Automatic plotting of PCA objects

Usage

# S3 method for class 'prcomp'
autoplot(object, dims = c(1, 2), biplot = FALSE, group = NULL, ...)

# S3 method for class 'prcomp'
autolayer(
  object,
  dims = c(1, 2),
  group = NULL,
  labels = NULL,
  type = c("t2data", "t2mean", "c2data"),
  outliers = TRUE,
  level = 0.95,
  ...
)

Arguments

object: An object of prcomp class
dims: Dimensions to plot
biplot: whether to show the loadings as well as the scores
group: Groups of the data to be shown on the plot
...: ignored
labels: optionally, a vector of labels for showing the outliers. If NULL, the outliers will be identified by row number.
type: The type of the coverage / confidence area shown by autolayer.prcomp(), can be one of t2data (T2 Hotelling coverage), c2data (chi-squared coverage) or t2mean (T-squared based confidence area for the group mean).
outliers: if TRUE, label the outliers.
level: Either coverage probability (for type = "t2data" and "c2data") or confidence level (for type = "t2mean").

Value

A ggplot2 object

Details

The functions autoplot.prcomp and autolayer.prcomp are for automatic plotting of prcomp objects, similar to ggfortify::autoplot.prcomp.

Note, however, that the group parameter is a vector of the same length as the number of rows in the PCA object, rather than a column name in the data frame.

Examples

pca <- prcomp(iris[,1:4], scale.=TRUE)

library(ggplot2)
autoplot(pca, group = iris$Species) +
  autolayer(pca, group = iris$Species)


# show the 90% confidence area for the group means
autoplot(pca, group = iris$Species) +
  autolayer(pca, group = iris$Species,
            type="t2mean", level = 0.90,
            outliers = FALSE)