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

[Edgar Acuna]

Hello,

If you don not use the option prior in lda then the priors  are estimated
proportionally to the size of each class. If you do not use the option
prior in predict.lda then they are taken from the lda object. Otherwise
you can use any numbers in the vector of priors the only condition is that
they add up one.

The CV option of lda refers to the leave-out-out method. Several people
use n-fold CV. I wrote an R function to compute 10-fold CV for lda. Look
at :
math.uprm.edu/~edgar/CV10LDA.TXT

Regards,

Edgar Acuna

On Sun, 18 May 2003, Edoardo M Airoldi wrote:

> 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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