Fit a DFA with different number of trends and return the leave one out (LOO) value as calculated by the loo package.

find_dfa_trends(
  y = y,
  kmin = 1,
  kmax = 5,
  iter = 2000,
  thin = 1,
  compare_normal = FALSE,
  convergence_threshold = 1.05,
  variance = c("equal", "unequal"),
  ...
)

Arguments

y

A matrix of data to fit. Columns represent time element.

kmin

Minimum number of trends, defaults to 1.

kmax

Maximum number of trends, defaults to 5.

iter

Iterations when sampling from each Stan model, defaults to 2000.

thin

Thinning rate when sampling from each Stan model, defaults to 1.

compare_normal

If TRUE, does model selection comparison of Normal vs. Student-t errors

convergence_threshold

The maximum allowed value of Rhat to determine convergence of parameters

variance

Vector of variance arguments for searching over large groups of models. Can be either or both of ("equal","unequal")

...

Other arguments to pass to fit_dfa()

Examples

# \donttest{ set.seed(42) s <- sim_dfa(num_trends = 2, num_years = 20, num_ts = 3) # only 1 chain and 180 iterations used so example runs quickly: m <- find_dfa_trends( y = s$y_sim, iter = 50, kmin = 1, kmax = 2, chains = 1, compare_normal = FALSE, variance = "equal", convergence_threshold = 1.1, control = list(adapt_delta = 0.95, max_treedepth = 20) )
#> #> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 4.5e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.45 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: 0.002942 seconds (Warm-up) #> Chain 1: 0.194462 seconds (Sampling) #> Chain 1: 0.197404 seconds (Total) #> Chain 1:
#> Warning: There were 5 divergent transitions after warmup. See #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them.
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See #> http://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.1, indicating chains have not mixed. #> Running the chains for more iterations may help. See #> http://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 #> http://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 #> http://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for the input samples (1 chains: each with iter = 25; warmup = 12): #> #> Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS #> x[1,1] 0.3 0.8 1.1 0.8 0.3 2.06 4 13 #> x[1,2] -0.5 0.0 0.5 0.0 0.3 2.06 7 13 #> x[1,3] 0.5 0.6 1.8 0.9 0.5 2.06 4 13 #> x[1,4] -0.6 -0.4 1.4 0.1 0.9 2.06 4 13 #> x[1,5] -0.9 -0.6 2.5 0.3 1.4 2.06 4 13 #> x[1,6] -0.2 0.0 2.3 0.7 1.0 1.87 8 13 #> x[1,7] -0.3 -0.1 1.9 0.4 0.9 1.87 6 13 #> x[1,8] 0.1 0.5 1.7 0.7 0.6 1.87 10 13 #> x[1,9] -0.5 -0.2 2.9 0.6 1.3 2.06 6 13 #> x[1,10] 