Eigenvector algorithm for PLS
Computes the PLS solution by eigenvector decompositions.
pls_eigen(X, Y, a)
X |
X input data, centered (and scaled) |
Y |
Y input data, centered (and scaled) |
a |
number of PLS components |
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.
P |
matrix with loadings for X |
T |
matrix with scores for X |
Q |
matrix with loadings for Y |
U |
matrix with scores for Y |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
data(cereal) res <- pls_eigen(cereal$X,cereal$Y,a=5)
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