pca_importance: PCA importance
In specmine: Metabolomics and Spectral Data Analysis and Mining

Description Usage Arguments Value Examples

Gets the importance from the PCs.

1 2	pca_importance(pca.res, pcs = 1:length(pca.res$sdev), sd = TRUE, prop = TRUE, cumul = TRUE, min.cum = NULL)

`pca.res`	prcomp object with the PCA results.
`pcs`	vector with the PCs to get.
`sd`	boolean value indicating if standard deviation will be returned or not.
`prop`	boolean value that indicates if the proportion of variance is returned or not.
`cumul`	boolean value that indicates if the cumulative variance is returned or not.
`min.cum`	allows to define minimum cumulative % of variance

Returns the information about the importance of the PCs.

  ## Example of performing a classical PCA analysis
  library(specmine.datasets)
  data(cachexia)
  pca.result = pca_analysis_dataset(cachexia)
  pca_importance(pca.result, pcs = 1:5)