The PCA function that it uses is
prcomp(), which is the same as what my own package (PCAtools) and DESeq2 use.
Yes, it is performing partitioning around medoids (PAM) and identifying X number of clusters (user pre-selects desired number as second parameter to
autoplot() then performs PCA on the dataset and shades the points based on the PAM cluster assignments. Here is the proof:
g1 <- autoplot(prcomp(iris[-5]), frame = TRUE, frame.type = 'norm') g2 <- autoplot(pam(iris[-5], 2), frame = TRUE, frame.type = 'norm') require(grid) require(gridExtra) grid.arrange(g1,g2, ncol = 2)
They are the same points, but higlighted differently.
As is typical with many CRAN (and other) packages, the documentation is poor and the program functionality does not make it readily obvious what the function is doing.
In PCA, principal components are ordered by the fraction of variance explained (i.e. eigenvalues of the covariance matrix). If this doesn't make sense to you, please read some tutorial on PCA.