Description Usage Arguments Details Author(s) Examples
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.
1 | returnCore(mat.key, agreement.thresh = 50)
|
mat.key |
Matrix of rekeyed cluster assignments. E.g., the output of |
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. |
Can use examineCounts
to help determine the agreement.thresh
argument.
Albert Chen and Timothy E Sweeney
Maintainer: Albert Chen acc2015@stanford.edu
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # 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)
|
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