dlso: Perform Draws-Based Latent Structure Optimization

Description

Among the supplied latent structures, this function picks the structure that minimizes one of various loss functions.

Usage

dlso(x, loss = c("squaredError", "absoluteError", "binder",
  "lowerBoundVariationOfInformation")[1], multicore = TRUE,
  expectedPairwiseAllocationMatrix = NULL)

Arguments

A collection of clusterings as a B-by-n matrix, each of the B rows represents a clustering of n items using cluster labels. For clustering b, items i and j are in the same cluster if x[b,i] == x[b,j].

loss

One of "squaredError", "absoluteError", "binder", or "lowerBoundVariationOfInformation" to indicate the optimization should seeks to minimize expectation of the squared error loss, absolute error loss, Binder loss (Binder 1978), or the lower bound of the variation of information loss (Wade & Ghahramani 2017), respectively. The first three are equivalent.

multicore

Logical indicating whether computations should take advantage of multiple CPU cores.

expectedPairwiseAllocationMatrix

A n-by-n symmetric matrix whose (i,j) elements gives the estimated expected number of times that items i and j are in the same subset (i.e., cluster). If NULL, it is computed from x.

Value

A list A clustering (as a vector of cluster labels).

References

Wade, S. and Ghahramani, Z. (2017). Bayesian cluster analysis: Point estimation and credible balls. Bayesian analysis.

Binder, D. (1978). Bayesian Cluster Analysis. Biometrika, 65: 31<U+2013>38.

Examples

Run this code

# NOT RUN {
suppressWarnings({  # For testing purposes, suppress deprecation warning.

dlso(iris.clusterings)

})

# }