Plot Diagnostics for hmm_mcmc_normal Objects — plot.hmm_mcmc

This function creates a variety of diagnostic plots that can be useful when conducting Markov Chain Monte Carlo (MCMC) simulation of a normal hidden Markov model (HMM). These plots will help to assess convergence, fit, and performance of the MCMC simulation

Usage

# S3 method for class 'hmm_mcmc_normal'
plot(
  x,
  simulation = FALSE,
  true_means = NULL,
  true_sd = NULL,
  true_mat_T = NULL,
  true_states = NULL,
  show_titles = TRUE,
  ...
)

Arguments

x: (hmm_mcmc_normal) HMM MCMC normal object
simulation: (logical) optional parameter; default is simulation=FALSE, so the input data was empirical. If the input data was simulated, it must be set simulation=TRUE.
true_means: (numeric) optional parameter; true means. To be used if simulation=TRUE
true_sd: (numeric) optional parameter; true standard deviation. To be used if simulation=TRUE
true_mat_T: (matrix) optional parameter; true transition matrix. To be used if simulation=TRUE
true_states: (integer) optional parameter; true states. To be used if simulation=TRUE
show_titles: (logical) optional parameter; if TRUE then titles are shown for all graphs. By default, TRUE
...: not used

Value

Several diagnostic plots that can be used to evaluate the MCMC simulation of the normal HMM

Examples

# \donttest{
plot(example_hmm_mcmc_normal)










# }