[R] Probit function

[Achim Zeileis]

On Mon, 31 Aug 2009, Noah Silverman wrote:

> Um..... I did my research.  Have been for years.  I assume you're referring 
> to Boltman and Chapmanm "A multinomial logit model for handicapping horse 
> races" included in "Efficiency of racetrack betting markets".  Page 155 
> references what they call a "multinomial model".  From equation 14 in their 
> paper, it appears as if they are calculating "Utility" of a horse as a 
> number.  Far from what I understand a traditional "Multinomial" model is.
>
> The seminar that I referenced discussed using a probit model instead of a 
> logit model.  Since the Boltman and Chapman application didn't really have 
> multiple discreet choices, I'm not sure how the probit model would.  Hence my 
> inquiry.

But of course it has multiple choices (finishing ranks instead of binary 
win/lose), otherwise it wouldn't be very multinomial, would it?
Z

>
>
> On 8/31/09 6:23 PM, Achim Zeileis wrote:
>> On Mon, 31 Aug 2009, Noah Silverman wrote:
>> 
>>> I get that.
>>> 
>>> Still trying to figure out what the "multi" nominal labels they used were. 
>>> That's why I passed on the reference to the seminar summary.
>> 
>> So that I could do the research for you? Come on...the usual strategy 
>> applies: Look at the references! (Hint: The information is in the Bolton 
>> and Chapman paper.)
>> Z
>> 
>>> 
>>> On 8/31/09 5:40 PM, Achim Zeileis wrote:
>>>> On Mon, 31 Aug 2009, Noah Silverman wrote:
>>>> 
>>>>> Thanks Achim,
>>>>> 
>>>>> I discovered the Journal article just after posting this question.  It 
>>>>> did help explain more.
>>>>> 
>>>>> My original inspiration for looking at this package came from a seminar 
>>>>> "summary" given in 2002.  Unfortunately , I can not find any actual 
>>>>> published paper or lecture notes that explain the lecturer's application 
>>>>> of the MNP.
>>>>> 
>>>>> Here is a link to the PDF of the summary: 
>>>>> http://www-stat.stanford.edu/seminars/stat/abstracts2001-2002/gu.pdf
>>>>> 
>>>>> Most of the other published research on using logit or probit models for 
>>>>> horseracing data use a binary label of win/lose.  So, my thought was 
>>>>> that they were using the same for this application.
>>>>> 
>>>>> Any thoughts?
>>>> 
>>>> As I said in my last mail: *Multi*nomial probit typically conveys more 
>>>> than 2 choices while *bi*nomial probit conveys exactly 2 choices.
>>>> Z
>>>> 
>>>>> -- 
>>>>> Noah
>>>>> 
>>>>> 
>>>>> On 8/31/09 5:07 PM, Achim Zeileis wrote:
>>>>>> On Mon, 31 Aug 2009, Noah Silverman wrote:
>>>>>> 
>>>>>>> Hello,
>>>>>>> 
>>>>>>> I want to start testing using the MNP probit function in stead of the 
>>>>>>> lrm function in my current experiment.
>>>>>>> 
>>>>>>> I have one dependant label and two independent varaibles.
>>>>>>> 
>>>>>>> The lrm is simple
>>>>>>> 
>>>>>>> model <- lrm(label ~ val1 + val2)
>>>>>>> 
>>>>>>> I tried the same thing with the mnp function and got an error that I 
>>>>>>> don't understand
>>>>>>> 
>>>>>>> model <- mnp(label ~ val1 + val2)
>>>>>>> 
>>>>>>> I get back an immediate error that tells me, "The number of 
>>>>>>> alternatives should be at least 3"
>>>>>>> 
>>>>>>> Since I have a binary training label, this looks like a problem. 
>>>>>>> (Additionally, I thought that a probit was a appropriate tool for 
>>>>>>> building binary models.)
>>>>>>> 
>>>>>>> Any advice?
>>>>>> 
>>>>>> *Multi*nomial probit typically conveys more than 2 choices while 
>>>>>> *bi*nomial probit conveys exactly 2 choices. One could argue that the 
>>>>>> latter should be a special case of the former but the more general case 
>>>>>> has much more computational challenges.
>>>>>> 
>>>>>> The =2 vs >2 information might have been inferred from the title of the 
>>>>>> package already but if you wanted to take extreme actions you could 
>>>>>> read the mnp() manual page or oven the references it points you to: The 
>>>>>> software is discussed in the Journal of Statistical Software 
>>>>>> (http://www.jstatsoft.org/v14/i03/) and the theory is described in an 
>>>>>> article in the Journal of Econometrics (124, 311-334).
>>>>>> 
>>>>>> Z
>>>>>> 
>>>>>>> Thanks!
>>>>>>> 
>>>>>>> -N
>>>>>>> 
>>>>>>> ______________________________________________
>>>>>>> R-help at r-project.org mailing list
>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>>>> PLEASE do read the posting guide 
>>>>>>> http://www.R-project.org/posting-guide.html
>>>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>>> 
>>>>>>> 
>>>>> 
>>> 
>