hypervolume_prune: Removes small hypervolumes from a HypervolumeList

Description

Identifies hypervolumes characterized either by a number of uniformly random points or a volume below a user-specified value and removes them from a HypervolumeList.

This function is useful for removing small features that can occur stochastically during segmentation after set operations or hole detection.

Usage

hypervolume_prune(hvlist, num.points.min = NULL, volume.min = NULL, return.ids=FALSE)

Value

A HypervolumeList pruned to only those hypervolumes of sizes above the desired value. If returnids=TRUE, instead returns a list structure with first item being the HypervolumeList and the second item being the indices of the retained hypervolumes.

Arguments

hvlist: A HypervolumeList object.
num.points.min: The minimum number of points in each input hypervolume.
volume.min: The minimum volume in each input hypervolume
return.ids: If TRUE, returns indices of input list as well as a pruned hypervolume list

Details

Either minnp or minvol (but not both) must be specified.

Examples

Run this code

if (FALSE) {
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")]

hv = hypervolume_gaussian(penguins_adelie,name='Adelie')

hv_segmented <- hypervolume_segment(hv, 
                          num.points.max=200, distance.factor=1,
                          check.memory=FALSE) # intentionally under-segment
hv_segmented_pruned <- hypervolume_prune(hv_segmented, 
                          num.points.min=20)
plot(hv_segmented_pruned)
}