R/find_regimes.R
find_regimes.RdFit multiple models with differing numbers of regimes to trend data
find_regimes(
y,
sds = NULL,
min_regimes = 1,
max_regimes = 3,
iter = 2000,
thin = 1,
chains = 1,
...
)Data, time series or trend from fitted DFA model.
Optional time series of standard deviations of estimates. If passed in, residual variance not estimated.
Smallest of regimes to evaluate, defaults to 1.
Biggest of regimes to evaluate, defaults to 3.
MCMC iterations, defaults to 2000.
MCMC thinning rate, defaults to 1.
MCMC chains; defaults to 1 (note that running multiple chains may result in a "label switching" problem where the regimes are identified with different IDs across chains).
Other parameters to pass to rstan::sampling().
data(Nile)
find_regimes(log(Nile), iter = 50, chains = 1, max_regimes = 2)
#>
#> SAMPLING FOR MODEL 'regime_1' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 3
#> Chain 1: adapt_window = 20
#> Chain 1: term_buffer = 2
#> Chain 1:
#> Chain 1: Iteration: 1 / 50 [ 2%] (Warmup)
#> Chain 1: Iteration: 5 / 50 [ 10%] (Warmup)
#> Chain 1: Iteration: 10 / 50 [ 20%] (Warmup)
#> Chain 1: Iteration: 15 / 50 [ 30%] (Warmup)
#> Chain 1: Iteration: 20 / 50 [ 40%] (Warmup)
#> Chain 1: Iteration: 25 / 50 [ 50%] (Warmup)
#> Chain 1: Iteration: 26 / 50 [ 52%] (Sampling)
#> Chain 1: Iteration: 30 / 50 [ 60%] (Sampling)
#> Chain 1: Iteration: 35 / 50 [ 70%] (Sampling)
#> Chain 1: Iteration: 40 / 50 [ 80%] (Sampling)
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.001 seconds (Warm-up)
#> Chain 1: 0.001 seconds (Sampling)
#> Chain 1: 0.002 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is 1.43, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#>
#> SAMPLING FOR MODEL 'hmm_gaussian' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000123 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 3
#> Chain 1: adapt_window = 20
#> Chain 1: term_buffer = 2
#> Chain 1:
#> Chain 1: Iteration: 1 / 50 [ 2%] (Warmup)
#> Chain 1: Iteration: 5 / 50 [ 10%] (Warmup)
#> Chain 1: Iteration: 10 / 50 [ 20%] (Warmup)
#> Chain 1: Iteration: 15 / 50 [ 30%] (Warmup)
#> Chain 1: Iteration: 20 / 50 [ 40%] (Warmup)
#> Chain 1: Iteration: 25 / 50 [ 50%] (Warmup)
#> Chain 1: Iteration: 26 / 50 [ 52%] (Sampling)
#> Chain 1: Iteration: 30 / 50 [ 60%] (Sampling)
#> Chain 1: Iteration: 35 / 50 [ 70%] (Sampling)
#> Chain 1: Iteration: 40 / 50 [ 80%] (Sampling)
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.52 seconds (Warm-up)
#> Chain 1: 0.398 seconds (Sampling)
#> Chain 1: 0.918 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> $table
#> regimes looic
#> 1 1 -49.71780
#> 2 2 18.40809
#>
#> $best_model
#> $best_model$model
#> Inference for Stan model: regime_1.
