output: md_document: variant: markdown_github
Package DBHC is an implementation of a sequence clustering algorithm that uses a mixture of discrete-output hidden Markov models (HMMs), the Discrete Bayesian HMM Clustering (DBHC) algorithm. The algorithm uses heuristics based on the Bayesian Information Criterion (BIC) to search for the optimal number of hidden states in each HMM and the optimal number of clusters. The packages provides functions for finding clusters in discrete sequence data with the DBHC algorithm and for plotting heatmaps of the probability matrices that are estimated in the cluster models.
Below a basic example of how to use package DBHC for obtaining sequence clusters for the Swiss Household data in package TraMineR:
library(DBHC) library(TraMineR) ## Swiss Household Data data("biofam", package = "TraMineR") # Clustering algorithm new.alphabet <- c("P", "L", "M", "LM", "C", "LC", "LMC", "D") sequences <- seqdef(biofam[,10:25], alphabet = 0:7, states = new.alphabet) # Code below takes long time to run res <- hmm.clust(sequences) # Heatmaps cluster <- 1 # display heatmaps for cluster 1 transition.heatmap(res$partition[[cluster]]$transition_probs, res$partition[[cluster]]$initial_probs) emission.heatmap(res$partition[[cluster]]$emission_probs) ## A smaller example, which takes less time to run subset <- sequences[sample(1:nrow(sequences), 20, replace = FALSE),] # Clustering algorithm res <- hmm.clust(subset, K.max = 3) # Number of clusters print(res$n.clusters) # Table of cluster memberships table(res$memberships[,"cluster"]) # BIC for each number of clusters print(res$bic) # Heatmaps cluster <- 1 # display heatmaps for cluster 1 transition.heatmap(res$partition[[cluster]]$transition_probs, res$partition[[cluster]]$initial_probs) emission.heatmap(res$partition[[cluster]]$emission_probs)
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