[R] LDA with previous PCA for dimensionality reduction

[Christoph Lehmann]

Dear all, not really a R question but:

If I want to check for the classification accuracy of a LDA with 
previous PCA for dimensionality reduction by means of the LOOCV method:

Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA 
with the CV option set to TRUE (runs LOOCV)

-- OR--

do I need
- to compute for each 'test-bag' (the n-1 observations) a PCA 
(my.princomp.1),
- then run the LDA on the test-bag scores (-> my.lda.1)
- then compute the scores of the left-out-observation using 
my.princomp.1 (-> my.scores.2)
- and only then use predict.lda(my.lda.1, my.scores.2) on the scores of 
the left-out-observation

?
I read some articles, where they choose procedure 1, but I am not sure, 
if this is really correct?

many thanks for a hint

Christoph