penalized.pls.kernel {ppls}R Documentation

Kernel Penalized PLS

Description

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

Usage

penalized.pls.kernel(X, y, M, ncomp)

Arguments

X A matrix of centered and (possibly) scaled input data.
y A vector of centered and (possibly) scaled response data.
M A matrix that is a transformation of the penalty term P.
ncomp The number of PLS components.

Details

This function assumes that the columns of X and y are centered and . 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 (2007).

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. Boulesteix, G. Tutz (2007) "Penalized Partial Least Squares with Applications to B-Splines Transformations and Functional Data", preprint

available at http://ml.cs.tu-berlin.de/~nkraemer/publications.html

See Also

penalized.pls,penalized.pls.default

Examples

# this is an internal function

[Package ppls version 1.0 Index]