returnCore: Assign points to core clusters

In COMMUNAL: Robust Selection of Cluster Number K

Description

Takes keyed cluster assignments of each algorithm and makes final cluster assignmenmts by majority vote. If the majority percentage is <= the specified threshold, the cluster cannot be determined and is set to 0.

Usage

returnCore(mat.key, agreement.thresh = 50)

Arguments

mat.key	Matrix of rekeyed cluster assignments. E.g., the output of clusterKeys.
agreement.thresh	Percent of algorithms required to agree for a cluster assignment to be accepted. Otherwise, cluster is set to 0. By default at least half the algorithms must agree.

Details

Can use examineCounts to help determine the agreement.thresh argument.

Author(s)

Albert Chen and Timothy E Sweeney
Maintainer: Albert Chen [e-mail]

Examples

# reindexes cluster numbers to agree
clusters <- data.frame(
  alg1=as.integer(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)),
  alg2=as.integer(c(1,1,1,1,1,3,3,3,3,3,2,2,2,2,2)),
  alg3=as.integer(c(3,3,3,3,3,1,1,1,1,1,2,2,2,2,2))
)
mat.key <- clusterKeys(clusters)
mat.key # cluster indices are relabeled
examineCounts(mat.key)
core <- returnCore(mat.key, agreement.thresh=50) # find 'core' clusters
table(core) # the 'core' clusters

# some clusters assignments are undetermined
clusters <- data.frame(
  alg1=as.integer(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,2,2,3,3)),
  alg2=as.integer(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,2,2,3,3,1)),
  alg3=as.integer(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,2,3,1,1,2,3))
)
mat.key <- clusterKeys(clusters)
mat.key # last six samples have conflicting assignments
examineCounts(mat.key)
(core <- returnCore(mat.key, agreement.thresh=66)) # need at least 2 of 3 algs to agree
table(core)
(core <- returnCore(mat.key, agreement.thresh=99)) # need all algs to agree
table(core)

COMMUNAL documentation built on May 29, 2017, 6:36 p.m.