LDA/QDA classification from PLS model
Description
For each number of components LDA/QDA models are created from the scores of the supplied PLS model and classifications are performed.
Usage
1  lda_from_pls(model, grouping, newdata, ncomp)

Arguments
model 

grouping 
vector of grouping labels 
newdata 
predictors in the same format as in the 
ncomp 
maximum number of PLS components 
Value
matrix of classifications
See Also
VIP
(SR/sMC/LW/RC), filterPLSR
, shaving
,
stpls
, truncation
,
bve_pls
, ga_pls
, ipw_pls
, mcuve_pls
,
rep_pls
, spa_pls
,
lda_from_pls
, lda_from_pls_cv
, setDA
.
Examples
1 2 3 4 5 6 7  data(mayonnaise, package = "pls")
mayonnaise < within(mayonnaise, {dummy < model.matrix(~y1,data.frame(y=factor(oil.type)))})
pls < plsr(dummy ~ NIR, ncomp = 10, data = mayonnaise, subset = train)
with(mayonnaise, {
classes < lda_from_pls(pls, oil.type[train], NIR[!train,], 10)
colSums(oil.type[!train] == classes) # Number of correctly classified out of 42
})
