simulate_hmm: Simulate hidden Markov models

In seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Simulate hidden Markov models

Description

Simulate sequences of observed and hidden states given parameters of a hidden Markov model.

Usage

simulate_hmm(
  n_sequences,
  initial_probs,
  transition_probs,
  emission_probs,
  sequence_length
)

Arguments

n_sequences	The number of sequences to simulate.
initial_probs	A vector of initial state probabilities.
transition_probs	A matrix of transition probabilities.
emission_probs	A matrix of emission probabilities or a list of such objects (one for each channel).
sequence_length	Length for simulated sequences.

Value

A list of state sequence objects of class stslist.

See Also

build_hmm() and fit_model() for building and fitting hidden Markov models; stacked_sequence_plot() for plotting multiple sequence data sets; seqdef() for more information on state sequence objects; and simulate_mhmm() for simulating mixture hidden Markov models.

Examples

# Parameters for the HMM
emission_probs <- matrix(c(0.5, 0.2, 0.5, 0.8), 2, 2)
transition_probs <- matrix(c(5 / 6, 1 / 6, 1 / 6, 5 / 6), 2, 2)
initial_probs <- c(1, 0)

# Setting the seed for simulation
set.seed(1)

# Simulating sequences
sim <- simulate_hmm(
  n_sequences = 10, initial_probs = initial_probs,
  transition_probs = transition_probs,
  emission_probs = emission_probs,
  sequence_length = 20
)

stacked_sequence_plot(sim, sort_by = "mds", type = "i")

seqHMM documentation built on June 8, 2025, 10:16 a.m.