orthlsplsCv: Low Level Cross-Validation Function

In lspls: LS-PLS Models

Description

Low-level function to perform the cross-validation in lsplsCv.

Usage

orthlsplsCv(Y, X, Z, ncomp, segments, trace = FALSE, ...)

Arguments

Y	matrix. Response matrix.
X	matrix. The first predictor matrix (typically a design matrix).
Z	list. List of predictor matrices.
ncomp	list. The number of components to fit from each matrix.
segments	list. The segments to use.
trace	logical; if TRUE, the segment number is printed for each segment.
...	Further arguments. Currently not used.

Details

This function is not meant to be called directly by the user. It performs cross-validation of ortogonalized LS-PLS-models without splitting of parallell matrices into common and unique components. See the references for details.

Value

An array of cross-validated predictions. The first dimension corresponds to the observations, the second to the responses, and the rest to the number of components of the PLS models.

Author(s)

Bjørn-Helge Mevik

References

Jørgensen, K., Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables. Journal of Chemometrics, 18(10), 451–464.

Jørgensen, K., Mevik, B.-H., Næs, T. Combining Designed Experiments with Several Blocks of Spectroscopic Data. (Submitted)

Mevik, B.-H., Jørgensen, K., Måge, I., Næs, T. LS-PLS: Combining Categorical Design Variables with Blocks of Spectroscopic Measurements. (Submitted)

See Also

lspls, lsplsCv, orthlspls.fit

lspls documentation built on May 2, 2019, 12:19 p.m.