[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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