pls_eigen {chemometrics} | R Documentation

## 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 <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

    data(cereal)
    res <- pls_eigen(cereal$X,cereal$Y,a=6)

[Package chemometrics version 0.6 Index]