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

ggcorplot {rsvd} | R Documentation

## Variables factor map for `rpca` using `ggplot`.

### 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, erichson@uw.edu

### See Also

`rpca`, `ggplot`

### Examples

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[Package rsvd version 0.9 Index]