[Limdep Nlogit List] structure for testing interaction effects in opt out alternative
Jason Ong
doctorjasonong at gmail.com
Thu Apr 2 13:29:03 AEDT 2020
Hi,
I would like to explore the effect of sociodemographic characteristics for
people choosing the opt out alternative in my DCE.
When I put the interaction terms for my opt out alternative in the main
model, is there a preference of just adding the sociodemographic terms
(Option 1 - see text in red below) OR creating an interaction term with the
opt out alternative (Option 2 - see text in red below)
they actually give quite different results.
*OPTION 1*
NLOGIT
; Lhs = choicev, cset, altij
; Choices = A,B,C
; rpl
?; Correlated
; fcn=cost1(n), cost2(n), cost3(n),loc1(n), loc2(n), loc3(n), loc4(n),
loc5(n), loc6(n), loc7(n), mode1(n), mode2(n), pers1(n), pers2(n),
pers3(n), hivmed(n)
; pds=12
; pts=10
? 10 for tests, between 500-1000 iterations for final model (let it run
overnight)
; halton
; Model:
U(A,B)=cost1*cost1+cost2*cost2+cost3*cost3+loc1*loc1+loc2*loc2+loc3*loc3+loc4*loc4+loc5*loc5+loc6*loc6+loc7*loc7
+mode1*mode1+mode2*mode2+pers1*pers1+pers2*pers2+pers3*pers3+hivmed*hivmed/
U(C)=neither + young*young + male*male + second*second + eversex*eversex +
evertest*evertest$
-----------------------------------------------------------------------------
Random Parameters Multinom. Logit Model
Dependent variable CHOICEV
Log likelihood function -2750.06883
Restricted log likelihood -3559.50382
Chi squared [ 38](P= .000) 1618.86998
Significance level .00000
McFadden Pseudo R-squared .2274011
Estimation based on N = 3240, K = 38
Inf.Cr.AIC = 5576.1 AIC/N = 1.721
---------------------------------------
Log likelihood R-sqrd R2Adj
No coefficients -3559.5038 .2274 .2228
Constants only can be computed directly
Use NLOGIT ;...;RHS=ONE$
At start values -2781.2815 .0112 .0054
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Replications for simulated probs. = 10
Used Halton sequences in simulations.
RPL model with panel has 270 groups
Fixed number of obsrvs./group= 12
Number of obs.= 3240, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOICEV| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
|Random parameters in utility functions..........................
COST1| .14649*** .04458 3.29 .0010 .05912 .23386
COST2| -.15473*** .04625 -3.35 .0008 -.24537 -.06409
COST3| -.59762*** .05661 -10.56 .0000 -.70857 -.48666
LOC1| .10425 .07731 1.35 .1775 -.04726 .25577
LOC2| .09492 .07367 1.29 .1976 -.04947 .23931
LOC3| -.22262*** .07107 -3.13 .0017 -.36191 -.08333
LOC4| -.16815** .07468 -2.25 .0244 -.31453 -.02177
LOC5| .11153 .07643 1.46 .1445 -.03827 .26132
LOC6| -.16419** .07699 -2.13 .0329 -.31508 -.01330
LOC7| .15955** .07811 2.04 .0411 .00645 .31265
MODE1| .20164*** .03570 5.65 .0000 .13166 .27161
MODE2| -.06031 .03723 -1.62 .1052 -.13328 .01265
PERS1| -.07449* .04442 -1.68 .0935 -.16154 .01256
PERS2| -.00084 .04654 -.02 .9857 -.09205 .09038
PERS3| -.06296 .06329 -.99 .3198 -.18700 .06108
HIVMED| -.16880*** .02146 -7.87 .0000 -.21086 -.12675
|Nonrandom parameters in utility functions.......................
