ggfortify (version 0.4.17)

autoplot.pca_common: Autoplot PCA-likes

Description

Autoplot PCA-likes

Usage

# S3 method for 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)