R: Variables factor map for 'rpca' using 'ggplot'.

ggcorplot {rsvd}

R Documentation

Variables factor map for `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)

`rpcaObj`	Object returned by the `rpca` function.
`pcs`	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).
`loadings`	Bool (TRUE, FALSE), optional. If TRUE, the eigenvectors are unit scaled by the square root of the eigenvalues W = W diag(sqrt(eigvals))*.
`var_labels`	Bool (TRUE, FALSE), optional. Plot variable names, if TRUE.
`var_labels.names`	Array_like, optional. User specific labels for the variables
`alpha`	Scalar, optional. Alpha transparency of the arrows.
`top.n`	Scalar, optional. Number of (most influencial) variables to label with small circles.
`.................`	.

N. Benjamin Erichson, erichson@uw.edu

[Package rsvd version 0.9 Index]