This function creates a visualization of the results of the PCA applied on a sample of QTS and returns the corresponding ggplot2::ggplot object which enable further customization of the plot.

## Arguments

- object
An object of class

`prcomp_qts`

as produced by the`prcomp.qts_sample()`

method.- what
A string specifying what kind of visualization the user wants to perform. Choices are words starting with

`PC`

and ending with a PC number (in which case the mean QTS is displayed along with its perturbations due to the required PC) or`scores`

(in which case individuals are projected on the required plane). Defaults to`PC1`

.- ...
If

`what = "PC?"`

, the user can specify whether to plot the QTS in the tangent space or in the original space by providing a boolean argument`original_space`

which defaults to`TRUE`

. If`what = "scores"`

, the user can specify the plane onto which the individuals will be projected by providing a length-2 integer vector argument`plane`

which defaults to`1:2`

.

## Value

A ggplot2::ggplot object.

## Examples

```
df <- as_qts_sample(vespa64$igp[1:16])
res_pca <- prcomp(df)
#> ℹ The maximum number of principal component is 15.
# Plot the data points in a PC plane
# And color points according to a categorical variable
p <- ggplot2::autoplot(res_pca, what = "scores")
#> The `plane` length-2 integer vector argument is not specified. Defaulting to
#> 1:2.
p + ggplot2::geom_point(ggplot2::aes(color = vespa64$V[1:16]))
```