sPlotSpectra: s-Plot of Spectra Data (Post PCA)

Description

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.

Usage

sPlotSpectra(spectra, pca, pc = 1, tol = 0.05, ...)

Arguments

spectra

An object of S3 class Spectra.

pca

The result of a pca calculation on Spectra (i.e. the output from c_pcaSpectra or r_pcaSpectra).

An integer specifying the desired pc plot.

tol

A number describing the fraction of points to be labeled. tol = 1.0 labels all the points; tol = 0.05 labels the most extreme 5 percent.

…

Additional parameters to be passed to plotting functions.

Value

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.

References

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).

Examples

Run this code

# NOT RUN {
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)

# }