kernelpls.fit {pls} | R Documentation |
Fits a PLSR model with the kernel algorithm.
kernelpls.fit(X, Y, ncomp, stripped = FALSE, ...)
X |
a matrix of observations. NA s and Inf s are not
allowed. |
Y |
a vector or matrix of responses. NA s and Inf s
are not allowed. |
ncomp |
the number of components to be used in the modelling. |
stripped |
logical. If TRUE the calculations are stripped
as much as possible for speed; this is meant for use with
cross-validation or simulations when only the coefficients are
needed. Defaults to FALSE . |
... |
other arguments. Currently ignored. |
This function should not be called directly, but through
the generic functions plsr
or mvr
with the argument
method="kernelpls"
(default). Kernel PLS is particularly efficient
when the number of objects is (much) larger than the number of
variables. The results are equal to the NIPALS algorithm. Several
different forms of kernel PLS have been described in literature, e.g.
by De Jong and Ter Braak, and two algorithms by Dayal and
MacGregor. This function implements the
fastest of the latter, not calculating the crossproduct matrix of
X. In the Dyal & MacGregor paper, this is “algorithm 1”.
A list containing the following components is returned:
coefficients |
an array of regression coefficients for 1, ...,
ncomp components. The dimensions of coefficients are
c(nvar, npred, ncomp) with nvar the number
of X variables and npred the number of variables to be
predicted in Y . |
scores |
a matrix of scores. |
loadings |
a matrix of loadings. |
loading.weights |
a matrix of loading weights. |
Yscores |
a matrix of Y-scores. |
Yloadings |
a matrix of Y-loadings. |
projection |
the projection matrix used to convert X to scores. |
Xmeans |
a vector of means of the X variables. |
Ymeans |
a vector of means of the Y variables. |
fitted.values |
an array of fitted values. The dimensions of
fitted.values are c(nobj, npred, ncomp) with
nobj the number samples and npred the number of
Y variables. |
residuals |
an array of regression residuals. It has the same
dimensions as fitted.values . |
Xvar |
a vector with the amount of X-variance explained by each number of components. |
Xtotvar |
Total variance in X . |
stripped
is TRUE
, only the components
coefficients
, Xmeans
and Ymeans
are returned.
Ron Wehrens and Bjørn-Helge Mevik
de Jong, S. and ter Braak, C. J. F. (1994) Comments on the PLS kernel algorithm. Journal of Chemometrics, 8, 169–174.
Dayal, B. S. and MacGregor, J. F. (1997) Improved PLS algorithms. Journal of Chemometrics, 11, 73–85.
mvr
plsr
pcr
widekernelpls.fit
simpls.fit
oscorespls.fit