R: Autoplot PCA-likes

autoplot.pca_common {ggfortify} | R Documentation

## Autoplot PCA-likes

### Description

Autoplot PCA-likes

### Usage

    ## S3 method for class 'pca_common'
    autoplot(object, data = NULL, scale = 1, x = 1,
      y = 2, variance_percentage = TRUE, ...)

### Arguments

`object` | PCA-like instance
`data` | Joined to fitting result if provided.
`scale` | scaling parameter, disabled by 0
`x` | principal component number used in x axis
`y` | principal component number used in y axis
`variance_percentage` | show the variance explained by the principal component?
`...` | other arguments passed to `ggbiplot`

### Examples

    autoplot(stats::prcomp(iris[-5]))
    autoplot(stats::prcomp(iris[-5]), data = iris)
    autoplot(stats::prcomp(iris[-5]), data = iris, colour = 'Species')
    autoplot(stats::prcomp(iris[-5]), label = TRUE, loadings = TRUE, loadings.label = TRUE)
    autoplot(stats::prcomp(iris[-5]), frame = TRUE)
    autoplot(stats::prcomp(iris[-5]), data = iris, frame = TRUE,
             frame.colour = 'Species')
    autoplot(stats::prcomp(iris[-5]), data = iris, frame = TRUE,
             frame.type = 't', frame.colour = 'Species')

    autoplot(stats::princomp(iris[-5]))
    autoplot(stats::princomp(iris[-5]), data = iris)
    autoplot(stats::princomp(iris[-5]), data = iris, colour = 'Species')
    autoplot(stats::princomp(iris[-5]), label = TRUE, loadings = TRUE, loadings.label = TRUE)

    #Plot PC 2 and 3
    autoplot(stats::princomp(iris[-5]), x = 2, y = 3)

    #Don't show the variance explained
    autoplot(stats::princomp(iris[-5]), variance_percentage = FALSE)

    d.factanal <- stats::factanal(state.x77, factors = 3, scores = 'regression')
    autoplot(d.factanal)
    autoplot(d.factanal, data = state.x77, colour = 'Income')
    autoplot(d.factanal, label = TRUE, loadings = TRUE, loadings.label = TRUE)

[Package ggfortify version 0.4.3 Index]