pls (version 2.4-3)

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, ...)

Arguments

X

a matrix of observations. NAs and Infs are not allowed.

Y

a vector or matrix of responses. NAs and Infs 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.

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.

Value

• 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.
• If stripped is TRUE, only the components coefficients, Xmeans and Ymeans are returned.

encoding

latin1

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.

See Also

mvr plsr cppls pcr kernelpls.fit simpls.fit oscorespls.fit