View source: R/kernelpls.fit.R
kernelpls.fit  R Documentation 
Fits a PLSR model with the kernel algorithm.
kernelpls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE, ...)
X 
a matrix of observations. 
Y 
a vector or matrix of responses. 
ncomp 
the number of components to be used in the modelling. 
center 
logical, determines if the X and Y matrices are mean centered or not. Default is to perform mean centering. 
stripped 
logical. If 
... 
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, ...,

scores 
a matrix of scores. 
loadings 
a matrix of loadings. 
loading.weights 
a matrix of loading weights. 
Yscores 
a matrix of Yscores. 
Yloadings 
a matrix of Yloadings. 
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 
residuals 
an array of
regression residuals. It has the same dimensions as 
Xvar 
a vector with the amount of Xvariance explained by each component. 
Xtotvar 
Total variance in 
If stripped
is TRUE
, only the components coefficients
,
Xmeans
and Ymeans
are returned.
Ron Wehrens and BjørnHelge 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
cppls
pcr
widekernelpls.fit
simpls.fit
oscorespls.fit
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