ecdfdist: Distance Measures between Multiple Empirical Cumulative...

In maotai: Tools for Matrix Algebra, Optimization and Inference

Distance Measures between Multiple Empirical Cumulative Distribution Functions

Description

We measure distance between two empirical cumulative distribution functions (ECDF). For simplicity, we only take an input of ecdf objects from stats package.

Usage

ecdfdist(elist, method = c("KS", "Lp", "Wasserstein"), p = 2, as.dist = FALSE)

Arguments

elist	a length N list of ecdf objects.
method	name of the distance/dissimilarity measure. Case insensitive.
p	exponent for Lp or Wasserstein distance.
as.dist	a logical; TRUE to return dist object, FALSE to return an (N\times N) symmetric matrix of pairwise distances.

Value

either dist object of an (N\times N) symmetric matrix of pairwise distances by as.dist argument.

See Also

ecdf

Examples

## toy example : 10 of random and uniform distributions
mylist = list()
for (i in 1:10){
  mylist[[i]] = stats::ecdf(stats::rnorm(50, sd=2))
}
for (i in 11:20){
  mylist[[i]] = stats::ecdf(stats::runif(50, min=-5))
}

## compute Kolmogorov-Smirnov distance
dm = ecdfdist(mylist, method="KS")

## visualize
mks  =" KS distances of 2 Types"
opar = par(no.readonly=TRUE)
par(pty="s")
image(dm[,nrow(dm):1], axes=FALSE, main=mks)
par(opar)

maotai documentation built on March 31, 2023, 6:48 p.m.