# Kernel Penalized PLS

### Description

Internal function that computes the penalized PLS solutions based on a kernel matrix.

### Usage

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

### Arguments

`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 |

### Details

This function assumes that the columns of `X`

and `y`

are centered. The matrix `M`

is defined as the inverse of
*(I + P)*.
The
computation of the regression coefficients is based on a Kernel
representation of penalized PLS. If the number of observations
is large with respect to the number of variables, it is
computationally more efficient to use the function
`penalized.pls.default`

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

### Value

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

### Note

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

.

### Author(s)

Nicole Kraemer

### References

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

### See Also

`penalized.pls`

,`penalized.pls.default`

### Examples

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