simulate_mhmm: Simulate Mixture Hidden Markov Models

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

Simulate Mixture Hidden Markov Models

Description

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

Usage

simulate_mhmm(
  n_sequences,
  initial_probs,
  transition_probs,
  emission_probs,
  sequence_length,
  formula = NULL,
  data = NULL,
  coefficients = NULL
)

Arguments

n_sequences	The number of sequences to simulate.
initial_probs	A list containing vectors of initial state probabilities for the submodel of each cluster.
transition_probs	A list of matrices of transition probabilities for the submodel of each cluster.
emission_probs	A list which contains matrices of emission probabilities or a list of such objects (one for each channel) for the submodel of each cluster. Note that the matrices must have dimensions s x m where s is the number of hidden states and m is the number of unique symbols (observed states) in the data.
sequence_length	The length of the simulated sequences.
formula	Covariates as an object of class formula(), left side omitted.
data	An optional data frame, a list or an environment containing the variables in the model. If not found in data, the variables are taken from environment(formula).
coefficients	An optional k x l matrix of regression coefficients for time-constant covariates for mixture probabilities, where l is the number of clusters and k is the number of covariates. A logit-link is used for mixture probabilities. The first column is set to zero.

Value

A list of state sequence objects of class stslist.

See Also

build_mhmm() and fit_model() for building and fitting mixture hidden Markov models.

Examples

emission_probs_1 <- matrix(c(0.75, 0.05, 0.25, 0.95), 2, 2)
emission_probs_2 <- matrix(c(0.1, 0.8, 0.9, 0.2), 2, 2)
colnames(emission_probs_1) <- colnames(emission_probs_2) <-
  c("heads", "tails")

transition_probs_1 <- matrix(c(9, 0.1, 1, 9.9) / 10, 2, 2)
transition_probs_2 <- matrix(c(35, 1, 1, 35) / 36, 2, 2)
rownames(emission_probs_1) <- rownames(transition_probs_1) <-
  colnames(transition_probs_1) <- c("coin 1", "coin 2")
rownames(emission_probs_2) <- rownames(transition_probs_2) <-
  colnames(transition_probs_2) <- c("coin 3", "coin 4")

initial_probs_1 <- c(1, 0)
initial_probs_2 <- c(1, 0)

n <- 30
set.seed(123)
covariate_1 <- runif(n)
covariate_2 <- sample(c("A", "B"),
  size = n, replace = TRUE,
  prob = c(0.3, 0.7)
)
dataf <- data.frame(covariate_1, covariate_2)

coefs <- cbind(cluster_1 = c(0, 0, 0), cluster_2 = c(-1.5, 3, -0.7))
rownames(coefs) <- c("(Intercept)", "covariate_1", "covariate_2B")

sim <- simulate_mhmm(
  n = n, initial_probs = list(initial_probs_1, initial_probs_2),
  transition_probs = list(transition_probs_1, transition_probs_2),
  emission_probs = list(emission_probs_1, emission_probs_2),
  sequence_length = 20, formula = ~ covariate_1 + covariate_2,
  data = dataf, coefficients = coefs
)

stacked_sequence_plot(sim, 
  sort_by = "start", sort_channel = "states", type = "i"
)

hmm <- build_mhmm(sim$observations,
  initial_probs = list(initial_probs_1, initial_probs_2),
  transition_probs = list(transition_probs_1, transition_probs_2),
  emission_probs = list(emission_probs_1, emission_probs_2),
  formula = ~ covariate_1 + covariate_2,
  data = dataf
)

fit <- fit_model(hmm)
fit$model

paths <- hidden_paths(fit$model, as_stslist = TRUE)

stacked_sequence_plot(
  list(
    "estimated paths" = paths, 
    "true (simulated)" = sim$states
  ),
  sort_by = "start",
  sort_channel = "true (simulated)",
  type = "i"
)

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