ephclust: Hierarchical Agglomerative Clustering for Empirical...

In kyoustat/T4ecdf: Tools for Inference with Empirical CDF

Description

Hierarchical Agglomerative Clustering for Empirical Distributions

Usage

ephclust(
  elist,
  method = c("single", "complete", "average", "mcquitty", "ward.D", "ward.D2",
    "centroid", "median"),
  type = c("KS", "Lp", "Wasserstein"),
  p = 2
)

Examples

## 3 sets of 1d samples, 10 each and add some noise
#    set 1 : mixture of two gaussians
#    set 2 : single gamma distribution
#    set 3 : mixture of gaussian and gamma

# generate data
myn   = 50
elist = list()
for (i in 1:10){
   elist[[i]] = stats::ecdf(c(rnorm(myn, mean=-2), rnorm(myn, mean=2)))
}
for (i in 11:20){
   elist[[i]] = stats::ecdf(rgamma(2*myn,1))
}
for (i in 21:30){
   elist[[i]] = stats::ecdf(rgamma(myn,1) + rnorm(myn, mean=3))
}

# run EP-hclust with 3 different methods and 2 distance metrics
eh2ks = ephclust(elist, method="single")
eh3ks = ephclust(elist, method="complete")
eh4ks = ephclust(elist, method="median")
eh2w2 = ephclust(elist, type="wasserstein", method="single")
eh3w2 = ephclust(elist, type="wasserstein", method="complete")
eh4w2 = ephclust(elist, type="wasserstein", method="median")

# visualize
opar = par(mfrow=c(2,3))
plot(eh2ks, main="KS+single")
plot(eh3ks, main="KS+complete")
plot(eh4ks, main="KS+median")
plot(eh2w2, main="2 Wasserstein+single")
plot(eh3w2, main="2 Wasserstein+complete")
plot(eh4w2, main="2 Wasserstein+median")
par(opar)

kyoustat/T4ecdf documentation built on Dec. 15, 2019, 10:12 p.m.