mbrda | R Documentation |
This is a wrapper for the ade4::mbpcaiv
function for computing mbRDA.
mbrda(formula, data, subset, na.action, X = NULL, Y = NULL, ncomp = 1, ...)
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 |
|
Y |
|
ncomp |
|
... |
additional arguments to ade4::mbpcaiv. |
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.
multiblock, mvr
object with scores, block-scores and block-loading. Relevant plotting functions: multiblock_plots
and result functions: multiblock_results
.
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.
Overviews of available methods, multiblock
, and methods organised by main structure: basic
, unsupervised
, asca
, supervised
and complex
.
# 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)
scoreplot(mbr) # Exploiting mvr object structure from pls package
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