relabel function

A Relabel Algorithm

Function relabel implements Algorithm 2 in Matthew Stephens (2000) JRSSB for the posterior allocation probability matrix, minimizing the Kullback-Leibler distance. Step 2 in this algorithm is solved using the Hungarian (Munkres) algorithm to the assignment problem.

relabel(probs.mcmc, nIter, nItem, nClust, 
    RELABEL.THRESHOLD, PRINT = 0, PACKAGE="DIRECT")

Arguments

• probs.mcmc: A nItem*nIter-by-nClust matrix of samples of the posterior allocation probability matrix stored in file *_mcmc_probs.out generated by resampleClusterProb.
• nIter: Number of stored MCMC samples.
• nItem: Number of items.
• nClust: Number of inferred clusters.
• RELABEL.THRESHOLD: A positive scalar. Used to determine whether the optimization in the relabeling algorithm has converged.
• PRINT: If TRUE, print intermediate values onto the screen. Used for debugging with small data sets.
• PACKAGE: Not for use.

Returns

Permuted labels for each store MCMC sample are written to file *_mcmc_perms.out, in which each row contains an inferred permutation (relabel) of labels of mixture components.

References

Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361.

Stephens, M. (2000) Dealing with label switching in mixture models. Journal of the Royal Statistical Society, Series B, 62: 795-809.

Author(s)

Audrey Q. Fu

Note

This function calls a routine written in C, where implementation of Munkres algorithm is adapted from the C code by Dariush Lotfi (June 2008; web download).

See Also

DIRECT for the complete method.

DPMCMC for the MCMC sampler under the Dirichlet-process prior.

resampleClusterProb for resampling of posterior allocation probability matrix in posterior inference.

Examples

## See example for DIRECT.