biplot-methods {pcaMethods} | R Documentation

## Plot a overlaid scores and loadings plot

### Description

Visualize two-components simultaneously

### Usage

    ## S3 method for class 'pcaRes'
    biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE,
      ...)

    ## S4 method for signature 'pcaRes'
    biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE,
      ...)

### Arguments

`x` | a pcaRes object
`choices` | which two pcs to plot
`scale` | The variables are scaled by lambda^scale and the observations are scaled by lambda ^ (1-scale) where `lambda` are the singular values as computed by `princomp`. Normally 0 <= scale <= 1, and a warning will be issued if the specified 'scale' is outside this range.
`pc.biplot` | If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then the inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance.
`...` | optional arguments to be passed to `biplot.default`.

### Details

This is a method for the generic function 'biplot'. There is considerable confusion over the precise definitions: those of the original paper, Gabriel (1971), are followed here. Gabriel and Odoroff (1990) use the same definitions, but their plots actually correspond to `pc.biplot = TRUE`.

### Value

a plot is produced on the current graphics device.

### Author(s)

Kevin Wright, Adapted from `biplot.prcomp`

### See Also

`prcomp`, `pca`, `princomp`

### Examples

    data(iris)
    pcIr <- pca(iris[,1:4])
    biplot(pcIr)

[Package pcaMethods version 1.74.0 Index]