This function returns a partition that summarizes the partition distribution using the least-square clustering method (Dahl 2006), with extensions to perform greedy optimization and limit the number of subsets.
estimate.partition(x, pairwise.probabilities = NULL,
max.subsets = 0, max.scans = 0, parallel = TRUE)An object from the sample.partitions function.
An object of class shallot.pairwiseProbability obtained from pairwise.probabilities. If not supplied, it will be computed from x.
An integer limiting the number of subsets. Defaults to 0, which does not impose a constraint on the number of subsets.
An integer controlling the greedy search. Defaults to 0, which disables the greedy search.
Should all of the CPU cores should be used? Defaults to TRUE.
A partition as a vector of cluster labels.
Dahl, D. B. (2006), Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, in Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller, Marina Vannucci (Eds.), Cambridge University Press.
sample.partitions,
process.samples,
plot.partition,
adj.rand.index
# NOT RUN {
example(shallot)
# }
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