Example of a Simulated Normal Model
Examples
# Data stored in the object
plot(density(example_hmm_mcmc_normal$data), main = "")
# Priors used in simulation
example_hmm_mcmc_normal$priors
#> $prior_means
#> [1] 0.60 0.95 1.95
#>
#> $prior_sd
#> [1] 0.1564129
#>
#> $prior_T
#> [,1] [,2] [,3]
#> [1,] 0.5347611 0.4652389 0.0000000
#> [2,] 0.2121507 0.1163606 0.6714886
#> [3,] 0.0000000 0.5884313 0.4115687
#>
# Model
example_hmm_mcmc_normal
#> Model: HMM Normal
#> Type: MCMC
#> Iter: 1500
#> Warmup: 600
#> Thin: 1
#> States: 3
summary(example_hmm_mcmc_normal)
#> Estimated means:
#> mean[1] mean[2] mean[3]
#> 0.5486377 1.0052886 1.9054420
#>
#> Estimated standard deviation:
#> 0.1983137
#>
#> Estimated transition rates:
#> 1 2 3
#> 1 0.6916361 0.3083639 0.0000000
#> 2 0.3205970 0.5760693 0.1033337
#> 3 0.0000000 0.3734367 0.6265633
#>
#> Number of windows assigned to hidden states:
#> 1 2 3
#> 3698 3517 977
#>
#> Approximate Kullback-Leibler divergence between observed and estimated distributions:
#> 0.01327481
#>
#> Log Likelihood:
#> mean sd median
#> -2795.575088 1.799081 -2795.238206
#>
#> P-value of t-test for difference between means of states (stepwise):
#> 1-2 2-3
#> 0 0
#>