Package 'DBHC'

Title: Sequence Clustering with Discrete-Output HMMs
Description: Provides an implementation of a mixture of hidden Markov models (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering (DBHC) algorithm. The DBHC algorithm is an HMM Clustering algorithm that finds a mixture of discrete-output HMMs while using heuristics based on Bayesian Information Criterion (BIC) to search for the optimal number of HMM states and the optimal number of clusters.
Authors: Gabriel Budel [aut, cre], Flavius Frasincar [aut]
Maintainer: Gabriel Budel <[email protected]>
License: GPL (>= 3)
Version: 0.0.3
Built: 2026-05-10 08:31:12 UTC
Source: https://github.com/gabybudel/dbhc

Help Index

Cluster Assignment

Description

Assign sequences to cluster models that give the highest sequence-to-hmm likelihood. Used in hmm.clust.

Usage

assign.clusters(partition, memberships, sequences, smoothing = 1e-04)

Arguments

partition

A list object with the partition, a mixture of HMMs. Each element in the list is an hmm object (see build_hmm).
memberships

A matrix with cluster memberships for each sequence.
sequences

An stslist object (see seqdef) of sequences with discrete observations.
smoothing

Smoothing parameter for absolute discounting in smooth.probabilities.

Value

The updated matrix with cluster memberships for each sequence.

See Also

Used in main function for the DBHC algorithm hmm.clust.

HMM BIC

Description

Compute the BIC of a single HMM given a threshold epsilon for counting parameters. Auxiliary function used in size.search.

Usage

cluster.bic(hmm, eps = 0.001)

Arguments

hmm

An hmm object (see build_hmm).
eps

A threshold epsilon for counting parameters.

Value

The BIC of hmm.

See Also

Used in size.search.

Count HMM Parameters

Description

Count the number of parameters in an HMM larger than a small number epsilon. Auxiliary function used in partition.bic and cluster.bic.

Usage

count.parameters(hmm, eps = 0.001)

Arguments

hmm

An hmm object (see build_hmm).
eps

A threshold epsilon for counting parameters.

Value

The number of parameters larger than eps.

See Also

Used in partition.bic and cluster.bic.

Heatmap Emission Probabilities

Description

Plots a heatmap of an HMM's emission probabilities.

Usage

emission.heatmap(emission, base_size = 10)

Arguments

emission

A matrix with emission probabilities (see also build_hmm).
base_size

Numerical, a size parameter for the plots made using ggplot2 (see theme), default = 10.

See Also

See hmm.clust for an example.

DBHC Algorithm

Description

Implementation of the DBHC algorithm, an HMM clustering algorithm that finds a mixture of discrete-output HMMs. The algorithm uses heuristics based on BIC to search for the optimal number of hidden states in each HMM and the optimal number of clusters.

Usage

hmm.clust(
  sequences,
  id = NULL,
  smoothing = 1e-04,
  eps = 0.001,
  init.size = 2,
  alphabet = NULL,
  K.max = NULL,
  log_space = FALSE,
  print = FALSE,
  seed.size = 3
)

Arguments

sequences

An stslist object (see seqdef) of sequences with discrete observations or a data.frame.
id

A vector with ids that identify the sequences in sequences.
smoothing

Smoothing parameter for absolute discounting in smooth.probabilities.
eps

A threshold epsilon for counting parameters in count.parameters.
init.size

The number of HMM states in an initial HMM.
alphabet

The alphabet of output labels, if not provided alphabet is taken from stslist object (see seqdef).
K.max

Maximum number of clusters, if not provided algorithm searches for the optimal number itself.
log_space

Logical, parameter provided to fit_model for whether to use optimization in log space or not.
print

Logical, whether to print intermediate steps or not.
seed.size

Seed size, the number of sequences to be selected for a seed

Value

A list with components:

sequences

An stslist object of sequences with discrete observations.

id

A vector with ids that identify the sequences in sequences.

cluster

A vector with found cluster memberships for the sequences.

partition

A list object with the partition, a mixture of HMMs. Each element in the list is an hmm object.

memberships

A matrix with cluster memberships for each sequence.

n.clusters

Numerical, the found number of clusters.

sizes

A vector with the number of HMM states for each cluster model.

bic

A vector with the BICs for each cluster model.

Examples

## Simulated data
library(seqHMM)
output.labels <-  c("H", "T")

# HMM 1
states.1 <- c("A", "B", "C")
transitions.1 <- matrix(c(0.8,0.1,0.1,0.1,0.8,0.1,0.1,0.1,0.8), nrow = 3)
rownames(transitions.1) <- states.1
colnames(transitions.1) <- states.1
emissions.1 <- matrix(c(0.5,0.75,0.25,0.5,0.25,0.75), nrow = 3)
rownames(emissions.1) <- states.1
colnames(emissions.1) <- output.labels
initials.1 <- c(1/3,1/3,1/3)

# HMM 2
states.2 <- c("A", "B")
transitions.2 <- matrix(c(0.75,0.25,0.25,0.75), nrow = 2)
rownames(transitions.2) <- states.2
colnames(transitions.2) <- states.2
emissions.2 <- matrix(c(0.8,0.6,0.2,0.4), nrow = 2)
rownames(emissions.2) <- states.2
colnames(emissions.2) <- output.labels
initials.2 <- c(0.5,0.5)

