mbpls: Multiblock Partial Least Squares - MB-PLS
In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Description Usage Arguments Details Value References See Also Examples

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

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

`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.

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.

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.

Wangen, L.E. and Kowalski, B.R. (1988). A multiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 3, 3–20.
Westerhuis, J.A., Kourti, T., and MacGregor,J.F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12, 301–321.

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

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