Penalized PLS based on NIPALS Algorithm and blockswise variable selection
Internal function that computes the penalized PLS solutions with included block-wise variable selection.
penalized.pls.select(X, y, M, ncomp,blocks)
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
vector of length
This function assumes that the columns of
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. Moreover, in each iteration,
the weight vector is only defined by one block of variables. For more details, see Kraemer,
Boulesteix, and Tutz (2008).
Penalized PLS coefficients for all 1,2,...,ncomp components
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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