#> 1 chains, each with iter=50; warmup=25; thin=1;
#> post-warmup draws per chain=25, total post-warmup draws=25.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> mu_k 6.81 0.00 0.01 6.79 6.80 6.81 6.82 6.84 25 0.97
#> sigma_k 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[1] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[2] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[3] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[4] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[5] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[6] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[7] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[8] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[9] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[10] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[11] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[12] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[13] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[14] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[15] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[16] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[17] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[18] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[19] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[20] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[21] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[22] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[23] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[24] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[25] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[26] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[27] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[28] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[29] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[30] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[31] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[32] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[33] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[34] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[35] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[36] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[37] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[38] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[39] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[40] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[41] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[42] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[43] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[44] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[45] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[46] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[47] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[48] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[49] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[50] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[51] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[52] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[53] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[54] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[55] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[56] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[57] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[58] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[59] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[60] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[61] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[62] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[63] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[64] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[65] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[66] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[67] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[68] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[69] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[70] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[71] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[72] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[73] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[74] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[75] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[76] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[77] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[78] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[79] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[80] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[81] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[82] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[83] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[84] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[85] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[86] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[87] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[88] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[89] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[90] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[91] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[92] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[93] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[94] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[95] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[96] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[97] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[98] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[99] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> sigmas[100] 0.19 0.01 0.01 0.17 0.18 0.19 0.20 0.21 7 1.16
#> log_lik[1] 0.10 0.02 0.10 -0.07 0.06 0.11 0.16 0.30 18 1.01
#> log_lik[2] -0.13 0.04 0.12 -0.37 -0.17 -0.10 -0.06 0.09 12 1.07
#> log_lik[3] 0.68 0.02 0.08 0.57 0.62 0.66 0.75 0.81 11 1.07
#> log_lik[4] -0.44 0.06 0.16 -0.79 -0.47 -0.41 -0.36 -0.21 9 1.12
#> log_lik[5] -0.13 0.04 0.12 -0.37 -0.17 -0.10 -0.06 0.09 12 1.07
#> log_lik[6] -0.13 0.04 0.12 -0.37 -0.17 -0.10 -0.06 0.09 12 1.07
#> log_lik[7] 0.58 0.02 0.07 0.48 0.55 0.57 0.61 0.72 9 1.10
#> log_lik[8] -0.58 0.06 0.18 -0.97 -0.62 -0.56 -0.49 -0.34 8 1.14
#> log_lik[9] -1.67 0.13 0.35 -2.39 -1.78 -1.55 -1.46 -1.27 7 1.18
#> log_lik[10] -0.01 0.03 0.11 -0.21 -0.05 0.01 0.06 0.20 14 1.04
#> log_lik[11] 0.61 0.02 0.07 0.51 0.55 0.59 0.65 0.75 19 1.02
#> log_lik[12] 0.72 0.03 0.08 0.61 0.66 0.71 0.78 0.85 9 1.11
#> log_lik[13] 0.15 0.02 0.09 0.00 0.11 0.16 0.21 0.35 19 1.00
#> log_lik[14] 0.61 0.02 0.07 0.51 0.55 0.59 0.65 0.75 19 1.03
#> log_lik[15] 0.54 0.02 0.07 0.44 0.49 0.51 0.57 0.68 22 0.99
#> log_lik[16] 0.69 0.02 0.08 0.58 0.63 0.67 0.75 0.82 11 1.08
#> log_lik[17] -0.25 0.04 0.14 -0.53 -0.28 -0.21 -0.17 -0.02 10 1.09
#> log_lik[18] 0.52 0.02 0.07 0.41 0.50 0.51 0.54 0.66 11 1.06
#> log_lik[19] 0.69 0.02 0.08 0.58 0.63 0.67 0.76 0.82 11 1.08
#> log_lik[20] -0.01 0.03 0.11 -0.21 -0.05 0.01 0.06 0.20 14 1.04
#> log_lik[21] 0.20 0.02 0.09 0.07 0.15 0.20 0.26 0.39 21 0.98
#> log_lik[22] -0.44 0.06 0.16 -0.79 -0.47 -0.41 -0.36 -0.21 9 1.12
#> log_lik[23] -0.07 0.03 0.11 -0.29 -0.12 -0.04 0.00 0.15 13 1.06
#> log_lik[24] -0.72 0.07 0.20 -1.16 -0.77 -0.68 -0.62 -0.46 8 1.15
#> log_lik[25] -0.80 0.08 0.22 -1.25 -0.85 -0.76 -0.69 -0.52 8 1.15
#> log_lik[26] -0.51 0.06 0.17 -0.88 -0.54 -0.48 -0.42 -0.27 9 1.13
#> log_lik[27] 0.50 0.01 0.07 0.41 0.46 0.48 0.54 0.65 24 0.98
#> log_lik[28] 0.20 0.02 0.09 0.07 0.15 0.20 0.26 0.39 21 0.98
#> log_lik[29] 0.40 0.02 0.07 0.24 0.37 0.39 0.43 0.52 18 0.99
#> log_lik[30] 0.66 0.03 0.07 0.57 0.61 0.65 0.69 0.80 8 1.14
#> log_lik[31] 0.72 0.03 0.08 0.62 0.67 0.71 0.76 0.86 7 1.15
#> log_lik[32] -0.24 0.03 0.15 -0.57 -0.30 -0.20 -0.14 -0.06 23 0.99
#> log_lik[33] 0.72 0.03 0.08 0.60 0.66 0.70 0.78 0.85 9 1.10
#> log_lik[34] 0.64 0.02 0.07 0.55 0.59 0.62 0.67 0.78 8 1.14
#> log_lik[35] -0.17 0.03 0.14 -0.48 -0.22 -0.12 -0.08 0.00 24 0.98
#> log_lik[36] 0.73 0.03 0.08 0.62 0.68 0.73 0.78 0.87 8 1.13
#> log_lik[37] -0.27 0.03 0.16 -0.60 -0.32 -0.22 -0.16 -0.08 23 0.99
#> log_lik[38] 0.54 0.02 0.07 0.44 0.49 0.51 0.57 0.68 22 0.99
#> log_lik[39] 0.43 0.01 0.07 0.33 0.38 0.40 0.47 0.58 26 0.96
#> log_lik[40] 0.67 0.02 0.08 0.56 0.61 0.65 0.73 0.80 12 1.06
#> log_lik[41] 0.64 0.02 0.07 0.55 0.59 0.62 0.67 0.78 8 1.13
#> log_lik[42] 0.06 0.02 0.11 -0.19 0.02 0.10 0.13 0.19 25 0.96
#> log_lik[43] -5.81 0.36 1.07 -7.69 -6.66 -5.45 -5.01 -4.42 9 1.11
#> log_lik[44] 0.61 0.02 0.07 0.52 0.58 0.60 0.65 0.75 8 1.12
#> log_lik[45] -0.16 0.03 0.14 -0.46 -0.21 -0.11 -0.07 0.01 24 0.98
#> log_lik[46] 0.10 0.02 0.10 -0.07 0.06 0.11 0.16 0.30 18 1.01
#> log_lik[47] 0.20 0.02 0.09 0.07 0.15 0.20 0.26 0.39 21 0.98
#> log_lik[48] 0.64 0.02 0.07 0.55 0.59 0.62 0.67 0.78 8 1.13
#> log_lik[49] 0.34 0.02 0.08 0.16 0.30 0.34 0.38 0.45 20 0.98
