penalized.pls.kernel {ppls} | R Documentation |
Internal function computing the penalized PLS solutions based on a kernel matrix.
penalized.pls.kernel(X, y, M, ncomp)
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. |
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).
coefficients |
Penalized PLS coefficients for all 1,2,...,ncomp compoents |
This is an internal function that is called by penalized.pls
.
Nicole Kraemer
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
penalized.pls
,penalized.pls.default
# this is an internal function