NEITHER| -1.63089*** .08338 -19.56 .0000 -1.79431 -1.46747
YOUNG| .03880 .07502 .52 .6050 -.10824 .18583
MALE| .05301 .06767 .78 .4335 -.07963 .18564
SECOND| .14195* .07599 1.87 .0617 -.00698 .29088
EVERSEX| .19307*** .06955 2.78 .0055 .05676 .32939
EVERTEST| -.05922 .07121 -.83 .4057 -.19879 .08036
|Distns. of RPs. Std.Devs or limits of triangular................
NsCOST1| .19758** .09131 2.16 .0305 .01862 .37655
NsCOST2| .21214* .11753 1.81 .0711 -.01820 .44249
NsCOST3| .49293*** .07104 6.94 .0000 .35369 .63216
NsLOC1| .24263** .11400 2.13 .0333 .01920 .46606
NsLOC2| .16908* .09025 1.87 .0610 -.00780 .34596
NsLOC3| .07511 .10010 .75 .4531 -.12109 .27130
NsLOC4| .00998 .10294 .10 .9227 -.19177 .21174
NsLOC5| .07192 .12174 .59 .5547 -.16669 .31052
NsLOC6| .20690** .10337 2.00 .0453 .00430 .40949
NsLOC7| .07074 .09646 .73 .4633 -.11831 .25979
NsMODE1| .08671* .04435 1.96 .0505 -.00021 .17363
NsMODE2| .14239*** .04517 3.15 .0016 .05386 .23092
NsPERS1| .03078 .04556 .68 .4993 -.05852 .12007
NsPERS2| .05790 .04805 1.20 .2282 -.03628 .15209
NsPERS3| .04128 .06577 .63 .5302 -.08762 .17018
NsHIVMED| .03298 .03951 .83 .4040 -.04447 .11042
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Mar 27, 2020 at 00:45:24 PM
-----------------------------------------------------------------------------
or
*OPTION 2*
I created interaction terms here
e.g. you_nei = young*neither
NLOGIT
; Lhs = choicev, cset, altij
; Choices = A,B,C
; rpl
?; Correlated
; fcn=cost1(n), cost2(n), cost3(n),loc1(n), loc2(n), loc3(n), loc4(n),
loc5(n), loc6(n), loc7(n), mode1(n), mode2(n), pers1(n), pers2(n),
pers3(n), hivmed(n)
; pds=12
; pts=10
? 10 for tests, between 500-1000 iterations for final model (let it run
overnight)
; halton
; Model:
U(A,B)=cost1*cost1+cost2*cost2+cost3*cost3+loc1*loc1+loc2*loc2+loc3*loc3+loc4*loc4+loc5*loc5+loc6*loc6+loc7*loc7
+mode1*mode1+mode2*mode2+pers1*pers1+pers2*pers2+pers3*pers3+hivmed*hivmed/
U(C)=neither + you_nei*you_nei+ men_nei*men_nei + sec_nei*sec_nei +
sex_nei*sex_nei + tes_nei*tes_nei$
-----------------------------------------------------------------------------
Random Parameters Multinom. Logit Model
Dependent variable CHOICEV
Log likelihood function -2750.06883
Restricted log likelihood -3559.50382
Chi squared [ 38](P= .000) 1618.86998
Significance level .00000
McFadden Pseudo R-squared .2274011
Estimation based on N = 3240, K = 38
Inf.Cr.AIC = 5576.1 AIC/N = 1.721
---------------------------------------
Log likelihood R-sqrd R2Adj
No coefficients -3559.5038 .2274 .2228
Constants only can be computed directly
Use NLOGIT ;...;RHS=ONE$
At start values -2781.2815 .0112 .0054
Note: R-sqrd = 1 - logL/Logl(constants)
Warning: Model does not contain a full
set of ASCs. R-sqrd is problematic. Use
model setup with ;RHS=one to get LogL0.
---------------------------------------
Response data are given as ind. choices
Replications for simulated probs. = 10
Used Halton sequences in simulations.
RPL model with panel has 270 groups
Fixed number of obsrvs./group= 12
Number of obs.= 3240, skipped 0 obs
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
CHOICEV| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
|Random parameters in utility functions..........................
