penalized.pls.kernel {ppls} | R Documentation |
Internal function that computes the penalized PLS solutions based on a kernel matrix.
penalized.pls.kernel(X, y, M, ncomp)
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 |
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 Kr"amer, Boulesteix, and Tutz (2008).
coefficients |
Penalized PLS coefficients for all 1,2,...,ncomp compoents |
This is an internal function that is called by penalized.pls
.
Nicole Kr"amer
N. Kr"amer, 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.
penalized.pls
,penalized.pls.default
# this is an internal function