coclusteringMatrix {clusternomics} | R Documentation |
Compute the posterior co-clustering matrix from global cluster assignments.
coclusteringMatrix(assignments)
assignments |
Matrix of cluster assignments, where each row corresponds to cluster assignments sampled in one MCMC iteration |
Posterior co-clustering matrix, where element [i,j]
represents
the posterior probability that data points i
and j
belong
to the same cluster.
# Generate simple test dataset groupCounts <- c(50, 10, 40, 60) means <- c(-1.5,1.5) testData <- generateTestData_2D(groupCounts, means) datasets <- testData$data # Fit the model # 1. specify number of clusters clusterCounts <- list(global=10, context=c(3,3)) # 2. Run inference # Number of iterations is just for demonstration purposes, use # a larger number of iterations in practice! results <- contextCluster(datasets, clusterCounts, maxIter = 10, burnin = 5, lag = 1, dataDistributions = 'diagNormal', verbose = TRUE) # Extract only the sampled global assignments samples <- results$samples clusters <- plyr::laply(1:length(samples), function(i) samples[[i]]$Global) coclusteringMatrix(clusters)