Penalized PLS based on NIPALS Algorithm

Share:

Description

Internal function that computes the penalized PLS solutions.

Usage

1
penalized.pls.default(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 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).

Value

coefficients

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

Note

This is an internal function that is called by link{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.kernel

Examples

1
# this is an internal function