Eigenvector algorithm for PLS

Description

Computes the PLS solution by eigenvector decompositions.

Usage

1
pls_eigen(X, Y, a)

Arguments

X

X input data, centered (and scaled)

Y

Y input data, centered (and scaled)

a

number of PLS components

Details

The X loadings (P) and scores (T) are found by the eigendecomposition of X'YY'X. The Y loadings (Q) and scores (U) come from the eigendecomposition of Y'XX'Y. The resulting P and Q are orthogonal. The first score vectors are the same as for standard PLS, subsequent score vectors different.

Value

P

matrix with loadings for X

T

matrix with scores for X

Q

matrix with loadings for Y

U

matrix with scores for Y

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

mvr

Examples

1
2
data(cereal)
res <- pls_eigen(cereal$X,cereal$Y,a=5)

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