COST1| .14649*** .04458 3.29 .0010 .05912 .23386
COST2| -.15473*** .04625 -3.35 .0008 -.24537 -.06409
COST3| -.59762*** .05661 -10.56 .0000 -.70857 -.48666
LOC1| .10425 .07731 1.35 .1775 -.04726 .25577
LOC2| .09492 .07367 1.29 .1976 -.04947 .23931
LOC3| -.22262*** .07107 -3.13 .0017 -.36191 -.08333
LOC4| -.16815** .07468 -2.25 .0244 -.31453 -.02177
LOC5| .11153 .07643 1.46 .1445 -.03827 .26132
LOC6| -.16419** .07699 -2.13 .0329 -.31508 -.01330
LOC7| .15955** .07811 2.04 .0411 .00645 .31265
MODE1| .20164*** .03570 5.65 .0000 .13166 .27161
MODE2| -.06031 .03723 -1.62 .1052 -.13328 .01265
PERS1| -.07449* .04442 -1.68 .0935 -.16154 .01256
PERS2| -.00084 .04654 -.02 .9857 -.09205 .09038
PERS3| -.06296 .06329 -.99 .3198 -.18700 .06108
HIVMED| -.16880*** .02146 -7.87 .0000 -.21086 -.12675
|Nonrandom parameters in utility functions.......................
NEITHER| -1.63089*** .08338 -19.56 .0000 -1.79431 -1.46747
YOU_NEI| -.02335 .04515 -.52 .6050 -.11185 .06515
MEN_NEI| -.03191 .04073 -.78 .4335 -.11174 .04793
SEC_NEI| -.08544* .04574 -1.87 .0617 -.17508 .00420
SEX_NEI| -.11621*** .04186 -2.78 .0055 -.19826 -.03417
TES_NEI| .03564 .04286 .83 .4057 -.04837 .11965
|Distns. of RPs. Std.Devs or limits of triangular................
NsCOST1| .19758** .09131 2.16 .0305 .01862 .37655
NsCOST2| .21214* .11753 1.81 .0711 -.01820 .44249
NsCOST3| .49293*** .07104 6.94 .0000 .35369 .63216
NsLOC1| .24263** .11400 2.13 .0333 .01920 .46606
NsLOC2| .16908* .09025 1.87 .0610 -.00780 .34596
NsLOC3| .07511 .10010 .75 .4531 -.12109 .27130
NsLOC4| .00998 .10294 .10 .9227 -.19177 .21174
NsLOC5| .07192 .12174 .59 .5547 -.16669 .31052
NsLOC6| .20690** .10337 2.00 .0453 .00430 .40949
NsLOC7| .07074 .09646 .73 .4633 -.11831 .25979
NsMODE1| .08671* .04435 1.96 .0505 -.00021 .17363
NsMODE2| .14239*** .04517 3.15 .0016 .05386 .23092
NsPERS1| .03078 .04556 .68 .4993 -.05852 .12007
NsPERS2| .05790 .04805 1.20 .2282 -.03628 .15209
NsPERS3| .04128 .06577 .63 .5302 -.08762 .17018
NsHIVMED| .03298 .03951 .83 .4040 -.04447 .11042
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Mar 27, 2020 at 00:55:10 PM
-----------------------------------------------------------------------------
thank you for your help
Best,
*Jason Ong*
Twitter: @DrJasonJOng
PhD, MMed, MBBS, FAChSHM, FRACGP
Sexual Health Physician, Melbourne Sexual Health Centre, Alfred Health
Associate Professor (Hon), London School of Hygiene and Tropical Medicine,
UK
Central Clinical School, Monash University, Australia
Melbourne School of Population and Global Health, University of Melbourne,
Australia
Associate Editor, Sexually Transmitted Infections
Special Issues Editor, Sexual Health
Board Director, ASHM (www.ashm.org.au)
https://protect-au.mimecast.com/s/k35-CjZ1N7iGE2E6SWRL9Z?domain=lshtm.ac.uk
https://protect-au.mimecast.com/s/7TjeCk81N9tXl7lLcVYPnY?domain=researchgate.net
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