`R/plot2Loadings.R`

`plot2Loadings.Rd`

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.

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

- 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*.

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.

See `plotLoadings`

to plot one loading against the
original variable (frequency) axis. See `sPlotSpectra`

for
a different approach. Additional documentation at
https://bryanhanson.github.io/ChemoSpec/

```
# 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
```