kernelpls.fit(X, Y, ncomp, stripped = FALSE, ...)
NA
s and Inf
s are not
allowed.NA
s and Inf
s
are not allowed.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
.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
.fitted.values
are c(nobj, npred, ncomp)
with
nobj
the number samples and npred
the number of
Y variables.fitted.values
.X
.stripped
is TRUE
, only the components
coefficients
, Xmeans
and Ymeans
are returned.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 Dayal, B. S. and MacGregor, J. F. (1997) Improved PLS algorithms. Journal of Chemometrics, 11, 73--85.
mvr
plsr
pcr
simpls.fit
oscorespls.fit