Follow-up: [R] Fisher LDA and prior=c(...) argument
Edoardo M Airoldi
eairoldi at stat.cmu.edu
Mon May 19 00:59:27 CEST 2003
hello,
a clarification.
I am using LDA and QDA function of MASS library. I understand Fisher
LDA is a method non-probabilistic in nature, so I wonder what happens when
I try to predict my test set examples as in:
> fit <- lda(labels~., data=train.table, prior=c(.5,.5))
> pred <- predict(fit, data=test.table, prior=c(.5,.5))
Specifically I ask this because in my problem there are 700 examples
class A, and 50 in class B, and I'd be glad to use a way to weight the
contribution of the examples in different classes (in the prediction
stage for LDA I guess)
My guess is that the CODE above estimates the likelihood of 'the
projection of the data onto the canonical variate' (only one with 2
classes) as in: P(example | class=.) and then implements the Bayes
rule to return the maximum a-posteriori class, using the estimated
likelihood and the given prior=c(...)
Is that correct? Any pointer towards the understanding is appreciated.
Further any pointer towards an example that uses the argument CV=TRUE is
also appreciated, since i was not able (apparently) to get any change by
setting it to TRUE =:-)
Edoardo M. Airoldi
http://www.stat.cmu.edu/~eairoldi
BH 232L (412) 268.7829
PC Lab (412) 268.8719
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