1 Sep 2009 02:40

## Re: Probit function

```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.)
>>>
>>
>> *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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