Kernel Penalized PLS
Internal function that computes the penalized PLS solutions based on a kernel matrix.
penalized.pls.kernel(X, y, M, ncomp)
matrix of centered and (possibly) scaled input data
vector of centered and (possibly) scaled response data
matrix that is a transformation of the penalty term P. Default is
number of PLS components
This function assumes that the columns of
are centered. The matrix
M is defined as the inverse of
(I + P).
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).
Penalized PLS coefficients for all 1,2,...,ncomp compoents
This is an internal function that is called by
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
# this is an internal function
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