lda_from_pls_cv: Cross-validated LDA/QDA classification from PLS model

In plsVarSel: Variable Selection in Partial Least Squares

Cross-validated 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. This use of cross-validation has limitations. Handle with care!

Usage

lda_from_pls_cv(model, X, y, ncomp, Y.add = NULL)

Arguments

model	pls model fitted with the pls package
X	predictors in the same format as in the pls model
y	vector of grouping labels
ncomp	maximum number of PLS components
Y.add	additional responses

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

data(mayonnaise, package = "pls")
mayonnaise <- within(mayonnaise, {dummy <- model.matrix(~y-1,data.frame(y=factor(oil.type)))})
pls <- plsr(dummy ~ NIR, ncomp = 8, data = mayonnaise, subset = train, 
            validation = "CV", segments = 40, segment.type = "consecutive")
with(mayonnaise, {
 classes <- lda_from_pls_cv(pls, NIR[train,], oil.type[train], 8)
 colSums(oil.type[train] == classes) # Number of correctly classified out of 120
})

plsVarSel documentation built on Jan. 12, 2023, 5:09 p.m.