hypervolume_thin

Many hypervolume algorithms have computational complexities that scale with the number of random points used to characterize a hypervolume (<code>@RandomPoints</code>). This value can be reduced to improve runtimes at the cost of lower resolution.

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

hypervolume_thin function

<dl> <dt>hv</dt>
<dd>An object of class <code>Hypervolume</code></dd> <dt>factor</dt>
<dd>A number in (0,1) describing the fraction of random points to keep.</dd> <dt>num.points</dt>
<dd>A number describing the number random points to keep.</dd></dl>

Arguments

Reduces the number of random points in a hypervolume — hypervolume_thin

<dl>

 <dt>hv</dt>
<dd>An object of class <code>Hypervolume</code></dd>

 <dt>factor</dt>
<dd>A number in (0,1) describing the fraction of random points to keep.</dd>

 <dt>num.points</dt>
<dd>A number describing the number random points to keep.</dd>

</dl>

hypervolume_thin: Reduces the number of random points in a hypervolume

Description

Usage

Value

Arguments

Details

Examples