hypervolume_distance: Distance between two hypervolumes

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

Distance between two hypervolumes

Description

Calculates the distance between two hypervolumes either defined as the Euclidean distance between centroids or as the minimum Euclidean distance between the random points comprising either hypervolume.

Usage

hypervolume_distance(hv1, hv2, type = "centroid", 
  num.points.max = 1000, check.memory = TRUE)

Arguments

hv1	A Hypervolume object.
hv2	A Hypervolume object.
type	If 'centroid', the centroid distance; if 'minimum', the minimum distance.
num.points.max	The number of random points to subsample from each input hypervolume. Ignored if type='centroid'.
check.memory	If TRUE, prints expected memory usage and returns an error before allocating memory. Ignored if type='centroid'.

Details

Minimum distance calculations scale quadratically with npmax and may be computationally costly.

Value

The distance between the two hypervolumes.

Examples

## Not run: 
data(penguins,package='palmerpenguins')
penguins_no_na = as.data.frame(na.omit(penguins))
penguins_adelie = penguins_no_na[penguins_no_na$species=="Adelie",
                    c("bill_length_mm","bill_depth_mm","flipper_length_mm")]
penguins_chinstrap = penguins_no_na[penguins_no_na$species=="Chinstrap",
                      c("bill_length_mm","bill_depth_mm","flipper_length_mm")]

hv1 = hypervolume_gaussian(penguins_adelie)
hv2 = hypervolume_gaussian(penguins_chinstrap)

# note that minimum distance is smaller than centroid distance as expected
hypervolume_distance(hv1, hv2, type='centroid')
hypervolume_distance(hv1, hv2, type='minimum', num.points.max=500, check.memory=FALSE)

## End(Not run)

hypervolume documentation built on May 29, 2024, 8:19 a.m.