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

Description Usage Arguments Author(s) See Also Examples

View source: R/ggcorplot.R

Description

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

Usage

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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.

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Author(s)

N. Benjamin Erichson, [email protected]

See Also

rpca, ggplot

Examples

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Benli11/rPCA documentation built on May 5, 2019, 2:41 p.m.