[R] Fisher LDA and prior=c(...) argument

[Edoardo M Airoldi]

hello,
 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.
 My guess is that the CODE above estimates the likelihood of of the Fisher 
scores for (example | class) and then implements the Bayes rule to return 
the maximum a-posteriori class.

 Is that correct?  Any pointer towards that direction is appreciated.  
Please cc to edo at stat.cmu.edu your reply.
Thanks

Edoardo M. Airoldi
http://www.stat.cmu.edu/~eairoldi
BH 232L  (412) 268.7829
PC Lab   (412) 268.8719