[Limdep Nlogit List] MNL Dummy Variable issues
contactemt
contactemt at bigfoot.com
Fri Oct 13 22:53:28 EST 2006
Sorry dont understand this:
>Choose this single alternative carefully.
Why? All alternatives are generic. What will be so special about the one I
choose?
The only distinction I have is that one of the alternatives is chosen in
each choice set.
> However, if you do this why would you not expect differences in the
> parameter
> for pRF1 across gender?
Does it matter? All I want is a better fit for predictive purposes ie for a
novel choice set the probability that each alternative has for being the one
chosen as "the best".
If adding the gender provides a better prediction I am happy - if it doesn't
I leave it out. I just want the ability to test its impact in teh MNL model.
Cheers
> If you have "unlabeled alternatives" (also called generic) you can create
> your
> own generic dummy variable that you can use in place of the ONE option
> given in
> the NLOGIT setup. Simply code a new attribute (call it genASC) with the
> value
> of 1.0 for every alternative EXCEPT one. For that single alternative give
> the
> same new attribute a coded value of 0. Choose this single alternative
> carefully. It becomes your base alternative. Now create a 2nd new
> variable
> that is the interaction of gender and genASC (gendXCon = gender * genASC).
> Your
> utility function would now be genASC + gendXCon + pRF1. The code might
> look
> like:
>
> NLOGIT ; Lhs = CHOICE, SETSIZE
> ; Rhs = genASC, gendXCon, pRF1
> ; Prob = probs $
>
> This interacts gender with every alternative except the base. This
> essentially
> tests whether the overall constant's utility is different across genders.
> However, if you do this why would you not expect differences in the
> parameter
> for pRF1 across gender?
>
> Tom
>
> -----Original Message-----
> From: limdep-bounces at limdep.itls.usyd.edu.au
> [mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of contactemt
> Sent: Friday, October 13, 2006 6:45 AM
> To: Limdep and Nlogit Mailing List
> Subject: Re: [Limdep Nlogit List] MNL Dummy Variable issues
>
> Hi,
>
> I have understood what ASC are now and realise they are not applicable to
> my
> model :)
>
> My model deals with generic choices - Greene uses the term "unlabeled" -
> with the further complication that the choice set size can vary. I have
> looked at his book and he describes the problem with unlabeled and ASC's
> in
> Appendix 10A.
>
> Coincidentally, in his example he uses gender as a non varying parameter
> (within sets) as I did.
> However, to get around the lack of ASC's he uses a pre defined utility
> model
> and combines it with one of the utility variables. Why he chooses a
> particular variable I don't know - and what is TTgen anyway (if you have
> the
> book).
>
> My variables are purely measured items - I do not wish to apply utility
> constraints to them. I want the data to describe the model. Any utility
> constructs I place on the model would be arbitrary. So is there another
> approach I can use?
>
> But if not, say I do try to use interactions to include categories - or
> SDC's as he calls them:
>
> How should I choose the variable(s) to interact with?
> Should I choose 1 or many?
> Should I only include the variable interaction term or should I include it
> by itself also - what would be the point as they would be collinear
> wouldn't
> they?
> How should I encode gender?
> If I choose 1,0 then half the interactions would be 0.
> If I choose 1,-1 will NLOGIT be able to fit the =ve and -ve values of the
> interacting term(s) OK?
>
> If I have more categories/dummy variables to add to the model, do I need a
> set of interactions for each one or can I combine them?
>
> I have searched and not been able to find any examples of the type of
> unlabeled model I wish to run, and I'm afraid I don't have the ability to
> extrapolate from the exclusively "labeled" models out there.
>
> Any further help appreciated.
>
>
>
>> You get this because the default in NLOGIT is to fit alternative specific
>> constants (when you use the ONE term) and you have 30 alternatives in the
>> choice
>> set. Add to that the interaction between gender you requested in RHS2
>> and
>> your
>> one generic attribute (pRF1) you get 59 parameters. Several of these
>> parameters
>> are fixed which means you have very low frequencies for them or one level
>> of
>> gender never choice that specific alternative.
>>
>> Perhaps you should read about choice modeling in Greene's book Applied
>> Choice
>> Analysis. He discusses all these issues and the defaults of NLOGIT.
>>
>> Tom
>>
>> -----Original Message-----
>> From: limdep-bounces at limdep.itls.usyd.edu.au
>> [mailto:limdep-bounces at limdep.itls.usyd.edu.au] On Behalf Of contactemt
>> Sent: Thursday, October 12, 2006 1:29 PM
>> To: Limdep and Nlogit Mailing List
>> Subject: Re: [Limdep Nlogit List] MNL Dummy Variable issues
>>
>> Thanks,
>>
>> After a quick look it seems the Limdep rh2 variable is used for this.
