expectation_ball

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

Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.

Benjamin Blonder

hypervolume

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

expectation_ball function

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

Arguments

Hypersphere expectation — expectation_ball

<dl>

 <dt>input</dt>
<dd>A m x n matrix or data frame, where m is the number of observations and n is the dimensionality.</dd>

 <dt>point.density</dt>
<dd>The point density of the output expectation. If <code>NULL</code>, defaults to <code>v / num.points</code> where <code>d</code> is the dimensionality of the input and v is the volume of the hypersphere.</dd>

 <dt>num.samples</dt>
<dd>The number of points in the output expectation. If <code>NULL</code>, defaults to <code>10^(3+sqrt(ncol(d)))</code> where <code>d</code> is the dimensionality of the input. <code>num.points</code> has priority over <code>point.density</code>; both cannot be specified.</dd>

 <dt>use.random</dt>
<dd>If <code>TRUE</code> and the <code>input</code> is of class <code>Hypervolume</code>, sets boundaries based on the <code>@RandomPoints</code> slot; otherwise uses <code>@Data</code>.</dd>

</dl>

expectation_ball: Hypersphere expectation

Description

Usage

Value

Arguments

Examples