rpca
using ggplot
.Creates a pretty plot which is showing the correlation of the original variable with the principal component (PCs).
ggcorplot(
rpcaObj,
pcs = c(1, 2),
loadings = TRUE,
var_labels = FALSE,
var_labels.names = NULL,
alpha = 1,
top.n = NULL
)
Object returned by the rpca
function.
Array_like. An array with two values indicating the two PCs which should be used for plotting. By default the first two PCs are used, e.g., \(c(1,2)\).
Bool (\(TRUE\), \(FALSE\)), optional. If \(TRUE\), the eigenvectors are unit scaled by the square root of the eigenvalues \(W = W * diag(sqrt(eigvals))\).
Bool (\(TRUE\), \(FALSE\)), optional. Plot variable names, if \(TRUE\).
Array_like, optional. User specific labels for the variables
Scalar, optional. Alpha transparency of the arrows.
Scalar, optional. Number of (most influencial) variables to label with small circles.