hidden_paths: Most Probable Paths of Hidden States

In seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Most Probable Paths of Hidden States

Description

Function hidden_paths computes the most probable path of hidden states of a (mixture) hidden Markov model given the observed sequences.

Usage

hidden_paths(model, respect_void = TRUE)

Arguments

model	A hidden Markov model of class hmm or a mixture HMM of class mhmm.
respect_void	If TRUE (default), states at the time points corresponding to TraMineR's void in the observed sequences are set to void in the hidden state sequences as well.

Value

The most probable paths of hidden states as an stslist object (see seqdef). The log-probability is included as an attribute log_prob.

See Also

hmm_biofam for information on the model used in the example; and seqIplot, ssplot, or mssplot for plotting hidden paths.

Examples

# Load a pre-defined HMM
data("hmm_biofam")

# Compute the most probable hidden state paths given the data and the model
mpp <- hidden_paths(hmm_biofam)

# Plot hidden paths for the first 100 individuals
ssplot(mpp, type = "I", tlim = 1:100)

# Because the model structure is so sparse that the posterior probabilities are
# mostly peaked to single state at each time point, the joint probability of
# observations and most probable paths of hidden states is almost identical to
# log-likelihood:

sum(attr(mpp, "log_prob"))
logLik(hmm_biofam)

seqHMM documentation built on July 9, 2023, 6:35 p.m.