Internal function that computes the penalized PLS solutions.

1 | ```
penalized.pls.default(X, y, M, ncomp)
``` |

`X` |
matrix of centered and (possibly) scaled input data |

`y` |
vector of centered and (possibly) scaled response data |

`M` |
matrix that is a transformation of the penalty term P. Default is |

`ncomp` |
number of PLS components |

This function assumes that the columns of `X`

and `y`

are centered and - optionally - scaled. The matrix `M`

is defined
as the inverse of *(I + P)*. The
computation of the regression coefficients is based on an extension of
the classical NIPALS algorithm for PLS. If the number of observations
is small with respect to the number of variables, it is
computationally more efficient to use the function
`penalized.pls.kernel`

. For more details, see Kraemer,
Boulesteix, and Tutz (2008).

`coefficients` |
Penalized PLS coefficients for all 1,2,...,ncomp components |

This is an internal function that is called by `link{penalized.pls}`

.

Nicole Kraemer

N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). *Penalized Partial Least Squares with Applications
to B-Spline Transformations and Functional Data*. Chemometrics and Intelligent Laboratory Systems, 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009

`penalized.pls`

, `penalized.pls.kernel`

1 | ```
# this is an internal function
``` |

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