hypervolume (version 1.2.2)

negative_features: Negative feature detection

Description

Detects the negative features of an observed hypervolume relative to an expectation

Usage

negative_features(hv_obs, hv_exp, set_npoints_max = NULL, set_check_memory = TRUE)

Arguments

hv_obs

The observed hypervolume whose negative features are to be detected

hv_exp

The expected hypervolume that provides a baseline expectation geometry

set_npoints_max

Maximum number of points to be used for set operations comparing hv_obs to hv_exp. Defaults to 100*10^n, where n is the dimensionality of the input hypervolumes.

set_check_memory

If TRUE, estimates the memory usage required to perform set operations, then exits. If FALSE, prints resource usage and continues algorithm. It is useful for preventing crashes to check the estimated memory usage on large or high

Value

• A Hypervolume object containing a uniformly random set of points describing the negative features in hv_obs. Note that the point density of this object is likely to be much lower than that of the input hypervolumes due to the stochastic geometry algorithms used.

Details

This algorithm has a good Type I error rate (rarely detects negative features that do not actually exist). However it can have a high Type II error rate (failure to find negative features when they do exist). To reduce this error rate, make sure to re-run the algorithm with input hypervolumes with higher values of @PointDensity, or increase set_npoints_max.

The algorithm performs the set difference between the observed and expected hypervolumes, then removes stray points in this hypervolume by deleting any random point whose distance from any other random point is greater than expected.

Examples

# generate annulus data
data_annulus <- data.frame(matrix(data=runif(4000),ncol=2))
names(data_annulus) <- c("x","y")
data_annulus  <- subset(data_annulus, 
sqrt((x-0.5)^2+(y-0.5)^2) > 0.4 & sqrt((x-0.5)^2+(y-0.5)^2) < 0.5)

# MAKE HYPERVOLUME (low reps for fast execution)
hv_annulus <- hypervolume(data_annulus,bandwidth=0.1,name='annulus',reps=500)

# GET CONVEX EXPECTATION
hv_convex <- expectation_convex(hv_annulus, check_memory=FALSE)

# DETECT FEATURES (low npoints for fast execution)
features_annulus <- negative_features(
                      hv_obs=hv_annulus, 
                      hv_exp=hv_convex,
                      set_check_memory=FALSE)


# PLOT RESULTS
plot(hypervolume_join(hv_annulus, features_annulus))