pls_eigen: Eigenvector algorithm for PLS

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Eigenvector algorithm for PLS

Description

Computes the PLS solution by eigenvector decompositions.

Usage

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 <[e-mail]>

References

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

See Also

mvr

Examples

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

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.