gestaltung 2.088e-06 -5.820e-06 -1.460e-05
b_beitrag -1.0993e-04 -3.994e-06 -9.985e-06
b_umweltbildung b_zugang b_gestaltung
asc_gemeinschaft -2.1667e-04 1.0691e-04 -2.0075e-04
asc_klein -2.4558e-04 9.287e-05 -1.9965e-04
b_groesse -1.281e-05 2.746e-06 -9.919e-06
b_entfernung -1.625e-07 -1.227e-05 2.088e-06
b_gemeinschaft -3.279e-05 5.371e-06 -5.820e-06
b_kultur 2.616e-05 2.690e-06 -1.460e-05
b_umweltbildung 5.0584e-04 1.146e-05 3.842e-05
b_zugang 1.146e-05 4.204e-05 1.075e-05
b_gestaltung 3.842e-05 1.075e-05 4.8939e-04
b_beitrag 1.0352e-04 5.433e-05 1.4109e-04
b_beitrag
asc_gemeinschaft 8.5045e-04
asc_klein 8.1749e-04
b_groesse 7.331e-05
b_entfernung -1.0993e-04
b_gemeinschaft -3.994e-06
b_kultur -9.985e-06
b_umweltbildung 1.0352e-04
b_zugang 5.433e-05
b_gestaltung 1.4109e-04
b_beitrag 0.001659
Robust covariance matrix:
asc_gemeinschaft asc_klein b_groesse
asc_gemeinschaft 0.003923 0.003495 3.434e-05
asc_klein 0.003495 0.004093 2.363e-05
b_groesse 3.434e-05 2.363e-05 3.1245e-04
b_entfernung -4.2294e-04 -4.1771e-04 -3.722e-05
b_gemeinschaft -1.6873e-04 -2.9513e-04 5.110e-05
b_kultur -3.5157e-04 -3.1605e-04 -7.567e-05
b_umweltbildung -3.2896e-04 -3.5905e-04 -7.914e-05
b_zugang 1.1863e-04 1.0116e-04 2.347e-05
b_gestaltung -2.8127e-04 -2.6983e-04 1.694e-05
b_beitrag 0.001198 0.001162 6.508e-05
b_entfernung b_gemeinschaft b_kultur
asc_gemeinschaft -4.2294e-04 -1.6873e-04 -3.5157e-04
asc_klein -4.1771e-04 -2.9513e-04 -3.1605e-04
b_groesse -3.722e-05 5.110e-05 -7.567e-05
b_entfernung 2.1309e-04 2.639e-05 -1.475e-06
b_gemeinschaft 2.639e-05 4.2531e-04 -4.435e-05
b_kultur -1.475e-06 -4.435e-05 5.8109e-04
b_umweltbildung 2.515e-05 -5.416e-05 1.5797e-04
b_zugang -1.619e-05 1.428e-05 -2.759e-06
b_gestaltung -5.223e-07 8.182e-06 1.882e-05
b_beitrag -1.3369e-04 -4.080e-05 7.814e-05
b_umweltbildung b_zugang b_gestaltung
asc_gemeinschaft -3.2896e-04 1.1863e-04 -2.8127e-04
asc_klein -3.5905e-04 1.0116e-04 -2.6983e-04
b_groesse -7.914e-05 2.347e-05 1.694e-05
b_entfernung 2.515e-05 -1.619e-05 -5.223e-07
b_gemeinschaft -5.416e-05 1.428e-05 8.182e-06
b_kultur 1.5797e-04 -2.759e-06 1.882e-05
b_umweltbildung 4.7231e-04 2.230e-06 5.443e-05
b_zugang 2.230e-06 4.709e-05 2.065e-05
b_gestaltung 5.443e-05 2.065e-05 5.9997e-04
b_beitrag 4.488e-05 5.799e-05 6.392e-05
b_beitrag
asc_gemeinschaft 0.001198
asc_klein 0.001162
b_groesse 6.508e-05
b_entfernung -1.3369e-04
b_gemeinschaft -4.080e-05
b_kultur 7.814e-05
b_umweltbildung 4.488e-05
b_zugang 5.799e-05
b_gestaltung 6.392e-05
b_beitrag 0.001975
Classical correlation matrix:
asc_gemeinschaft asc_klein b_groesse
asc_gemeinschaft 1.0000 0.8803 -0.19360
asc_klein 0.8803 1.0000 -0.18363
b_groesse -0.1936 -0.1836 1.00000
b_entfernung -0.4984 -0.4817 -0.04631
b_gemeinschaft -0.2014 -0.2856 0.05966
b_kultur -0.2688 -0.2489 -0.04963
b_umweltbildung -0.2126 -0.2401 -0.03049
b_zugang 0.3639 0.3149 0.02267
b_gestaltung -0.2003 -0.1984 -0.02400
b_beitrag 0.4609 0.4413 0.09633
b_entfernung b_gemeinschaft b_kultur
asc_gemeinschaft -0.498445 -0.201388 -0.268838
asc_klein -0.481678 -0.285625 -0.248906
b_groesse -0.046311 0.059660 -0.049629
b_entfernung 1.000000 -0.020479 -0.009884
b_gemeinschaft -0.020479 1.000000 0.032902
b_kultur -0.009884 0.032902 1.000000
b_umweltbildung -5.2023e-04 -0.065797 0.053294
b_zugang -0.136294 0.037389 0.019014
