mhmm_mvad: Mixture hidden Markov model for the mvad data

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

Mixture hidden Markov model for the mvad data

Description

A mixture hidden Markov model (MHMM) fitted for the mvad data.

Format

A mixture hidden Markov model of class mhmm: two clusters including 3 and 4 hidden states. No covariates.

Details

The model is loaded by calling data(mhmm_mvad). It was created with the following code:

data("mvad", package = "TraMineR")

mvad_alphabet <-
  c("employment", "FE", "HE", "joblessness", "school", "training")
mvad_labels <- c("employment", "further education", "higher education",
  "joblessness", "school", "training")
mvad_scodes <- c("EM", "FE", "HE", "JL", "SC", "TR")
mvad_seq <- seqdef(mvad, 17:86, alphabet = mvad_alphabet,
  states = mvad_scodes, labels = mvad_labels, xtstep = 6)

attr(mvad_seq, "cpal") <- colorpalette[[6]]

# Starting values for the emission matrices
emiss_1 <- matrix(
  c(0.01, 0.01, 0.01, 0.01, 0.01, 0.95,
    0.95, 0.01, 0.01, 0.01, 0.01, 0.01,
    0.01, 0.01, 0.01, 0.95, 0.01, 0.01),
  nrow = 3, ncol = 6, byrow = TRUE)

emiss_2 <- matrix(
  c(0.01, 0.01, 0.01, 0.06, 0.90, 0.01,
    0.01, 0.95, 0.01, 0.01, 0.01, 0.01,
    0.01, 0.01, 0.95, 0.01, 0.01, 0.01,
    0.95, 0.01, 0.01, 0.01, 0.01, 0.01),
  nrow = 4, ncol = 6, byrow = TRUE)

# Starting values for the transition matrix

trans_1 <-  matrix(
  c(0.95, 0.03, 0.02,
    0.01, 0.98, 0.01,
    0.01, 0.01, 0.98),
  nrow = 3, ncol = 3, byrow = TRUE)

trans_2 <-  matrix(
  c(0.97, 0.01, 0.01, 0.01,
    0.01, 0.97, 0.01, 0.01,
    0.01, 0.01, 0.97, 0.01,
    0.01, 0.01, 0.01, 0.97),
  nrow = 4, ncol = 4, byrow = TRUE)

# Starting values for initial state probabilities
initial_probs_1 <- c(0.5, 0.25, 0.25)
initial_probs_2 <- c(0.4, 0.4, 0.1, 0.1)

# Building a hidden Markov model with starting values
init_mhmm_mvad <- build_mhmm(observations = mvad_seq,
  transition_probs = list(trans_1, trans_2),
  emission_probs = list(emiss_1, emiss_2),
  initial_probs = list(initial_probs_1, initial_probs_2))

# Fit the model
set.seed(123)
mhmm_mvad <- fit_model(init_mhmm_mvad, control_em = list(restart = list(times = 25)))$model

See Also

Examples of building and fitting MHMMs in build_mhmm and fit_model; and mvad for more information on the data.

Examples

data("mhmm_mvad")

summary(mhmm_mvad)

if (interactive()) {
  # Plotting the model for each cluster (change with Enter)
  plot(mhmm_mvad)
}

seqHMM documentation built on July 9, 2023, 6:35 p.m.