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

In erichson/rSVD: Randomized Singular Value Decomposition

Description

Creates a pretty plot which is showing the correlation of the original variable with the principal component (PCs).

Usage

ggcorplot(
  rpcaObj,
  pcs = c(1, 2),
  loadings = TRUE,
  var_labels = FALSE,
  var_labels.names = NULL,
  alpha = 1,
  top.n = NULL
)

Arguments

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.

Author(s)

N. Benjamin Erichson, [e-mail]

See Also

rpca, ggplot

Examples

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erichson/rSVD documentation built on April 16, 2021, 7:34 a.m.