plot2Loadings: Plot PCA Loadings from a Spectra Object Against Each Other

Description

Plots two PCA loadings specified by the user, and labels selected (extreme) points. Typically used to determine which variables (frequencies) are co-varying, although in spectroscopy most peaks are represented by several variables and hence there is a lot of co-varying going on. Also useful to determine which variables are contributing the most to the clustering on a score plot.

Usage

plot2Loadings(spectra, pca, loads = c(1, 2), tol = 0.05, ...)

Value

The returned value depends on the graphics option selected (see GraphicsOptions()).

base: None. Side effect is a plot.
ggplot2: The plot is displayed, and a ggplot2 object is returned if the value is assigned. The plot can be modified in the usual ggplot2 manner.

Arguments

spectra: An object of S3 class Spectra().
pca: 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 (it appears on the plot). This is automatically provided if ChemoSpec functions c_pcaSpectra or r_pcaSpectra were used to create pca.
loads: A vector of two integers specifying which loading vectors to plot.
tol: A number describing the fraction of points to be labeled. tol = 1.0 labels all the points; tol = 0.05 labels approximately the most extreme 5 percent. Set to 'none' to completely suppress labels. Note that a simple approach based upon quantiles is used, assumes that both x and y are each normally distributed, and treats x and y separately. Thus, this is not a formal treatment of outliers, just a means of labeling points. Groups are lumped together for the computation.
...: Parameters to be passed to the plotting routines. Applies to base graphics only.

Author

Bryan A. Hanson (DePauw University), Tejasvi Gupta.

Examples

Run this code


# This example assumes the graphics output is set to ggplot2 (see ?GraphicsOptions).
library("ggplot2")
data(SrE.IR)
pca <- c_pcaSpectra(SrE.IR)
myt <- expression(bolditalic(Serenoa) ~ bolditalic(repens) ~ bold(IR ~ Spectra))
p <- res <- plot2Loadings(SrE.IR, pca, loads = c(1, 2), tol = 0.001)
p <- p + ggtitle(myt)
p