>> So I have:
>>
>> NLOGIT ; Lhs = CHOICE, SETSIZE
>> ; Rhs = v
>> ; Rh2 = One, GENDER
>> ; Prob = probs $
>>
>>
>> Incidentally, (I havent read through the issues but)
>> I run a very simple (one attribute)
>>
>> NLOGIT ; Lhs = CHOICE, SETSIZE
>> ; Rhs = pRF1
>> ; Rh2 = One, GENDER
>> ; Prob = probs $
>>
>>
>> as a test and get 59 parameters. Why is this?
>>
>> Sorry if a stupid Q.
>> +
>> | Discrete choice (multinomial logit) model |
>> | Maximum Likelihood Estimates |
>> | Model estimated: Oct 12, 2006 at 06:14:54PM.|
>> | Dependent variable Choice |
>> | Weighting variable None |
>> | Number of observations 1704 |
>> | Iterations completed 18 |
>> | Log likelihood function -.3005320E-08 |
>> | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj |
>> | No coefficients -5795.6403 1.00000 1.00000 |
>> | Constants only. Must be computed directly. |
>> | Use NLOGIT ;...; RHS=ONE $ |
>> | Response data are given as ind. choice. |
>> | Number of obs.= 1704, skipped 0 bad obs. |
>> +---------------------------------------------+
>>
>>
>> |+---------+--------------+----------------+--------+---------+
>> |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] |
>> +---------+--------------+----------------+--------+---------+
>> PRF1 .28562667 1100.58000 .000 .9998
>> A_Alt.1 121.889077 ......(Fixed Parameter).......
>> AltxHCA1 -86.8291397 47685.8780 -.002 .9985
>> A_Alt.2 -17.6981970 50283.9113 .000 .9997
>> AltxHCA2 11.4289719 38053.7076 .000 .9998
>> A_Alt.3 -15.6341676 28185.0248 -.001 .9996
>> AltxHCA3 9.45993004 23720.4712 .000 .9997
>> A_Alt.4 -14.0208822 18985.8265 -.001 .9994
>> AltxHCA4 7.93083927 14502.9551 .001 .9996
>> A_Alt.5 -13.7822969 ......(Fixed Parameter).......
>> AltxHCA5 7.88816799 ......(Fixed Parameter).......
>> A_Alt.6 -12.9643251 ......(Fixed Parameter).......
>> AltxHCA6 7.03157581 431.216215 .016 .9870
>> A_Alt.7 -11.5834031 2324.85755 -.005 .9960
>> AltxHCA7 5.80855146 1886.76721 .003 .9975
>> A_Alt.8 -9.82066678 ......(Fixed Parameter).......
>> AltxHCA8 4.04330766 271.466257 .015 .9881
>> A_Alt.9 -8.15221444 48.3176779 -.169 .8660
>> AltxHCA9 2.65183332 31.0102930 .086 .9319
>> A_Alt.10 -6.50502468 24.7267852 -.263 .7925
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.11 -5.06016269 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.12 -4.04467058 .00742264 -544.910 .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.13 -3.22120224 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.14 -2.70256143 .202507D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.15 -2.37109609 .211547D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.16 -2.16550807 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.17 -2.01944461 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.18 -1.97037254 .217681D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.19 -1.98283571 .242113D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.20 -2.04980440 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.21 -2.13493369 .113446D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.22 -2.14340813 .125961D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.23 -2.14440660 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.24 -2.18364464 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.25 -2.23302643 .174142D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.26 -2.26795137 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.27 -2.29960229 ......(Fixed Parameter).......
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.28 -2.29869412 .153029D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>> A_Alt.29 -2.30517803 .174283D-04 ******** .0000
>> AltxHCA* 7.33600806 .02888315 253.989 .0000
>>
>>
>>
>>>
>> That is exactly right: anything constant across the choice set
>> needs to be put in as an interaction effect, via multiplying
>> with with non-constant quantities. That way, you are in effect
>> estimating two coefficients -- assuming you are assessing the effect
>> of a dummy variable -- for each of those other (non-contant)
>> quantities. To keep with your original example, you'd be getting a
>> set of "male coefficients" and "female coefficients" for each of the
>> non-constant variables with which you're interacting. Note that this
>> would only be estimated *across* choice sets, since each individual
>> is, presumably, constant in gender, so the gender "variable" never
>> varies within any one choice set. [You should be careful that you
>> don't have a small proportion of either zeros or ones in your dummy
>> variable, or you may wind up not having enough cases to estimate the
>> gender difference in coefficients. You might also consider some form
>> of hierarchical modeling, particularly hierarchical Bayes.]
>>
>> FF
>>
>> Quoting "Thomas C. Eagle" <teagle at tceagle.com>:
>>
>>> You have to interact the category variables with alternative
>> specific
>>> constants,
>>> much like you do with socio-demographic effects.
>>>
>>> Tom
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>>
>>
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