nClustMulti {anocva}

Multiple Samples Optimal Number of Clusters Estimation

Description

Estimates the optimal number of clusters for multiple samples using either Slope or Silhouette criterion. The optimal number of clusters will be verified in the range 2,..., maxClust. Takes the mean of all samples in order to perform the estimation.

Usage

nClustMulti(dataDist, p = 1, maxClust = 20, clusteringFunction,
  criterion = c("slope", "silhouette"))

Arguments

dataDist

An matrix with n subjects. Each subject has the size of NxN and represents the distances between the elements of the sample.

p

Slope adjust parameter.

maxClust

The maximum number of clusters to be tried.

clusteringFunction

The clustering function to be used.

criterion

The criterion that will be used for estimating the number of clusters. The options are "slope" or "silhouette". If not defined, "slope" will be used.

Value

The optimal number of clusters.

References

Fujita A, Takahashi DY, Patriota AG (2014b) A non-parametric method to estimate the number of clusters. Computational Statistics & Data Analysis 73:27–39

Rousseeuw PJ (1987) Sihouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20:53–65

Examples

# Install packages if necessary
# install.packages('MASS')
# install.packages('cluster')

library(anocva)
library(MASS)
library(cluster)

set.seed(5000)

# A k-means function that returns cluster labels directly.
myKmeans = function(dist, k){
  return(kmeans(dist, k, iter.max = 50, nstart = 5)$cluster)
}

# Number of subjects in each population
nsub = 25
# Number of items in each subject
nitem = 60

# Generate simulated data
data = array(NA, c(nsub, nitem*2, 2))
data.dist = array(NA, c(nsub, nitem*2, nitem*2))
meanx = 2
delta = 0.5
# Covariance matrix
sigma = matrix(c(0.03, 0, 0, 0.03), 2)
for (i in seq(nsub)){
  sub = rbind(mvrnorm(nitem, mu = c(0, 0), Sigma = sigma ),
              mvrnorm(nitem, mu = c(meanx,0), Sigma = sigma))
  data[i,,] = sub
  data.dist[i,,] = as.matrix(dist(data[i,,]))
}

# Estimate the optimal number of clusters
r = nClustMulti(dataDist = data.dist, p = 1, maxClust = 20,
                clusteringFunction = myKmeans, criterion = "slope")
sprintf("The optimal number of clusters found was %d.", r)