Build a Mixture Markov Model
Function build_mmm is a shortcut for constructing a mixture Markov
model as a restricted case of an mhmm object.
build_mmm(observations, n_clusters, transition_probs, initial_probs, formula, data, coefficients, cluster_names = NULL, ...)
observations |
An |
n_clusters |
A scalar giving the number of clusters/submodels
(not used if starting values for model parameters are given with
|
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 |
Covariates as an object of class |
data |
An optional data frame, list or environment containing the variables
in the model. If not found in data, the variables are taken from
|
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 |
Object of class mhmm with following elements:
observationsState sequence object or a list of such containing the data.
transition_probsA matrix of transition probabilities.
emission_probsA matrix or a list of matrices of emission probabilities.
initial_probsA vector of initial probabilities.
coefficientsA matrix of parameter coefficients for covariates (covariates in rows, clusters in columns).
XCovariate values for each subject.
cluster_namesNames for clusters.
state_namesNames for hidden states.
symbol_namesNames for observed states.
channel_namesNames for channels of sequence data
length_of_sequences(Maximum) length of sequences.
n_sequencesNumber of sequences.
n_symbolsNumber of observed states (in each channel).
n_statesNumber of hidden states.
n_channelsNumber of channels.
n_covariatesNumber of covariates.
n_clustersNumber of clusters.
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.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.