simpls {pls.pcr}R Documentation

De Jong's SIMPLS

Description

This function should not be called directly, but through the generic pls function with the argument method="simpls". It is much faster than the NIPALS algorithm, especially when the number of X variables increases, but gives slightly different results in the case of multivariate Y. SIMPLS truly maximises the covariance criterion. According to De Jong, the standard PLS2 algorithms lie closer to ordinary least-squares regression where a precise fit is sought; SIMPLS lies closer to PCR with stable predictions.

Usage

simpls(X, Y, ncomp, newX)

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 latent variables to be used in the modelling. The default number of latent variables is the smallest of the number of objects or the number of variables in X.
newX optional new measurements: if present, predictions will be made for them.

Value

A list containing the following components is returned:

B an array of regression coefficients for all items in ncomp. The dimensions of B are c(nvar, npred, length(ncomp)) with nvar the number of X variables and npred the number of variables to be predicted in Y.
XvarExpl Fraction of X-variance explained.
YvarExpl Fraction of Y-variance explained (one column, even for multiple Y).
Ypred predictions for newX (if asked).

References

S. de Jong, Chemometrics and Intelligent Laboratory Systems, 18 (1993) 251-263.

See Also

kernelpls mvr

Examples

data(NIR)
attach(NIR)
NIR.simpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="SIMPLS")

[Package pls.pcr version 0.2.7 Index]