penalized.pls.kernel: Kernel Penalized PLS

In ppls: Penalized Partial Least Squares

Description

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

Usage

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 M=NULL, which corresponds to no penalization.
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

# this is an internal function

ppls documentation built on May 1, 2019, 10:53 p.m.