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? -- 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@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. >> >> [[alternative HTML version deleted]] ______________________________________________ R-help@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.
