Hyperoverlap can be used to detect and visualise overlap in n-dimensional space.
To explore the functions in hyperoverlap, we'll use the
iris dataset. This dataset contains 150 observations of three species of iris ("setosa", "versicolor" and "virginica"). These data are four-dimensional (Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) and are documented in
?iris. We'll set up five test datasets to explore the different functions: 1.
test1 two entities (setosa, virginica); three dimensions (Sepal.Length, Sepal.Width, Petal.Length) 1.
test2 two entities (versicolor, virginica); three dimensions (as above) 1.
test3 two entities (setosa, virginica); four dimensions 1.
test4 two entities (versicolor, virginica); four dimensions 1.
test5 all entities, all dimensions
test1 <- iris[which(iris$Species!="versicolor"),c(1:3,5)] test2 <- iris[which(iris$Species!="setosa"),c(1:3,5)] test3 <- iris[which(iris$Species!="versicolor"),] test4 <- iris[which(iris$Species!="setosa"),] test5 <- iris
Note that entities may be species, genera, populations etc.
To plot the decision boundary using
hyperoverlap_plot, the data cannot exceed three dimensions. For high-dimensional visualisation, see
library(hyperoverlap) setosa_virginica3d <- hyperoverlap_detect(test1[,1:3], test1$Species) versicolor_virginica3d <- hyperoverlap_detect(test2[,1:3], test2$Species)
To examine the result:
setosa_virginica3d@result #gives us the result: overlap or non-overlap? #>  "non-overlap" versicolor_virginica3d@result #>  "overlap" setosa_virginica3d@shape #for the non-overlapping pair, was the decision boundary linear or curvilinear? #>  "linear" hyperoverlap_plot(setosa_virginica3d) #plot the data and the decision boundary in 3d