b_gestaltung 0.006795 -0.011875 -0.030242
b_beitrag -0.194317 -0.004425 -0.011234
b_umweltbildung b_zugang b_gestaltung
asc_gemeinschaft -0.21263 0.36394 -0.200300
asc_klein -0.24007 0.31491 -0.198426
b_groesse -0.03049 0.02267 -0.024000
b_entfernung -5.2023e-04 -0.13629 0.006795
b_gemeinschaft -0.06580 0.03739 -0.011875
b_kultur 0.05329 0.01901 -0.030242
b_umweltbildung 1.00000 0.07859 0.077213
b_zugang 0.07859 1.00000 0.074949
b_gestaltung 0.07721 0.07495 1.000000
b_beitrag 0.11301 0.20571 0.156582
b_beitrag
asc_gemeinschaft 0.460867
asc_klein 0.441279
b_groesse 0.096334
b_entfernung -0.194317
b_gemeinschaft -0.004425
b_kultur -0.011234
b_umweltbildung 0.113009
b_zugang 0.205711
b_gestaltung 0.156582
b_beitrag 1.000000
Robust correlation matrix:
asc_gemeinschaft asc_klein b_groesse
asc_gemeinschaft 1.00000 0.87208 0.03102
asc_klein 0.87208 1.00000 0.02090
b_groesse 0.03102 0.02090 1.00000
b_entfernung -0.46258 -0.44728 -0.14423
b_gemeinschaft -0.13063 -0.22368 0.14018
b_kultur -0.23285 -0.20494 -0.17759
b_umweltbildung -0.24167 -0.25824 -0.20602
b_zugang 0.27600 0.23043 0.19348
b_gestaltung -0.18333 -0.17219 0.03913
b_beitrag 0.43033 0.40877 0.08285
b_entfernung b_gemeinschaft b_kultur
asc_gemeinschaft -0.462580 -0.13063 -0.232852
asc_klein -0.447283 -0.22368 -0.204937
b_groesse -0.144228 0.14018 -0.177587
b_entfernung 1.000000 0.08765 -0.004193
b_gemeinschaft 0.087646 1.00000 -0.089205
b_kultur -0.004193 -0.08921 1.000000
b_umweltbildung 0.079269 -0.12085 0.301544
b_zugang -0.161624 0.10090 -0.016679
b_gestaltung -0.001461 0.01620 0.031878
b_beitrag -0.206098 -0.04451 0.072940
b_umweltbildung b_zugang b_gestaltung
asc_gemeinschaft -0.24167 0.27600 -0.183334
asc_klein -0.25824 0.23043 -0.172191
b_groesse -0.20602 0.19348 0.039126
b_entfernung 0.07927 -0.16162 -0.001461
b_gemeinschaft -0.12085 0.10090 0.016198
b_kultur 0.30154 -0.01668 0.031878
b_umweltbildung 1.00000 0.01495 0.102258
b_zugang 0.01495 1.00000 0.122864
b_gestaltung 0.10226 0.12286 1.000000
b_beitrag 0.04647 0.19015 0.058727
b_beitrag
asc_gemeinschaft 0.43033
asc_klein 0.40877
b_groesse 0.08285
b_entfernung -0.20610
b_gemeinschaft -0.04451
b_kultur 0.07294
b_umweltbildung 0.04647
b_zugang 0.19015
b_gestaltung 0.05873
b_beitrag 1.00000
20 worst outliers in terms of lowest average per choice prediction:
ID Avg prob per choice
1863 0.2412086
1735 0.2484631
10182 0.2524603
10214 0.2544820
1807 0.2562983
1315 0.2565500
1074 0.2566505
1784 0.2566725
1205 0.2569582
1812 0.2569582
867 0.2575401
10892 0.2600787
1670 0.2606873
10581 0.2606901
10020 0.2630297
10744 0.2633822
10311 0.2639950
1579 0.2642995
151 0.2656306
1947 0.2666865
Changes in parameter estimates from starting values:
Initial Estimate Difference
asc_gemeinschaft 0.61000 0.59703 -0.01297
asc_klein 0.43000 0.39320 -0.03680
b_groesse 0.00000 0.07265 0.07265
b_entfernung -0.17000 -0.22544 -0.05544
b_gemeinschaft 0.06000 0.12696 0.06696
b_kultur 0.06000 0.15736 0.09736
b_umweltbildung 0.13000 0.14462 0.01462
b_zugang 0.05000 0.07087 0.02087
b_gestaltung 0.31000 0.38512 0.07512
b_beitrag -0.12000 1.15467 1.27467
Settings and functions used in model definition:
apollo_control
--------------
Value
modelName "modelclogitbase"
modelDescr "Conditional Logit in preference space without Interaction Terms"
indivID "ID"
