relabel {DIRECT} | R Documentation |
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")
probs.mcmc |
A |
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. |
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.
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).
Audrey Q. Fu
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.
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.
## See example for DIRECT.