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Hotelling’s T² Ellipses and Outlier Detection for ggplot2

Here are a few examples of plots you can create with gghotelling:

Features:

• Classical Hotelling and data ellipses with geom_hotelling()
• Robust Hotelling ellipses using MCD estimator with robust=TRUE
• Hotelling confidence ellipses for group means with type="t2mean"
• Kernel density coverage contours with geom_kde()
• Outlier detection with outliers()
• Outlier visualization with stat_outliers() and plot_outliers()
• Convex hulls with geom_hull()
• Bagplots with geom_bag()
• Autoplot and autolayer methods for prcomp objects

See the vignette for more details.

Installation

You can install the development version of gghotelling from GitHub with:

# install.packages("pak")
pak::pak("january3/gghotelling")

Minimal Example

The package defines a new geom, geom_hotelling(), which can be used to add Hotelling ellipses to ggplot2 scatter plots. Here are two examples of usage:

library(ggplot2)
library(gghotelling)

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

# minimum example
p1 <- ggplot(df, aes(PC1, PC2, color=Species)) +
  geom_hotelling() +
  geom_point()

# set custom CI/coverage level
p2 <- ggplot(df, aes(PC1, PC2, color=Species)) +
  geom_hotelling(alpha=0.1, aes(fill = Species), level=.99) +
  geom_point()

cowplot::plot_grid(p1, p2, ncol=2)

AI and other tools disclosure

I used LLM modes to a) detect bugs, spelling errors, clerical mistakes and other deficiencies in the code and b) better understand the ggplot2 specific code (especially after_stat()).

Furthermore, I used devtools, checktor and covr.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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