mhmm_mvad {seqHMM} | R Documentation |
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)
}
[Package seqHMM version 1.2.6 Index]