salso (version 0.2.5)

dlso: Latent Structure Optimization Based on Draws

Description

This function provides a partition to summarize a partition distribution using the draws-based latent structure optimization (DLSO) method, which is also known as the least-squares clustering method (Dahl 2006). The method seeks to minimize an estimation criterion by picking the minimizer among the partitions supplied by the draws argument. The implementation currently supports the minimization of several partition estimation criteria. For details on these criteria, see partition.loss.

Usage

dlso(candidates, loss = VI(), x = NULL, parallel = FALSE)

Arguments

candidates

A \(B\)-by-\(n\) matrix, where each of the \(B\) rows represents a clustering of \(n\) items using cluster labels. For the \(b\)th clustering, items \(i\) and \(j\) are in the same cluster if x[b,i] == x[b,j]. One of the rows will be used as the partition estimate.

loss

See the documentation for this argument in partition.loss.

x

See the documentation for this argument in partition.loss.

parallel

This argument is currently ignored and will be removed in the future.

Value

An integer vector giving the estimated partition, encoded using cluster labels.

See Also

partition.loss, psm, summary.salso.estimate, salso

Examples

# NOT RUN {
# For examples, use 'nCores=1' per CRAN rules, but in practice omit this.
dlso(iris.clusterings, loss=VI())
probs <- psm(iris.clusterings, nCores=1)
dlso(iris.clusterings, loss=binder(), x=probs)

# Compute loss with all draws, but pick the best among the first 10.
dlso(iris.clusterings[1:10,], loss=VI(), x=iris.clusterings)

# }