hmm_mvad: Hidden Markov model for the mvad data

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

Hidden Markov model for the mvad data

Description

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

Format

A hidden Markov model of class hmm; unrestricted model with six hidden states.

Details

Model 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 matrix
emiss <- matrix(
  c(0.05, 0.05, 0.05, 0.05, 0.75, 0.05, # SC
    0.05, 0.75, 0.05, 0.05, 0.05, 0.05, # FE
    0.05, 0.05, 0.05, 0.4,  0.05, 0.4,  # JL, TR
    0.05, 0.05, 0.75, 0.05, 0.05, 0.05, # HE
    0.75, 0.05, 0.05, 0.05, 0.05, 0.05),# EM
  nrow = 5, ncol = 6, byrow = TRUE)

# Starting values for the transition matrix
trans <- matrix(0.025, 5, 5)
diag(trans) <- 0.9

# Starting values for initial state probabilities
initial_probs <- c(0.2, 0.2, 0.2, 0.2, 0.2)

# Building a hidden Markov model
init_hmm_mvad <- build_hmm(observations = mvad_seq,
  transition_probs = trans, emission_probs = emiss,
  initial_probs = initial_probs)

set.seed(21)
fit_hmm_mvad <- fit_model(init_hmm_mvad, control_em = list(restart = list(times = 100)))
hmm_mvad <- fit_hmm_mvad$model

See Also

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

Examples

data("hmm_mvad")

# Plotting the model
plot(hmm_mvad)

helske/seqHMM documentation built on July 6, 2023, 6:45 a.m.