[R] logistic regression or discriminant analysis ?

[Marc R. Feldesman]

At 01:49 AM 5/24/2002, Daniel Amorèse took a glob of electronic fairy dust, 
mushed it together in odd and bizarre ways, and ruminated:
 >Le 2002.05.23 19:38, Marc Feldesman a écrit :
 >> At 06:25 PM 5/23/2002 +0200, Daniel Amorèse wrote:
 >> >Hello,
 >What I have done: the correlation matrix tells me that many
 >variables are correlated. Thus, I performed a lda using only 5
 >variables (this selection is arbitrary performed among uncorrelated
 >variables). The graphical output shows points clouds that are not
 >circular: this result may suggest difference in covariance
 >matrices, hence lda seems not to be the more suitable method for
 >separating groups.

Elliptical point clouds do not, in themselves, suggest unequal covariance 
structures.  If the major axes of the ellipses are all oriented in the same 
direction and are more-or-less parallel, you don't have much evidence of 
unequal covariance structure.  Unless the covariance structures are really 
different, I've never seen much improvement using quadratic discriminant 
analysis.  The problem with quadratic discriminant analysis, in my 
experience, is that it is very difficult to interpret the results in the 
context of the variables producing the group separations.

I'd strongly recommend Professor Ripley's "Pattern Recognition and Neural 
Networks" (Cambridge University Press, 1996), as well as Hastie, 
Tibshirani, and Friedman's "The Elements of Statistical Learning" (2001, 
Cambridge University Press).  Both will help you considerably with your 
problem.

I also agree with Jon Baron that clustering techniques may be appropriate 
here.

Dr. Marc R. Feldesman
Professor and Chairman
Anthropology Department - Portland State University
email:  feldesmanm at pdx.edu
email:  feldesman at attglobal.net
fax:    503-725-3905


"Sometimes the lights are all shining on me, other times I can barely see,
lately it's occurred to me, what a long strange trip it's been..."  Jerry &
the boys



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