widekernelpls.fit: Wide Kernel PLS (R�nnar et al.)
Description
Fits a PLSR model with the wide kernel algorithm.Usage
widekernelpls.fit(X, Y, ncomp, stripped = FALSE,
tol = .Machine$double.eps^0.5, maxit = 100, ...)Value
- A list containing the following components is returned:
- coefficientsan 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. - scoresa matrix of scores.
- loadingsa matrix of loadings.
- loading.weightsa matrix of loading weights.
- Yscoresa matrix of Y-scores.
- Yloadingsa matrix of Y-loadings.
- projectionthe projection matrix used to convert X to scores.
- Xmeansa vector of means of the X variables.
- Ymeansa vector of means of the Y variables.
- fitted.valuesan 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. - residualsan array of regression residuals. It has the same
dimensions as
fitted.values. - Xvara vector with the amount of X-variance explained by each
number of components.
- XtotvarTotal variance in
X. - If
stripped is TRUE, only the components
coefficients, Xmeans and Ymeans are returned.
Details
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.References
R�nnar, 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.