No subject
Sat Jul 11 12:35:05 EST 2009
naive dummy variables without making choices about the alternatives.
Anyway, I hope I have explained more of what I'm trying to achieve.
Cheers, Ed
>
> -----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 8:53 AM
> To: Limdep and Nlogit Mailing List
> Subject: Re: [Limdep Nlogit List] MNL Dummy Variable issues
>
> 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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