Multiblock Redundancy Analysis - mbRDA

This is a wrapper for the ade4::mbpcaiv function for computing mbRDA.

Usage

mbrda(formula, data, subset, na.action, X = NULL, Y = NULL, ncomp = 1, ...)

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.
Y: matrix of responses.
ncomp: integer number of PLS components.
...: additional arguments to ade4::mbpcaiv.

Value

multiblock, mvr object with scores, block-scores and block-loading. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

Details

mbRDA is a multiblock formulation of Redundancy (Data) Analysis. RDA is theoretically between PLS and GCA. Like GCA, RDA does not consider correlations within X, but like PLS it does consider correlations within Y. RDA can also be viewed as a PCR of Y constrained to have scores that are also linear combinations of X. If the adegraphics package is attached, a nice overview can be plotted as plot(mbr$mbpcaiv) following the example below.

References

Bougeard, S., Qannari, E.M., Lupo, C., andHanafi, M. (2011). From Multiblock Partial Least Squares to Multiblock Redundancy Analysis. A Continuum Approach. Informatica, 22(1), 11–26.

Examples

# Convert data.frame with AsIs objects to list of matrices
data(potato)
potatoList <- lapply(potato, unclass)

mbr <- mbrda(Sensory ~ Chemical + Compression, data = potatoList, ncomp = 10)
mbr.XY <- mbrda(X = potatoList[c('Chemical','Compression')], Y = potatoList[['Sensory']], 
                ncomp = 10)
print(mbr)
#> Multiblock RDA 
#> 
#> Call:
#> mbrda(formula = Sensory ~ Chemical + Compression, data = potatoList,     ncomp = 10)
scoreplot(mbr) # Exploiting mvr object structure from pls package