pls2_nipals {chemometrics} | R Documentation

## PLS2 by NIPALS

### Description

NIPALS algorithm for PLS2 regression (y is multivariate)

### Usage

    pls2_nipals(X, Y, a, it = 50, tol = 1e-08, scale = FALSE)

### Arguments

`X` | original X data matrix
`Y` | original Y-data matrix
`a` | number of PLS components
`it` | number of iterations
`tol` | tolerance for convergence
`scale` | if TRUE the X and y data will be scaled in addition to centering, if FALSE only mean centering is performed

### Details

The NIPALS algorithm is the originally proposed algorithm for PLS. Here, the Y-data matrix is multivariate.

### 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
`D` | D-matrix within the algorithm
`W` | weights for X
`C` | weights for Y
`B` | final regression coefficients

### 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`, `pls1_nipals`

### Examples

    data(cereal)
    res <- pls2_nipals(cereal$X,cereal$Y,a=5)

[Package chemometrics version 0.6 Index]