[R] Reduced Error Logistic Regression, and R?

[Simon Blomberg]

>From what I've read (which isn't much), the idea is to estimate a
utility (preference) function for discrete categories, using logistic
regression, under the assumption that the residuals of the linear
predictor of the utilities are ~ Type I Gumbel. This implies the
"independence of irrelevant alternatives" in economic jargon. ie the
utility of choice a versus choice b is independent of the introduction
of a third choice c. It also implies homoscedasticity of the errors. The
model can be generalized in various ways. If you are willing to
introduce extra parameters into the model, such as the parameters of the
Gumbel distribution, you may get more precision in the estimates of the
utility function. An alternative (without the independence of irrelevant
alternatives assumption) is to model the errors as multivariate normal
(ie use probit regression), which is computationally much more
difficult.

Whether it makes substantive sense to use these models outside of
"discrete choice" experiments is another question.

 Patenting these methods is worrying. There have been a lot of people
working on discrete choice experiments over the years. It's hard to
believe that a single company could have ownership over an idea that is
the result of a collaborative effort such as this.

Cheers,

Simon.

 On Thu, 2007-04-26 at 12:29 +1000, Tim Churches wrote:
> This news item in a data mining newsletter makes various claims for a technique called "Reduced Error Logistic Regression": http://www.kdnuggets.com/news/2007/n08/12i.html
> 
> In brief, are these (ambitious) claims justified and if so, has this technique been implemented in R (or does anyone have any plans to do so)? 
> 
> Tim C
> 
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-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat. 
Lecturer and Consultant Statistician 
Faculty of Biological and Chemical Sciences 
The University of Queensland 
St. Lucia Queensland 4072 
Australia

Room 320, Goddard Building (8)
T: +61 7 3365 2506 
email: S.Blomberg1_at_uq.edu.au 

The combination of some data and an aching desire for 
an answer does not ensure that a reasonable answer can 
be extracted from a given body of data. - John Tukey.