Description Usage Arguments Details Value Author(s) References See Also Examples

NIPALS algorithm for PLS2 regression (y is multivariate)

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

`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 |

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

`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 |

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.

1 2 | ```
data(cereal)
res <- pls2_nipals(cereal$X,cereal$Y,a=5)
``` |

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