hypervolume: Hypervolume construction methods

In hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

Hypervolume construction methods

Description

Constructs hypervolumes using one of several possible methods after error-checking input data.

Usage

hypervolume(data, method = "gaussian", ...)

Arguments

data	A m x n matrix or data frame, where m is the number of observations and n is the dimensionality.
method	One of "box" (box kernel density estimation), "gaussian" (Gaussian kernel density estimation), or "svm" (one-class support vector machine). See respective functions for details.
...	Further arguments passed to hypervolume_box, hypervolume_gaussian, or hypervolume_svm.

Details

Checks for collinearity, missingness of input data, and appropriate random point coverage. Generates warning/errors as appropriate.

Value

A Hypervolume-class object corresponding to the inferred hypervolume.

See Also

weight_data, estimate_bandwidth, expectation_convex, expectation_ball, expectation_box, hypervolume_threshold

Examples

data(penguins,package='palmerpenguins')
penguins_no_na = as.data.frame(na.omit(penguins))
penguins_adelie = penguins_no_na[penguins_no_na$species=="Adelie",
                    c("bill_length_mm","bill_depth_mm","flipper_length_mm")]
hv = hypervolume(penguins_adelie,method='box')

hypervolume documentation built on May 29, 2024, 8:19 a.m.