pca_kmeans_plot2D: 2D PCA k-means plot
In specmine: Metabolomics and Spectral Data Analysis and Mining

Groups the points with the clusters given by k-means in a 2D PCA scores plot.

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pca_kmeans_plot2D(dataset, pca.result, num.clusters = 3, 
pcas = c(1, 2), kmeans.result = NULL, labels = FALSE, bw=FALSE,
ellipses = FALSE, leg.pos = "right", xlim = NULL, ylim = NULL)

`dataset`	list representing the dataset from a metabolomics experiment.
`pca.result`	prcomp object with the PCA results.
`num.clusters`	number of clusters of k-means.
`pcas`	vector with the principal components to be plotted.
`kmeans.result`	result from k-means. If null k-means is performed in the function.
`labels`	boolean value indicating if the samples' labels will be shown.
`ellipses`	boolean value that indicates if an ellipse will be drawn on each group of the metadata's variable. Ellipses will not be drawn if bw=TRUE.
`bw`	if TRUE, it will be displayed a black and white plot. It defaults to FALSE.
`leg.pos`	legend position.
`xlim`	vector with two positions with the x-axis limits.
`ylim`	vector with two positions with the y-axis limits.

  ## Example of a 2D PCA k-means plot
  library(specmine.datasets)
  data(cachexia)
  pca.result = pca_analysis_dataset(cachexia)
  pca_kmeans_plot2D(cachexia, pca.result, num.clusters = 3, pcas = c(1,2))