multiblock (version 0.8.0)

mbpls: Multiblock Partial Least Squares - MB-PLS

Description

A function computing MB-PLS scores, loadings, etc. on the super-level and block-level.

Usage

mbpls(X, Y, ncomp = 1, scale = FALSE, ...)

Arguments

X

list of input blocks.

Y

matrix of responses.

ncomp

integer number of PLS components.

scale

logical for autoscaling inputs (default = FALSE).

...

additional arguments to pls::plsr.

Value

multiblock, mvr object with super-scores, super-loadings, block-scores and block-loading, and the underlying mvr (PLS) object for the super model, with all its result and plot possibilities. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

Details

MB-PLS is the prototypical component based supervised multiblock method. It was originally formulated as a two-level method with a block-level and a super-level, but it was later discovered that it could be expressed as an ordinary PLS on concatenated weighted X blocks followed by a simple loop for calculating block-level loading weights, loadings and scores. This implementation uses the plsr function on the scaled input blocks (1/sqrt(ncol)) enabling all summaries and plots from the pls package.

References

• Wangen, L.E. and Kowalski, B.R. (1988). A multiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 3, 3<U+2013>20.

• Westerhuis, J.A., Kourti, T., and MacGregor,J.F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12, 301<U+2013>321.

See Also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.

Examples

# NOT RUN {
data(potato)
mb <- mbpls(potato[c('Chemical','Compression')], potato[['Sensory']], ncomp = 5)
print(mb)
scoreplot(mb, labels="names") # Exploiting mvr object structure from pls package
# }