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.

`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`

).

- pc
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
*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.

- ...
Parameters to be passed to the plotting routines. *Applies to base graphics only*.

## 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`

plot object is returned if the
value is assigned. The plot can be modified in the usual `ggplot2`

manner.

## 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).

## Author

Bryan A. Hanson (DePauw University), Tejasvi Gupta & Matthew J. Keinsley.

## Examples

```
# 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 <- sPlotSpectra(spectra = SrE.IR, pca = pca, pc = 1, tol = 0.001)
p <- p + ggtitle(myt)
p
```