#> log_lik[50] 0.60 0.02 0.07 0.51 0.57 0.59 0.64 0.74 9 1.12
#> log_lik[51] 0.36 0.02 0.07 0.19 0.33 0.36 0.40 0.48 19 0.98
#> log_lik[52] 0.67 0.03 0.07 0.58 0.63 0.66 0.71 0.81 7 1.15
#> log_lik[53] 0.71 0.03 0.07 0.61 0.66 0.69 0.75 0.85 7 1.15
#> log_lik[54] 0.70 0.03 0.07 0.61 0.66 0.69 0.74 0.85 7 1.15
#> log_lik[55] -0.20 0.03 0.15 -0.51 -0.25 -0.15 -0.11 -0.02 23 0.99
#> log_lik[56] 0.67 0.03 0.07 0.58 0.63 0.66 0.71 0.81 7 1.15
#> log_lik[57] 0.20 0.02 0.09 -0.01 0.16 0.22 0.27 0.31 24 0.96
#> log_lik[58] 0.51 0.02 0.07 0.39 0.48 0.50 0.53 0.64 12 1.05
#> log_lik[59] 0.46 0.01 0.07 0.37 0.42 0.44 0.50 0.62 25 0.97
#> log_lik[60] 0.31 0.02 0.08 0.12 0.27 0.31 0.35 0.42 21 0.97
#> log_lik[61] 0.43 0.02 0.07 0.29 0.40 0.43 0.46 0.56 16 1.01
#> log_lik[62] 0.71 0.03 0.07 0.61 0.66 0.70 0.75 0.85 7 1.15
#> log_lik[63] 0.67 0.03 0.07 0.58 0.63 0.66 0.71 0.81 7 1.15
#> log_lik[64] 0.71 0.03 0.08 0.60 0.65 0.69 0.77 0.84 9 1.10
#> log_lik[65] 0.64 0.02 0.07 0.53 0.58 0.61 0.68 0.77 17 1.04
#> log_lik[66] 0.74 0.03 0.08 0.63 0.68 0.74 0.78 0.88 7 1.14
#> log_lik[67] 0.61 0.02 0.07 0.52 0.57 0.59 0.64 0.75 9 1.12
#> log_lik[68] 0.57 0.02 0.07 0.47 0.52 0.54 0.60 0.71 21 1.00
#> log_lik[69] 0.38 0.02 0.07 0.22 0.35 0.38 0.42 0.50 19 0.99
#> log_lik[70] -0.45 0.04 0.18 -0.84 -0.50 -0.40 -0.33 -0.23 21 1.01
#> log_lik[71] -0.80 0.05 0.24 -1.30 -0.88 -0.70 -0.64 -0.52 19 1.03
#> log_lik[72] 0.67 0.03 0.07 0.58 0.63 0.66 0.71 0.81 7 1.15
#> log_lik[73] 0.57 0.02 0.07 0.48 0.54 0.57 0.60 0.71 9 1.10
#> log_lik[74] 0.19 0.02 0.09 -0.03 0.15 0.21 0.26 0.30 24 0.96
#> log_lik[75] 0.53 0.02 0.07 0.42 0.50 0.52 0.55 0.66 11 1.07
#> log_lik[76] 0.46 0.01 0.07 0.37 0.42 0.44 0.50 0.62 25 0.97
#> log_lik[77] 0.70 0.03 0.07 0.60 0.65 0.68 0.74 0.84 7 1.15
#> log_lik[78] 0.72 0.03 0.08 0.62 0.67 0.71 0.76 0.86 7 1.15
#> log_lik[79] 0.68 0.03 0.07 0.58 0.63 0.67 0.71 0.82 7 1.15
#> log_lik[80] 0.73 0.03 0.08 0.63 0.68 0.73 0.78 0.88 7 1.15
#> log_lik[81] 0.20 0.02 0.09 -0.01 0.16 0.22 0.27 0.31 24 0.96
#> log_lik[82] 0.24 0.02 0.09 0.03 0.20 0.25 0.30 0.35 23 0.96
#> log_lik[83] 0.65 0.03 0.07 0.56 0.61 0.64 0.69 0.79 8 1.14
#> log_lik[84] 0.43 0.01 0.07 0.33 0.38 0.40 0.47 0.58 26 0.96
#> log_lik[85] 0.73 0.03 0.08 0.62 0.68 0.73 0.78 0.87 8 1.12
#> log_lik[86] 0.63 0.02 0.07 0.53 0.58 0.61 0.67 0.77 18 1.04
#> log_lik[87] 0.51 0.02 0.07 0.39 0.49 0.50 0.54 0.65 12 1.06
#> log_lik[88] 0.73 0.03 0.08 0.62 0.67 0.72 0.79 0.87 8 1.12
#> log_lik[89] 0.66 0.02 0.08 0.55 0.60 0.64 0.71 0.79 14 1.05
#> log_lik[90] 0.58 0.02 0.07 0.49 0.55 0.58 0.61 0.72 9 1.10
#> log_lik[91] 0.54 0.02 0.07 0.44 0.49 0.51 0.57 0.68 22 0.99
#> log_lik[92] 0.74 0.03 0.08 0.63 0.68 0.74 0.78 0.88 7 1.13
#> log_lik[93] 0.74 0.03 0.08 0.63 0.68 0.74 0.78 0.88 7 1.14
#> log_lik[94] -0.19 0.04 0.13 -0.45 -0.22 -0.15 -0.12 0.03 11 1.08
#> log_lik[95] 0.74 0.03 0.08 0.63 0.68 0.73 0.78 0.88 8 1.13
#> log_lik[96] 0.22 0.02 0.09 0.01 0.18 0.23 0.28 0.33 24 0.96
#> log_lik[97] 0.73 0.03 0.08 0.62 0.68 0.72 0.78 0.87 8 1.12
#> log_lik[98] -0.01 0.02 0.12 -0.28 -0.05 0.04 0.06 0.14 25 0.97
#> log_lik[99] -0.05 0.02 0.12 -0.32 -0.09 0.01 0.02 0.11 25 0.97
#> log_lik[100] 0.17 0.02 0.09 -0.05 0.13 0.19 0.24 0.28 25 0.96
#> lp__ 114.02 0.15 0.74 112.57 113.57 114.22 114.57 114.94 25 1.09
#>
#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 10:38:15 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
#>
#> $best_model$y
#> Time Series:
#> Start = 1871
#> End = 1970
#> Frequency = 1
#> [1] 7.021084 7.056175 6.870053 7.098376 7.056175 7.056175 6.700731 7.114769
#> [9] 7.222566 7.038784 6.902743 6.840547 7.012115 6.901737 6.927558 6.866933
#> [17] 7.073270 6.683361 6.864848 7.038784 7.003065 7.098376 7.047517 7.130899
#> [25] 7.138867 7.106606 6.937314 7.003065 6.651572 6.733402 6.773080 6.542472
#> [33] 6.845880 6.725034 6.552508 6.820016 6.539586 6.927558 6.956545 6.876265
#> [41] 6.722630 6.587550 6.122493 6.714171 6.553933 7.021084 7.003065 6.723832
#> [49] 6.638568 6.710523 6.643790 6.739337 6.761573 6.759255 6.548219 6.739337
#> [57] 6.612041 6.679599 6.946976 6.632002 6.660575 6.762730 6.739337 6.850126
#> [65] 6.891626 6.799056 6.711740 6.917706 6.647688 6.516193 6.475433 6.740519
#> [73] 6.699500 6.609349 6.685861 6.946976 6.756932 6.773080 6.742881 6.791221
#> [81] 6.612041 6.618739 6.731018 6.956545 6.822197 6.893656 6.680855 6.827629
#> [89] 6.882437 6.703188 6.927558 6.809039 6.803505 7.064759 6.815640 6.614726
#> [97] 6.823286 6.576470 6.570883 6.606650
#>
#> $best_model$looic
#> [1] -49.7178
#>
#>
#> $n_loo_bad
#> [1] 35
#>
#> $n_loo_very_bad
#> [1] 3
#>