Draw a bi-plot, comparing 2 selected principal components / eigenvectors.
Choosing PCs with the Gavish-Donoho method
Choosing PCs with the Marchenko-Pastur limit
Correlate principal components to continuous variable metadata and test significancies of these.
Find the elbow point in the curve of variance explained by each successive PC. This can be used to determine the number of PCs to retain.
Return the principal component labels for an object of class 'pca'.
Return component loadings for principal components from an object of class 'pca'.
Return the explained variation for each principal component for an object of class 'pca'.
Draw multiple bi-plots.
Perform Horn's parallel analysis to choose the number of principal components to retain.
PCAtools
Plot the component loadings for selected principal components / eigenvectors and label variables driving variation along these.
Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.