Description Usage Arguments Value Author(s) References See Also Examples
Carry out multivariate image analysis of a Spectra2D
object
(multivariate image analysis is the same as a Tucker1 analysis).
Function pcasup1
from package ThreeWay is used.
1 | miaSpectra2D(spectra)
|
spectra |
An object of S3 class |
A list per pcasup1
. Of particular interest are the
elements C
containing the eigenvectors and 1c
containing the eigenvalues.
We add the class mia
to the list for our use later, as well as a method
element for annotating plots.
Bryan A. Hanson, DePauw University.
A. Smilde, R. Bro and P. Geladi "Multi-way Analysis: Applications in the Chemical Sciences" Wiley (2004). See especially Example 4.5.
P. Geladi and H. Grahn "Multivariate Image Analysis" Wiley (1996). Note that in this text the meanings of scores and loadings are reversed from the usual spectroscopic uses of the terms.
For other data reduction methods for Spectra2D
objects, see
pfacSpectra2D
and popSpectra2D
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | library("ggplot2")
data(MUD1)
res <- miaSpectra2D(MUD1)
# plotScores & plotScree use ggplot2 graphics
p1 <- plotScores(MUD1, res, tol = 1.0, ellipse = "cls")
p1 <- p1 + ggtitle("MIA Scores")
p1
p2 <- plotScree(res)
p2
# plotLoadings2D uses base graphics
MUD1a <- plotLoadings2D(MUD1, res,
load_lvls = seq(-90, 0, 10),
main = "MIA Comp. 1 Loadings"
)
# Selection of loading matrix levels can be aided by the following
# Use MUD1a$names to find the index of the loadings
inspectLvls(MUD1a,
which = 11, ylim = c(0, 80),
main = "Histogram of Loadings Matrix"
)
|
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