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.


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
>>>>
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>>>> PLEASE do read the posting guide 
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>>
>>

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