[Limdep Nlogit List] structure for testing interaction effects in opt out alternative
Jason Ong
doctorjasonong at gmail.com
Thu Apr 9 09:11:03 AEST 2020
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/93FXClx1NjiA0QwjhGnh4B?domain=ashm.org.au)
https://protect-au.mimecast.com/s/tJsoCmO5gluABOo9hOQWmB?domain=lshtm.ac.uk
https://protect-au.mimecast.com/s/wWzCCnx1jniKA8ZkhNgHP9?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/93FXClx1NjiA0QwjhGnh4B?domain=ashm.org.au)
> https://protect-au.mimecast.com/s/tJsoCmO5gluABOo9hOQWmB?domain=lshtm.ac.uk
> https://protect-au.mimecast.com/s/wWzCCnx1jniKA8ZkhNgHP9?domain=researchgate.net
>
>
>
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