build_mm: Build a Markov Model

Description

Function build_mm builds and automatically estimates a Markov model. It is also a shortcut for constructing a Markov model as a restricted case of an hmm object.

Usage

build_mm(observations)

Arguments

observations

An stslist object (see seqdef) containing the sequences.

Value

Object of class hmm with following elements:

observations: State sequence object or a list of such containing the data.
transition_probs: A matrix of transition probabilities.
emission_probs: A matrix or a list of matrices of emission probabilities.
initial_probs: A vector of initial probabilities.
state_names: Names for hidden states.
symbol_names: Names for observed states.
channel_names: Names for channels of sequence data.
length_of_sequences: (Maximum) length of sequences.
n_sequences: Number of sequences.
n_symbols: Number of observed states (in each channel).
n_states: Number of hidden states.
n_channels: Number of channels.

Details

Unlike the other build functions in seqHMM, the build_mm function automatically estimates the model parameters. As initial and transition probabilities can be directly estimated from the observed initial state probabilities and transition counts, there is no need for starting values or further estimation with the fit_model function.

Examples

Run this code

# NOT RUN {
# Construct sequence data
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)

# Define a color palette for the sequence data
attr(mvad_seq, "cpal") <- colorpalette[[6]]

# Estimate the Markov model
mm_mvad <- build_mm(observations = mvad_seq)

# }