Function summary.mhmm gives a summary of a mixture hidden Markov model.
# S3 method for mhmm
summary(
object,
parameters = FALSE,
conditional_se = TRUE,
log_space = FALSE,
...
)Transition probabilities. Only returned if parameters = TRUE.
Emission probabilities. Only returned if parameters = TRUE.
Initial state probabilities. Only returned if parameters = TRUE.
Log-likelihood.
Bayesian information criterion.
The most probable cluster according to posterior probabilities.
Coefficients of covariates.
Variance-covariance matrix of coefficients.
Prior cluster probabilities (mixing proportions) given the covariates.
Posterior cluster membership probabilities.
Cluster probabilities (columns) by the most probable cluster (rows).
Mixture hidden Markov model of class mhmm.
Whether or not to return transition, emission, and
initial probabilities. FALSE by default.
Return conditional standard errors of coefficients.
See vcov.mhmm for details. TRUE by default.
Make computations using log-space instead of scaling for greater
numerical stability at cost of decreased computational performance. Default is FALSE.
Further arguments to vcov.mhmm.
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.
build_mhmm and fit_model for building and
fitting mixture hidden Markov models; and
mhmm_biofam for information on the model used in examples.
# Loading mixture hidden Markov model (mhmm object)
# of the biofam data
data("mhmm_biofam")
# Model summary
summary(mhmm_biofam)
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