seqHMM (version 2.1.0)

summary.mhmm: Summary method for mixture hidden Markov models

Description

Function summary.mhmm gives a summary of a mixture hidden Markov model.

Usage

# S3 method for mhmm
summary(object, parameters = FALSE, conditional_se = TRUE, ...)

Value

• transition_probs
  Transition probabilities. Only returned if parameters = TRUE.

• emission_probs
  Emission probabilities. Only returned if parameters = TRUE.

• initial_probs
  Initial state probabilities. Only returned if parameters = TRUE.

• logLik
  Log-likelihood.

• BIC
  Bayesian information criterion.

• most_probable_cluster
  The most probable cluster according to posterior probabilities.

• coefficients
  Coefficients of covariates.

• vcov
  Variance-covariance matrix of coefficients.

• prior_cluster_probabilities
  Prior cluster probabilities (mixing proportions) given the covariates.

• posterior_cluster_probabilities
  Posterior cluster membership probabilities.

• classification_table
  Cluster probabilities (columns) by the most probable cluster (rows).

Arguments

object

Mixture hidden Markov model of class mhmm.

parameters

Whether or not to return transition, emission, and initial probabilities. FALSE by default.

conditional_se

Return conditional standard errors of coefficients. See vcov.mhmm() for details. TRUE by default.

...

Further arguments to vcov.mhmm().

Details

The summary.mhmm function computes features from a mixture hidden Markov model and stores them as a list. A print method prints summaries of these: log-likelihood and BIC, coefficients and standard errors of covariates, means of prior cluster probabilities, and information on most probable clusters.

See Also

build_mhmm() and fit_model() for building and fitting mixture hidden Markov models; and mhmm_biofam() for information on the model used in examples.

Examples

# Loading mixture hidden Markov model (mhmm object)
# of the biofam data
data("mhmm_biofam")

# Model summary
summary(mhmm_biofam)