expectation_ball: Hypersphere expectation

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

Hypersphere expectation

Description

Generates expectation hypervolume corresponding to a hypersphere that minimally encloses the data.

Usage

expectation_ball(input, point.density = NULL, num.samples = NULL,
                 use.random = FALSE)

Arguments

input	A m x n matrix or data frame, where m is the number of observations and n is the dimensionality.
point.density	The point density of the output expectation. If NULL, defaults to v / num.points where d is the dimensionality of the input and v is the volume of the hypersphere.
num.samples	The number of points in the output expectation. If NULL, defaults to 10^(3+sqrt(ncol(d))) where d is the dimensionality of the input. num.points has priority over point.density; both cannot be specified.
use.random	If TRUE and the input is of class Hypervolume, sets boundaries based on the @RandomPoints slot; otherwise uses @Data.

Value

A Hypervolume-class object corresponding to the expectation.

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")]
e_ball <- expectation_ball(penguins_adelie)

hypervolume documentation built on Sept. 14, 2023, 5:08 p.m.