Note that p1 and p2 depend only on d1 - d2. Have a look at a book like
Ripley's "Pattern Recognition and Neural Networks" or
Anderson's
multivariate statistics book for further information.
Reid Huntsinger
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Steve Adams
Sent: Wednesday, June 15, 2005 4:54 PM
To: r-help at stat.math.ethz.ch
Subject: [R] 2 LDA
Hi,
I am using Partek for LDA analysis. For a binary
response variable, it generates 2 discriminant
functions, one for each of the 2 levels of the
response variable. And I can simply calculate 2
discriminant scores (say d1 and d2) for each sampples
using the 2 discriminant functions, then I can use the
following formula to compute the posterior probability
for the sample:
p1=exp(d1-d2)/(1+exp(d1-d2))
p2=1/(1+exp(d1-d2))
In R, the lda function only generates 1 discriminant
function, and exactly the same posterior probability
as the LDA function in Partek does.
My question is what's the difference (or relationship)
between the above 2 LDA functions, where I can find
the reference for the partek LDA function?
Thanks
Steve
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