plotPCA {psichomics} | R Documentation

## Create a scatterplot from a PCA object

### Description

Create a scatterplot from a PCA object

### Usage

    plotPCA(
      pca,
      pcX = 1,
      pcY = 2,
      groups = NULL,
      individuals = TRUE,
      loadings = FALSE,
      nLoadings = NULL
    )

### Arguments

`pca` | `prcomp` object
`pcX` | Character: name of the X axis of interest from the PCA
`pcY` | Character: name of the Y axis of interest from the PCA
`groups` | Matrix: groups to plot indicating the index of interest of the samples (use clinical or sample groups)
`individuals` | Boolean: plot PCA individuals
`loadings` | Boolean: plot PCA loadings/rotations
`nLoadings` | Integer: Number of variables to plot, ordered by those that most contribute to selected principal components (this allows for faster performance as only the most contributing variables are rendered); if `NULL`, all variables are plotted

### Value

Scatterplot as an `highchart` object

### See Also

Other functions to analyse principal components: `calculateLoadingsContribution()`, `performPCA()`, `plotPCAvariance()`

### Examples

    pca <- prcomp(USArrests, scale=TRUE)
    plotPCA(pca)
    plotPCA(pca, pcX=2, pcY=3)

    # Plot both individuals and loadings
    plotPCA(pca, pcX=2, pcY=3, loadings=TRUE)

    # Only plot loadings
    plotPCA(pca, pcX=2, pcY=3, loadings=TRUE, individuals=FALSE)

[Package psichomics version 1.16.0 Index]