mbpls: Multiblock Partial Least Squares - MB-PLS

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Multiblock Partial Least Squares - MB-PLS

Description

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

Usage

mbpls(
  formula,
  data,
  subset,
  na.action,
  X = NULL,
  Y = NULL,
  ncomp = 1,
  scale = FALSE,
  blockScale = c("sqrtnvar", "ssq", "none"),
  ...
)

Arguments

formula	Model formula accepting a single response (block) and predictor block names separated by + signs.
data	The data set to analyse.
subset	Expression for subsetting the data before modelling.
na.action	How to handle NAs (no action implemented).
X	list of input blocks. If X is supplied, the formula interface is skipped.
Y	matrix of responses.
ncomp	integer number of PLS components.
scale	logical for autoscaling inputs (default = FALSE).
blockScale	Either a character indicating type of block scaling or a numeric vector of block weights (see Details).
...	additional arguments to pls::plsr.

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.

Block weighting is performed after scaling all variables and is by default "sqrtnvar": 1/sqrt(ncol(X[[i]])) in each block. Alternatives are "ssq": 1/norm(X[[i]], "F")^2 and "none": 1/1. Finally, if a numeric vector is supplied, it will be used to scale the blocks after "ssq" scaling, i.e., Z[[i]] = X[[i]] / norm(X[[i]], "F")^2 * blockScale[i].

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.

References

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

• 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.

See Also

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

Examples

data(potato)
# Formula interface
mb <- mbpls(Sensory ~ Chemical+Compression, data=potato, ncomp = 5)

# ... or X and Y
mb.XY <- mbpls(X=potato[c('Chemical','Compression')], Y=potato[['Sensory']], ncomp = 5)
identical(mb$scores, mb.XY$scores)
print(mb)
scoreplot(mb, labels="names") # Exploiting mvr object structure from pls package

# Block scaling with emphasis on first block
mbs <- mbpls(Sensory ~ Chemical+Compression, data=potato, ncomp = 5, blockScale = c(10, 1))
scoreplot(mbs, labels="names") # Exploiting mvr object structure from pls package

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.