Plot Diagnostics for hmm_mcmc_gamma_poisson Objects

This function creates a variety of diagnostic plots that can be useful when conducting Markov Chain Monte Carlo (MCMC) simulation of a gamma-poisson 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_gamma_poisson'
plot(
  x,
  simulation = FALSE,
  true_betas = NULL,
  true_alpha = NULL,
  true_mat_T = NULL,
  true_states = NULL,
  show_titles = TRUE,
  log_statesplot = FALSE,
  ...
)

Arguments

x

(hmm_mcmc_gamma_poisson) HMM MCMC gamma-poisson object

simulation

(logical); default is simulation=FALSE, so the input data was empirical. If the input data was simulated, it must be set simulation=TRUE.

true_betas

(numeric) true betas. To be used if simulation=TRUE

true_alpha

(numeric) true alpha. 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) if TRUE then titles are shown for all graphs. By default, TRUE

log_statesplot

(logical) if TRUE then log-statesplots are shown. By default, FALSE

...

not used

Value

Several diagnostic plots that can be used to evaluate the MCMC simulation of the gamma-poisson HMM

Examples

# \donttest{
plot(example_hmm_mcmc_gamma_poisson)
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2












# }