[R] lda()

[Marc R. Feldesman]

At 08:45 AM 7/12/2004, marzban wrote:
 >
 >Hello,
 >
 >For a simple problem with 1 predictor (x) and 2 classes (0 and 1), the
 >linear discriminant function should be something like
 >
 > 2(mu_0 - mu_1)/var  x    +    x-independent-terms
 >
 >where var is the common variance.
 >
 >Question 1: Why does lda() report only a single "Coefficients of linear
 >discriminants" when there are in fact two coefficients (the x-dependent
 >and the x-independent terms)?
 >
 >Question 2: And how is that single coefficient computed? It is certainly
 >not equal to 2(mu_0 -mu_1)/var .
 >
 >Regards,
 >Caren
 >--
 >http://www.nhn.ou.edu/~marzban


Perhaps some reading would be helpful.  I suggest you look first at the 
help file for lda().  Second, I suggest you read Venables and Ripley, MASS, 
4th Edition, where lda() is discussed extensively.  Third, I suggest you 
read Ripley's Pattern Recognition and Neural Networks, where the theory is 
laid out clearly.  Both of these latter books are referenced in lda's help 
file.

Finally, you might want to tell us what version of lda() you're using, what 
version of R you're using, and what platform you're running on.   For all 
we know, you're using a 2-year old version of R and lda, both long 
superceded by vastly improved programs and packages.