mixing "FALSE"
HB "FALSE"
nCores "1"
outputDirectory "modeloutput/"
debug "FALSE"
workInLogs "FALSE"
seed "13"
noValidation "FALSE"
noDiagnostics "FALSE"
calculateLLC "TRUE"
panelData "TRUE"
analyticGrad "TRUE"
analyticGrad_manualSet "FALSE"
overridePanel "FALSE"
preventOverridePanel "FALSE"
noModification "FALSE"
Hessian routines attempted
--------------------------
numerical jacobian of LL analytical gradient
Scaling in estimation
---------------------
Value
asc_gemeinschaft 0.59702793
asc_klein 0.39319962
b_groesse 0.07265481
b_entfernung 0.22543850
b_gemeinschaft 0.12696058
b_kultur 0.15735945
b_umweltbildung 0.14461548
b_zugang 0.07087200
b_gestaltung 0.38512237
b_beitrag 1.15466844
Scaling used in computing Hessian
---------------------------------
Value
asc_gemeinschaft 0.59702783
asc_klein 0.39319969
b_groesse 0.07265481
b_entfernung 0.22543845
b_gemeinschaft 0.12696059
b_kultur 0.15735944
b_umweltbildung 0.14461547
b_zugang 0.07087200
b_gestaltung 0.38512245
b_beitrag 1.15466801
apollo_probabilities
----------------------
function (apollo_beta, apollo_inputs, functionality = "estimate")
{
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
P = list()
V = list()
V[["alt1"]] = asc_gemeinschaft + b_groesse * GROESSE.1 +
b_entfernung * ENTFERNUNG.1 + b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
b_kultur * KULTURVERANSTALTUNGEN.1 + b_umweltbildung *
UMWELTBILDUNG.1 + b_zugang * ZUGANG.1 + b_gestaltung *
GESTALTUNG.1 - b_beitrag * BEITRAG.1
V[["alt2"]] = asc_klein + b_groesse * GROESSE.2 + b_entfernung *
ENTFERNUNG.2 + b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
b_kultur * KULTURVERANSTALTUNGEN.2 + b_umweltbildung *
UMWELTBILDUNG.2 + b_zugang * ZUGANG.2 + b_gestaltung *
GESTALTUNG.2 - b_beitrag * BEITRAG.2
V[["alt3"]] = 0
mnl_settings = list(alternatives = c(alt1 = 1, alt2 = 2,
alt3 = 3), avail = 1, choiceVar = choice, V = V)
P[["model"]] = apollo_mnl(mnl_settings, functionality)
P = apollo_panelProd(P, apollo_inputs, functionality)
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
modeloutput/modelclogitbase_estimates.csv 0 → 100644
+ 11
− 0
View file @ 56f7d898
"","Estimate","Std.err.","t-ratio(0)","Rob.std.err.","Rob.t-ratio(0)"
"asc_gemeinschaft",0.597027833410306,0.0453059110864622,13.1777028447995,0.0626346307891028,9.53191271168436
"asc_klein",0.393199686001952,0.0454831076101558,8.644960880249,0.0639759143691876,6.14605808887551
"b_groesse",0.0726548108009911,0.0186824937221467,3.88892467362985,0.0176763452795552,4.11028465737344
"b_entfernung",-0.225438445957915,0.0138898130977684,-16.2304880829631,0.0145975628320559,-15.4435674332469
"b_gemeinschaft",0.126960587776298,0.0221570374622404,5.73003444132194,0.0206230781566777,6.15623850192259
"b_kultur",0.157359440898145,0.0218226538728148,7.21082971004608,0.0241058361896908,6.52785655970908
"b_umweltbildung",0.144615472041501,0.0224909825875373,6.42993126150192,0.0217328013418834,6.65424902047942
"b_zugang",0.0708720030222938,0.00648390583823709,10.9304491444563,0.00686229505352955,10.3277405692198
"b_gestaltung",0.385122450057836,0.0221220734560692,17.4089671487,0.0244942499169282,15.722973814833
"b_beitrag",1.15466801164802,0.0407306187129726,28.3488944713786,0.0444383408071125,25.9835986374904
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