data-raw/hmm_mvad.R

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

library(seqHMM)
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, 15:86, alphabet = mvad_alphabet,
                   states = mvad_scodes, labels = mvad_labels, xtstep = 6,
                   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
usethis::use_data(hmm_mvad)