[Limdep Nlogit List] structure for testing interaction effects in opt out alternative
William Greene
wgreene at stern.nyu.edu
Thu Apr 9 23:40:38 AEST 2020
Jason. It's not clear to me how the models you show below are revealing
information
about the effect of the demographics on the probability of "OptOut." That
is, what
specific results show what you are looking for? That said, however, given
the way
you have phrased the question, I would have built this as a binary choice
model in
which the dependent variable equals 1 if choose OptOut and 0 if something
else. The
partial effects from that model would address what are looking for.
/Bill Greene
On Wed, Apr 8, 2020 at 7:11 PM Jason Ong via Limdep <
limdep at mailman.sydney.edu.au> wrote:
> hello,
>
> I am not sure if my post below got through?
> thanks 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 (https://protect-au.mimecast.com/s/novECQnMBZf3q1O5HxvjgM?domain=ashm.org.au)
> https://protect-au.mimecast.com/s/D_P9CROND2uRlDEAfNc7N9?domain=lshtm.ac.uk
> https://protect-au.mimecast.com/s/JoYICVARKgC5Kn3Dfy0wxD?domain=researchgate.net
>
>
>
>
> On Thu, Apr 2, 2020 at 1:29 PM Jason Ong <doctorjasonong at gmail.com> wrote:
>
> > 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 (https://protect-au.mimecast.com/s/novECQnMBZf3q1O5HxvjgM?domain=ashm.org.au)
> > https://protect-au.mimecast.com/s/D_P9CROND2uRlDEAfNc7N9?domain=lshtm.ac.uk
> > https://protect-au.mimecast.com/s/JoYICVARKgC5Kn3Dfy0wxD?domain=researchgate.net
> >
> >
> >
> _______________________________________________
> Limdep site list
> Limdep at mailman.sydney.edu.au
> http://limdep.itls.usyd.edu.au
>
>
--
William Greene
Department of Economics, emeritus
Stern School of Business, New York University
44 West 4 St.
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/YU5CCWLVXkUPYLOAfn1j4S?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.646.596.3296
Editor in Chief: Journal of Productivity Analysis
Editor in Chief: Foundations and Trends in Econometrics
Associate Editor: Economics Letters
Associate Editor: Journal of Business and Economic Statistics
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