s-Plot of Spectra Data (Post PCA)
Produces a scatter plot of the correlation of the variables against their covariance for a chosen principal component. It allows visual identification of variables driving the separation and thus is a useful adjunct to traditional loading plots.
sPlotSpectra(spectra, pca, pc = 1, tol = 0.05, ...)
spectra |
An object of S3 class |
pca |
The result of a pca calculation on |
pc |
An integer specifying the desired pc plot. |
tol |
A number describing the fraction of points to be labeled.
|
... |
Additional parameters to be passed to plotting functions. |
A data frame containing the frequency, covariance and correlation of
the selected pc for the Spectra object. A plot of the
correlation vs. covariance is created.
Matthew J. Keinsley and Bryan A. Hanson, DePauw University.
Wiklund, Johansson, Sjostrom, Mellerowicz, Edlund, Shockcor, Gottfries, Moritz, and Trygg. "Visualization of GC/TOF-MS-Based Metabololomics Data for Identification of Biochemically Interesting Compounds Usings OPLS Class Models" Analytical Chemistry Vol.80 no.1 pgs. 115-122 (2008).
Additional documentation at https://bryanhanson.github.io/ChemoSpec/
data(SrE.IR) IR.pca <- c_pcaSpectra(SrE.IR) myt <- expression(bolditalic(Serenoa) ~ bolditalic(repens) ~ bold(IR ~ Spectra)) splot <- sPlotSpectra( spectra = SrE.IR, pca = IR.pca, pc = 1, tol = 0.001, main = myt )
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