ChemoSpec (version 6.1.10)

irlba_pcaSpectra: IRLBA PCA of Spectra Objects

Description

A wrapper which carries out IRLBA 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. The data can be supplied already centered if desired.

Usage

irlba_pcaSpectra(spectra, choice = "noscale", n = 3, center = TRUE, ...)

Value

A modified object of class prcomp and computed_via_irlba, which includes a list element called $method, a character string describing the pre-processing carried out and the type of PCA performed (used to annotate plots).

Arguments

spectra

An object of S3 class Spectra().

choice

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.

n

Integer. The number of components desired.

center

Logical. Should the data be centered? Data must be centered for PCA, either before arriving here or via this argument.

...

Other parameters to be passed to irlba.

Author

Bryan A. Hanson (DePauw University).

Details

The scale choice autoscale scales the columns by their standard deviation. Pareto scales by the square root of the standard deviation.

References

J. Baglama and L. Reichel, "Augmented Implicitly Restarted Lanczos Bidiagonalization Methods" SIAM J. Sci. Comput. (2005).

See Also

prcomp_irlba for the underlying function, c_pcaSpectra for classical PCA calculations, r_pcaSpectra for robust PCA calculations, s_pcaSpectra for sparse PCA calculations. Additional documentation at https://bryanhanson.github.io/ChemoSpec/

For displaying the results, ChemoSpecUtils::plotScree(), ChemoSpecUtils::plotScores(), plotLoadings(), plot2Loadings(), sPlotSpectra().

Examples

if (FALSE) {
# This example assumes the graphics output is set to ggplot2 (see ?GraphicsOptions).
library("ggplot2")
data(SrE.NMR)
pca <- irlba_pcaSpectra(SrE.NMR)

p1 <- plotScree(pca)
p1

p2 <- plotScores(SrE.NMR, pca, pcs = c(1, 2), ellipse = "cls", tol = 0.05)
p2 <- p2 + ggtitle("Scores: SrE NMR Data")
p2

p3 <- plotLoadings(SrE.NMR, pca, loads = 1:2, ref = 1)
p3 <- p3 + ggtitle("Loadings: SrE NMR Data")
p3
}