View source: R/widekernelpls.fit.R
widekernelpls.fit  R Documentation 
Fits a PLSR model with the wide kernel algorithm.
widekernelpls.fit( X, Y, ncomp, center = TRUE, stripped = FALSE, tol = .Machine$double.eps^0.5, maxit = 100, ... )
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 
tol 
numeric. The tolerance used for determining convergence in the algorithm. 
maxit 
positive integer. The maximal number of iterations used in the internal Eigenvector calculation. 
... 
other arguments. Currently ignored. 
This function should not be called directly, but through the generic
functions plsr
or mvr
with the argument
method="widekernelpls"
. The wide kernel PLS algorithm is efficient
when the number of variables is (much) larger than the number of
observations. For very wide X
, for instance 12x18000, it can be
twice as fast as kernelpls.fit
and simpls.fit
.
For other matrices, however, it can be much slower. The results are equal
to the results of the NIPALS algorithm.
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.
The current implementation has not undergone extensive testing yet,
and should perhaps be regarded as experimental. Specifically, the internal
Eigenvector calculation does not always converge in extreme cases where the
Eigenvalue is close to zero. However, when it does converge, it always
converges to the same results as kernelpls.fit
, up to
numerical inacurracies.
The algorithm also has a bit of overhead, so when the number of observations
is moderately high, kernelpls.fit
can be faster even if the
number of predictors is much higher. The relative speed of the algorithms
can also depend greatly on which BLAS and/or LAPACK library is linked
against.
BjørnHelge Mevik
Rännar, S., Lindgren, F., Geladi, P. and Wold, S. (1994) A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer Objects. Part 1: Theory and Algorithm. Journal of Chemometrics, 8, 111–125.
mvr
plsr
cppls
pcr
kernelpls.fit
simpls.fit
oscorespls.fit
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