build_mmm {seqHMM}

Build a Mixture Markov Model

Description

Function build_mmm is a shortcut for constructing a mixture Markov model as a restricted case of an mhmm object.

Usage

build_mmm(
  observations,
  n_clusters,
  transition_probs,
  initial_probs,
  formula = NULL,
  data = NULL,
  coefficients = NULL,
  cluster_names = NULL,
  ...
)

Arguments

observations

An stslist object (see seqdef) containing the sequences.

n_clusters

A scalar giving the number of clusters/submodels (not used if starting values for model parameters are given with initial_probs and transition_probs).

transition_probs

A list of matrices of transition probabilities for submodels of each cluster.

initial_probs

A list which contains vectors of initial state probabilities for submodels of each cluster.

formula

Optional formula of class formula for the mixture probabilities. Left side omitted.

data

A data frame containing the variables used in the formula. Ignored if no formula is provided.

coefficients

An optional k x l matrix of regression coefficients for time-constant covariates for mixture probabilities, where l is the number of clusters and k is the number of covariates. A logit-link is used for mixture probabilities. The first column is set to zero.

cluster_names

A vector of optional names for the clusters.

...

Additional arguments to simulate_transition_probs.

Value

Object of class mhmm 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.

coefficients

A matrix of parameter coefficients for covariates (covariates in rows, clusters in columns).

X

Covariate values for each subject.

cluster_names

Names for clusters.

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.

n_covariates

Number of covariates.

n_clusters

Number of clusters.

See Also

fit_model for estimating model parameters; summary.mhmm for a summary of a mixture model; separate_mhmm for organizing an mhmm object into a list of separate hmm objects; and plot.mhmm for plotting mixture models.

Examples

# Define 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
)

# Initialize the MMM
set.seed(123)
mmm_mvad <- build_mmm(
  observations = mvad_seq,
  n_clusters = 2,
  formula = ~male, data = mvad
)

## Not run: 
# Estimate model parameters
mmm_mvad <- fit_model(mmm_mvad)$model

# Plot model (both clusters in the same plot)
require(igraph)
plot(mmm_mvad,
  interactive = FALSE,
  # Modify legend position and properties
  with.legend = "right", legend.prop = 0.3, cex.legend = 1.2,
  # Define vertex layout
  layout = layout_as_star,
  # Modify edge properties
  edge.label = NA, edge.arrow.size = 0.8, edge.curved = 0.2,
  # Modify vertex label positions (initial probabilities)
  vertex.label.pos = c("left", "right", "right", "left", "left", "right")
)

# Summary of the MMM
summary(mmm_mvad)

## End(Not run)