hypervolume_estimate_probability: Estimate probability a given location

Description

Estimates probability density at one or more of points within or outside a hypervolume. The estimation is carried out as the weighted sum of the probability density of all subsampled random points in the input hypervolume, where the weights are proportional to the distance from the test point raised to a certain power. The default power, -1, corresponds to inverse distance weighting.

Usage

hypervolume_estimate_probability(hv, points, 
                reduction.factor = 1, weight.exponent = -1, 
                set.edges.zero = TRUE, edges.zero.distance.factor = 1, 
                verbose = TRUE)

Arguments

An input hypervolume

points

A m x n matrix of m points of dimensionality n (same as the input hypervolume). These are the points at which the probability is to be estimated.

reduction.factor

A value between 0 and 1 corresponding to a thinning factor applied to random points of the input hypervolume. Smaller values result in faster runtimes but lower accuracy.

weight.exponent

The exponent of the distance weights. Should be negative and probably does not need to be changed.

set.edges.zero

If TRUE, any test points more than a critical distance (multiplied by edges.zero.distance.factor) away from a random point in the input hypervolume are assumed to have probability zero. Otherwise the weighted sum is used with no further modification.

edges.zero.distance.factor

Positive number used to multiply the critical distance for set.edges.zero. Larger values lead to more stringent criteria for test points being set to zero.

verbose

If TRUE, prints diagnostic progress messages.

Value

A vector of probability densities of length corresponding to m, the number of input points.

Details

Identifies the uniformly random points enclosed within a hypersphere centered on the point of interest, then averages the probability density at each of these points.

Examples

Run this code

# NOT RUN {
data(iris)
iris_ss = subset(iris, Species=="setosa")[,1:3]
hv = hypervolume_box(data=iris_ss,name='setosa')
probs <- hypervolume_estimate_probability(hv, points=iris_ss) 
# first point should have non-zero density, second, zero
# }