Description Usage Arguments Details Value Examples

View source: R/hypervolume_thin.R

Many hypervolume algorithms have computational complexities that scale with the number of random points used to characterize a hypervolume (`@RandomPoints`

). This value can be reduced to improve runtimes at the cost of lower resolution.

1 | ```
hypervolume_thin(hv, factor = NULL, num.points = NULL)
``` |

`hv` |
An object of class |

`factor` |
A number in (0,1) describing the fraction of random points to keep. |

`num.points` |
A number describing the number random points to keep. |

Either `factor`

or `npoints`

(but not both) must be specified.

A `Hypervolume`

object

1 2 3 4 5 6 | ```
data(iris)
hv1 = hypervolume_gaussian(subset(iris, Species=="setosa")[,1:3])
# downsample to 1000 random points
hv1_thinned = hypervolume_thin(hv1, num.points=1000)
hv1_thinned
``` |

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