# ecdfdist: Distance Measures between Multiple Empirical Cumulative... In maotai: Tools for Matrix Algebra, Optimization and Inference

## 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

 1 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.

ecdf
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ## 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)