LDA models using PLS latent variables as input space.
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X 
input variate matrix 
Y 
matrix with class membership (classes correspond
to columns). If missing,

grouping 
factor with class membership. If missing,

comps 
which latent variables should be used? 
ncomp 
how many latent variables should be calculated? 
stripped 
should the model be stripped to save
memory? (Stripping is different from

... 
further parameters for
further arguments for 
object 
the plslda model 
newdata 
matrix with new cases to be predicted. 
For the moment only kernelpls.fit
(see
kernelpls.fit
for the original) is
supported.
object of class "plslda", consisting of the mvr object
returned by plsr
and the lda object
returned by lda
.
list with results from link[MASS]{predict.lda}
plus the pls scores used for LDA prediction in element
$scores
Claudia Beleites
plsr
, lda
predict.lda
,
predict.mvr
rotate
for rotation of the LDA
part of the model
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