pca_scoresplot2D: 2D PCA scores plot
In specmine: Metabolomics and Spectral Data Analysis and Mining

Shows a 2D PCA scores plot of two principal componets.

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pca_scoresplot2D(dataset, pca.result, column.class, 
pcas = c(1, 2), labels = FALSE, ellipses = FALSE, bw=FALSE,
pallette = 2, leg.pos = "right", xlim = NULL, ylim = NULL)

`dataset`	list representing the dataset from a metabolomics experiment.
`pca.result`	prcomp object with the PCA results.
`column.class`	metadata's variable.
`pcas`	vector of two elements with the PCs that will be plotted.
`labels`	boolean value indicating if the sample's labels will be displayed.
`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.
`pallette`	parameter of scale_colour_brewer from ggplot2.
`leg.pos`	position of the legend.
`xlim`	vector with two numeric values indicating the limits of the x axis.
`ylim`	vector with two numeric values indicating the limits of the y axis.

  ## Example of a 2D PCA scores plot
  library(specmine.datasets)
  data(cachexia)
  pca.result = pca_analysis_dataset(cachexia)
  pca_scoresplot2D(cachexia, pca.result, "Muscle.loss", pcas = c(1,2), 
    		   ellipses = TRUE)