A hidden Markov model (MMM) fitted for the mvad data.
A hidden Markov model of class hmm;
unrestricted model with six hidden states.
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
Examples of building and fitting HMMs in build_hmm and
fit_model; and mvad for more information on the data.
data("hmm_mvad")
# Plotting the model
plot(hmm_mvad)
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