hypervolume: Hypervolume construction methods

In dc165/Hypervolume-Dev: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

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(iris)
hv = hypervolume(data=subset(iris, Species=="setosa")[,1:2],method='box')
summary(hv)

dc165/Hypervolume-Dev documentation built on Dec. 13, 2020, 6:02 p.m.