Follow-up: [R] Fisher LDA and prior=c(...) argument

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