A wrapper which carries out classical PCA analysis on a
Spectra object. The user can select various options for
scaling. There is no normalization by rows - do this manually using
normSpectra. There is an option to control centering, but
this is mainly for compatibility with the aov_pcaSpectra
series of functions. Centering the data should always be done in PCA and it
is the default here.
c_pcaSpectra(spectra, choice = "noscale", cent = TRUE)An object of S3 class Spectra().
A character string indicating the choice of scaling. One of
c("noscale", "autoscale", "Pareto"). "autoscale"
is called "standard normal variate" or "correlation matrix PCA" in some literature.
Logical: whether or not to center the data. Always center the data unless you know it to be already centered.
An object of class prcomp, modified to include a list
element called $method, a character string describing the
pre-processing carried out and the type of PCA performed (used to annotate
plots).
The scale choice autoscale scales the columns by their standard
deviation. Pareto scales by the square root of the standard
deviation.
K. Varmuza and P. Filzmoser Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press, 2009.
prcomp for the underlying function,
s_pcaSpectra for sparse PCA calculations,
r_pcaSpectra for robust PCA calculations,
irlba_pcaSpectra for PCA via the IRLBA algorithm.
Additional documentation at https://bryanhanson.github.io/ChemoSpec/
For displaying the results, ChemoSpecUtils::plotScree(), ChemoSpecUtils::plotScores(), plotLoadings(), plot2Loadings(), sPlotSpectra().
if (FALSE) {
# This example assumes the graphics output is set to ggplot2 (see ?GraphicsOptions).
library("ggplot2")
data(metMUD1)
pca <- c_pcaSpectra(metMUD1)
p1 <- plotScree(pca)
p1
p2 <- plotScores(metMUD1, pca, pcs = c(1, 2), ellipse = "cls", tol = 0.05)
p2 <- p2 + ggtitle("Scores: metMUD1 NMR Data")
p2
p3 <- plotLoadings(metMUD1, pca, loads = 1:2, ref = 1)
p3 <- p3 + ggtitle("Loadings: metMUD1 NMR Data")
p3
}