# Simulate
hmm.sim.1 <- simulate_hmm(n_sequences = 100,
                          initial_probs = initials.1,
                          transition_probs = transitions.1,
                          emission_probs = emissions.1,
                          sequence_length = 25)
hmm.sim.2 <- simulate_hmm(n_sequences = 100,
                          initial_probs = initials.2,
                          transition_probs = transitions.2,
                          emission_probs = emissions.2,
                          sequence_length = 25)
sequences <- rbind(hmm.sim.1$observations, hmm.sim.2$observations)
n <- nrow(sequences)

# Clustering algorithm
id <- paste0("K-", 1:n)
rownames(sequences) <- id
sequences <- sequences[sample(1:n, n),]

res <- hmm.clust(sequences, id = rownames(sequences))



#############################################################################

## 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)
## Not 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)

## End(Not run)


## A smaller example, which takes less time to run

subset <- sequences[sample(1:nrow(sequences), 20, replace = FALSE),]

# Clustering algorithm, limiting number of clusters to 2
res <- hmm.clust(subset, K.max = 2)

# 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)

Get HMM Log Likelihood

Description

Get the log likelihood of an HMM object and check if it is feasible (i.e., contains no illegal emissions). Auxiliary function used in partition.bic.

Usage

model.ll(hmm)

Arguments

hmm

An hmm object (see build_hmm).

Value

The log likelihood of the hmm object, print warning if model is infeasible (i.e., if the log likelihood is evaluated for a sequence that contains emissions that are assigned probability 0 in the hmm object).

See Also

Used in partition.bic.

Partition BIC

Description

Compute the BIC of a partition given a threshold epsilon for counting parameters. Auxiliary function used in hmm.clust.

Usage

partition.bic(partition, eps = 0.001)

Arguments

partition

A list object with the partition of HMMs, a mixture of HMMs.
eps

A threshold epsilon for counting parameters in count.parameters.

Value

The BIC of the partition.

See Also

Used in main function for the DBHC algorithm hmm.clust.

Seed Selection Procedure

Description

Seed selection procedure of the DBHC algorithm, also invokes size search algorithm for seed in size.search. Used in hmm.clust.

Usage

select.seeds(
  sequences,
  log_space = FALSE,
  K,
  seed.size = 3,
  init.size = 2,
  print = FALSE,
  smoothing = 1e-04
)

Arguments

sequences

An stslist object (see seqdef) of sequences with discrete observations.
log_space

Logical, parameter provided to fit_model for whether to use optimization in log space or not.
K

The number of seeds to select, equal to the number of clusters in a partition.
seed.size

Seed size, the number of sequences to be selected for a seed.
init.size

The number of HMM states in an initial HMM.
print

Logical, whether to print intermediate steps or not.
smoothing

Smoothing parameter for absolute discounting in smooth.probabilities.

Value

A partition as a list object with HMMs for the selected seeds.

See Also

Used in main function for the DBHC algorithm hmm.clust.

Sequence-to-HMM Likelihood

Description

Compute the sequence-to-HMM likelihood of an HMM evaluated for a single sequence and check if the sequence contains emissions that are not possible according to the HMM. Auxiliary function used in select.seeds and assign.clusters.

Usage

seq2hmm.ll(hmm)

Arguments

hmm

An hmm object (see build_hmm) containing a single sequence.

Value

The log likelihood of the sequence contained in hmm, value will be set to minus infinity if the sequence contains illegal emissions.

See Also

Used in select.seeds and assign.clusters.

Size Search Algorithm

Description

The size search algorithm finds the optimal number of HMM states for a set of sequences and returns both the optimal hmm object and the corresponding number of hidden states. Used in select.seeds.

Usage

size.search(sequences, log_space = FALSE, print = FALSE)

Arguments

sequences

An stslist object (see seqdef) of sequences with discrete observations.
log_space

Logical, parameter provided to fit_model for whether to use optimization in log space or not.
print

Logical, whether to print intermediate steps or not.

Value

A list with the optimal number of HMM states and the optimal hmm object.

See Also

Used in the DBHC seed selection procedure in select.seeds.

Smooth HMM Parameters

Description

Smooth the parameters of an HMM using absolute discounting given a threshold epsilon. Auxiliary function used in select.seeds, assign.clusters, and hmm.clust.

Usage

smooth.hmm(hmm, smoothing = 1e-04)

Arguments

hmm

A raw hmm object (see build_hmm).
smoothing

Smoothing parameter for absolute discounting in smooth.probabilities.

Value

An hmm object with smoothed probabilities.

See Also

Used in select.seeds, assign.clusters, and main function for the DBHC algorithm hmm.clust.

Smooth Probabilities

Description

Smooth a vector of probabilities using absolute discounting. Auxiliary function used in smooth.hmm.

Usage

smooth.probabilities(probs, smoothing = 1e-04)

Arguments

probs

A vector of raw probabilities.
smoothing

Smoothing parameter for absolute discounting.

Value

A vector of smoothed probabilities.

See Also

Used in smooth.hmm.

Heatmap Transition Probabilities

Description

Plots a heatmap of an HMM's initial and transition probabilities.

Usage

transition.heatmap(transition, initial = NULL, base_size = 10)

Arguments

transition

A matrix with transition probabilities (see also build_hmm).
initial

An (optional) vector of initial probabilities.
base_size

Numerical, a size parameter for the plots made using ggplot2 (see theme), default = 10.

See Also

See hmm.clust for an example.