shallot (version 0.4.8)

estimate.partition: Estimate Partition

Description

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.

Usage

estimate.partition(
  x,
  pairwise.probabilities = NULL,
  max.subsets = 0,
  max.scans = 0,
  parallel = TRUE
)

Arguments

x

An object from the sample.partitions function.

pairwise.probabilities

An object of class shallot.pairwiseProbability obtained from pairwise.probabilities. If not supplied, it will be computed from x.

max.subsets

An integer limiting the number of subsets. Defaults to 0, which does not impose a constraint on the number of subsets.

max.scans

An integer controlling the greedy search. Defaults to 0, which disables the greedy search.

parallel

Should all of the CPU cores should be used? Defaults to TRUE.

Value

A partition as a vector of cluster labels.

References

Dahl, D. B., Day, R., and Tsai, J. (2017), Random Partition Distribution Indexed by Pairwise Information, Journal of the American Statistical Association, 112, 721-732. <DOI:10.1080/01621459.2016.1165103>

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.

See Also

sample.partitions, process.samples, plot.partition, adj.rand.index

Examples

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example(shallot)
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