Uses the results from `aov_pcaSpectra`

to plot the scores.
Argument `submat`

is used to select PCA results from among those
stored in argument `PCA`

.

```
aovPCAscores(
spectra,
so,
submat = 1,
ellipse = "none",
tol = "none",
use.sym = FALSE,
leg.loc = "topright",
...
)
```

- spectra
An object of S3 class

`Spectra`

.- so
*List*of pca results created by`aov_pcaSpectra`

.- submat
Integer. Selects list element

`submat`

from`PCA`

which is a list of PCA results, each corresponding to the computation in`aov_pcaSpectra`

.- ellipse
A character vector specifying the type of ellipses to be plotted. One of

`c("both"`

,`"none"`

,`"cls"`

,`"rob")`

.`cls`

specifies classical confidence ellipses,`rob`

specifies robust confidence ellipses. An ellipse is drawn for each group unless there are three or fewer samples in the group.- 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.- use.sym
A logical; if TRUE, the color scheme is set to black and the points plotted with symbols. Applies only to

`Spectra`

objects.- leg.loc
Character; if

`"none"`

no legend will be drawn. Otherwise, any string acceptable to`legend`

.- ...
Additional parameters to be passed to

`plotScores`

. For example, you can plot confidence ellipses this way. Note that ellipses are drawn based on the groups in`spectra$groups`

, but the separation done by`aov_pcaSpectra`

is based on argument`fac`

. These may not correspond, but you can edit`spectra$groups`

to match if necessary.

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

).

- base:
A data frame or list containing the data plotted. Assign the value and run

`str()`

or`names()`

on it to see what it contains. 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. If you want the plotted values, you can access them via the base graphics mode.

Pinto, Bosc, Nocairi, Barros, and Rutledge. "Using ANOVA-PCA for Discriminant Analysis: ..." Analytica Chimica Acta 629.1-2 (2008): 47-55.

Harrington, Vieira, Espinoza, Nien, Romero, and Yergey. "Analysis of Variance--Principal Component Analysis: ..." Analytica Chimica Acta 544.1-2 (2005): 118-27.

The use of this function can be seen in
`aov_pcaSpectra`

. See also `plotScores`

.
Additional documentation at https://bryanhanson.github.io/ChemoSpec/