pls: oscorespls.fit – R documentation

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pls

oscorespls.fit

Orthogonal scores PLSR

Description

Fits a PLSR model with the orthogonal scores algorithm (aka the NIPALS algorithm).

Usage

oscorespls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE,
               tol = .Machine$double.eps^0.5, maxit = 100, ...)

Arguments

`X`	a matrix of observations. `NA`s and `Inf`s are not allowed.
`Y`	a vector or matrix of responses. `NA`s and `Inf`s are not allowed.
`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 `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 multi-response models.
`maxit`	positive integer. The maximal number of iterations used in the internal Eigenvector calculation.
`...`	other arguments. Currently ignored.

Details

This function should not be called directly, but through the generic functions plsr or mvr with the argument method="oscorespls". It implements the orthogonal scores algorithm, as described in Martens and Næs (1989). This is one of the two “classical” PLSR algorithms, the other being the orthogonal loadings algorithm.

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 component.
`Xtotvar`	Total variance in `X`.

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

Author(s)

Ron Wehrens and Bjørn-Helge Mevik

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.