hypervolume (version 3.1.4)

hypervolume_overlap_confidence: Confidence intervals for overlap statistics

Description

Generates confidence intervals of four different overlap statistics. In order to find the confidence interval for the overlap statistics of two hypervolumes, use hypervolume_resample twice to generate bootstraps. The function takes in paths to two sets of bootstrapped hypervolumes and gets overlap statistics for each possible pair. Confidence interval is calculated by taking a quantile of generated overlap statistics.

Usage

hypervolume_overlap_confidence(path1, path2, CI = .95, cores = 1)

Value

jaccard

Confidence interval for jaccard similarity score

sorensen

Confidence interval for sorensen similarity score

frac_unique_1

Confidence interval for fraction of first hypervolume that is unique

frac_unique_2

Confidence interval for fraction of second hypervolume that is unique

distribution

a matrix of overlap statistics used to generate the confidence intervals

Arguments

path1

A path to a directory of bootstrapped hypervolumes

path2

A path to a directory of bootstrapped hypervolumes

CI

Desired confidence interval proportion

cores

Number of logical cores to use while generating overlap statistics. If parallel backend already registered to doParallel, function will use that backend and ignore the argument in cores.

Details

The four overlap statistics are Sorensen, Jaccard, frac_unique_1, frac_unique_2. See hypervolume_overlap_statistics

Each hypervolume from path1 is overlapped with each hypervolume from path2 using hypervolume_set. The four overlap statistics are calculated for each overlap.

See Also

hypervolume_resample

Examples

if (FALSE) {
# Let us overlap two hypervolumes generated from multivariate nomral 
# distributions with different means and same covariance matrices.
sample1 = rmvnorm(150, mean = c(0, 0))
sample2 = rmvnorm(150, mean = c(0.5, 0.5))

hv1 = hypervolume(sample1)
hv2 = hypervolume(sample2)

# generates confidence intervals from quantiles of 20*20 overlaps
path1 = hypervolume_resample("mean_0_0", hv1, n = 20)
path2 = hypervolume_resample("mean_0.5_0.5", hv2, n = 20)

result = hypervolume_overlap_confidence(path1, path2)
# confidence index of Sorensen coefficient
print(result["sorensen"])

}