0.7 1.1 3.9 1.8 1.3 2.06 6 13 #> x[1,11] -0.4 -0.2 3.6 0.9 1.7 1.87 5 13 #> x[1,12] -0.6 -0.3 1.6 0.2 0.9 2.06 6 13 #> x[1,13] -0.2 1.1 1.7 0.8 0.7 2.06 7 13 #> x[1,14] -1.3 -0.2 0.2 -0.4 0.5 1.18 8 13 #> x[1,15] -3.2 -0.4 -0.2 -1.1 1.2 1.87 4 13 #> x[1,16] -2.4 -0.5 0.2 -0.7 1.0 2.06 4 13 #> x[1,17] -2.8 -1.0 -0.1 -1.1 1.1 2.06 4 13 #> x[1,18] -3.2 -1.0 -0.5 -1.5 1.1 2.06 4 13 #> x[1,19] -2.4 -0.6 -0.2 -1.1 0.8 1.27 6 13 #> x[1,20] -1.1 1.0 1.4 0.4 1.0 2.06 6 13 #> Z[1,1] -95.2 -5.2 -0.4 -30.0 43.3 2.06 4 13 #> Z[2,1] -1.4 -0.3 1.7 0.0 1.1 1.71 13 13 #> Z[3,1] -1.2 -0.4 0.8 -0.3 0.7 1.71 13 13 #> log_lik[1] -14.4 -3.0 -0.5 -5.2 5.7 2.06 4 13 #> log_lik[2] -4.2 -3.6 -1.9 -3.4 0.8 2.06 4 13 #> log_lik[3] -4.2 -3.8 -2.3 -3.5 0.7 2.06 4 13 #> log_lik[4] -4.2 -2.9 -0.5 -2.4 1.8 2.06 4 13 #> log_lik[5] -4.2 -2.9 -1.8 -3.0 1.0 2.06 4 13 #> log_lik[6] -4.2 -2.9 -1.7 -3.0 1.0 2.06 4 13 #> log_lik[7] -6.9 -3.0 -0.6 -3.2 2.6 2.06 4 13 #> log_lik[8] -4.2 -3.1 -1.1 -2.9 1.3 2.06 4 13 #> log_lik[9] -4.2 -2.9 -1.0 -2.8 1.4 2.06 4 13 #> log_lik[10] -6.9 -3.0 -0.8 -3.5 2.5 2.06 4 13 #> log_lik[11] -4.2 -2.9 -0.5 -2.4 1.7 2.06 4 13 #> log_lik[12] -4.2 -2.9 -0.6 -2.5 1.6 2.06 4 13 #> log_lik[13] -8.2 -3.4 -0.7 -4.0 3.0 2.06 4 13 #> log_lik[14] -4.2 -2.9 -0.6 -2.5 1.7 2.06 4 13 #> log_lik[15] -4.2 -2.9 -0.7 -2.6 1.5 2.06 4 13 #> log_lik[16] -4.2 -2.9 -0.6 -2.5 1.7 2.06 4 13 #> log_lik[17] -4.2 -3.8 -1.0 -2.9 1.4 2.06 4 13 #> log_lik[18] -4.2 -2.9 -0.7 -2.6 1.5 2.06 4 13 #> log_lik[19] -4.6 -2.9 -0.7 -2.7 1.6 2.06 4 13 #> log_lik[20] -4.2 -2.9 -1.1 -2.8 1.3 2.06 4 13 #> log_lik[21] -4.2 -2.9 -1.0 -2.8 1.4 2.06 4 13 #> log_lik[22] -6.3 -2.9 -0.6 -3.0 2.3 2.06 4 13 #> log_lik[23] -4.2 -2.9 -0.7 -2.6 1.5 2.06 4 13 #> log_lik[24] -4.2 -2.9 -0.8 -2.7 1.5 2.06 4 13 #> log_lik[25] -4.9 -3.8 -0.7 -3.0 1.7 1.87 4 13 #> log_lik[26] -4.2 -2.9 -0.7 -2.6 1.5 2.06 4 13 #> log_lik[27] -4.2 -2.9 -0.6 -2.5 1.6 2.06 4 13 #> log_lik[28] -13.0 -3.0 -0.6 -4.7 4.8 2.06 4 13 #> log_lik[29] -4.2 -3.1 -0.6 -2.6 1.6 2.06 4 13 #> log_lik[30] -4.2 -3.7 -0.5 -2.6 1.7 2.06 4 13 #> log_lik[31] -5.2 -3.0 -0.6 -2.9 1.9 2.06 4 13 #> log_lik[32] -4.2 -2.9 -0.7 -2.8 1.4 2.06 4 13 #> log_lik[33] -4.2 -2.9 -0.8 -2.7 1.4 2.06 4 13 #> log_lik[34] -6.1 -3.0 -0.8 -3.2 2.1 2.06 4 13 #> log_lik[35] -4.2 -2.9 -0.5 -2.4 1.7 2.06 4 13 #> log_lik[36] -4.2 -2.9 -0.5 -2.4 1.7 2.06 4 13 #> log_lik[37] -16.9 -3.3 -0.8 -5.9 6.4 2.06 4 13 #> log_lik[38] -4.2 -3.0 -0.5 -2.4 1.7 2.06 4 13 #> log_lik[39] -4.2 -2.9 -0.5 -2.4 1.7 2.06 4 13 #> log_lik[40] -4.4 -3.0 -0.8 -2.7 1.6 2.06 4 13 #> log_lik[41] -4.2 -3.5 -0.9 -2.8 1.5 2.06 4 13 #> log_lik[42] -4.3 -3.8 -1.7 -3.2 1.1 2.06 6 13 #> log_lik[43] -5.0 -3.0 -0.6 -2.8 1.8 2.06 4 13 #> log_lik[44] -5.2 -3.8 -0.7 -3.0 1.8 2.06 6 13 #> log_lik[45] -4.2 -3.0 -0.5 -2.6 1.6 2.06 4 13 #> log_lik[46] -4.3 -2.9 -0.6 -2.6 1.6 2.06 4 13 #> log_lik[47] -4.2 -2.9 -0.5 -2.5 1.6 2.06 4 13 #> log_lik[48] -4.2 -2.9 -0.6 -2.5 1.6 2.06 4 13 #> log_lik[49] -4.9 -3.1 -0.5 -2.6 1.9 2.06 4 13 #> log_lik[50] -4.2 -3.3 -0.5 -2.6 1.6 2.06 4 13 #> log_lik[51] -4.2 -2.9 -0.5 -2.6 1.6 2.06 4 13 #> log_lik[52] -7.8 -3.4 -0.6 -3.7 2.9 2.06 4 13 #> log_lik[53] -4.2 -3.0 -0.9 -2.8 1.4 2.06 4 13 #> log_lik[54] -4.2 -2.9 -0.9 -2.8 1.4 2.06 4 13 #> log_lik[55] -7.0 -3.7 -0.6 -3.3 2.7 2.06 4 13 #> log_lik[56] -4.2 -3.0 -0.8 -2.8 1.4 2.06 4 13 #> log_lik[57] -4.2 -2.9 -0.9 -2.8 1.4 2.06 4 13 #> log_lik[58] -14.5 -2.9 -0.8 -5.5 5.6 2.06 4 13 #> log_lik[59] -4.2 -2.9 -0.7 -2.6 1.5 2.06 4 13 #> log_lik[60] -4.2 -2.9 -0.7 -2.6 1.6 2.06 4 13 #> xstar[1,1] -2.6 0.2 1.7 -0.1 1.5 1.18 7 13 #> sigma[1] 0.6 7.4 25.4 11.7 11.5 2.06 4 13 #> nu[1] 2.5 2.6 4.2 3.1 0.7 2.06 4 13 #> lp__ -4856.7 -202.5 -67.7 -1503.1 2144.4 2.06 3 13 #> #> For each parameter, Bulk_ESS and Tail_ESS are crude measures of #> effective sample size for bulk and tail quantities respectively (an ESS > 100 #> per chain is considered good), and Rhat is the potential scale reduction #> factor on rank normalized split chains (at convergence, Rhat <= 1.05).
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> #> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 4.1e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.41 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: 0.015941 seconds (Warm-up) #> Chain 1: 0.017775 seconds (Sampling) #> Chain 1: 0.033716 seconds (Total) #> Chain 1:
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See #> http://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed. #> Running the chains for more iterations may help. See #> http://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 #> http://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 #> http://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for the input samples (1 chains: each with iter = 25; warmup = 12): #> #> Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS #> x[1,1] 0.2 0.3 0.3 0.3 0.0 1.58 4 13 #> x[2,1] 2.0 2.1 2.1 2.0 0.0 2.06 4 13 #> x[1,2] -0.1 -0.1 0.0 -0.1 0.0 1.12 7 13 #> x[2,2] 0.6 0.7 0.8 0.7 0.0 2.06 4 13 #> x[1,3] -0.5 -0.4 -0.3 -0.4 0.0 1.19 8 13 #> x[2,3] -0.9 -0.8 -0.7 -0.8 0.0 2.06 4 13 #> x[1,4] 0.2 0.4 0.4 0.3 0.1 1.39 7 13 #> x[2,4] -2.4 -2.4 -2.3 -2.4 0.0 1.58 4 13 #> x[1,5] -1.3 -1.2 -1.2 -1.2 0.0 1.58 7 13 #> x[2,5] -0.3 -0.2 -0.1 -0.2 0.0 1.32 5 13 #> x[1,6] 0.0 0.1 0.1 0.1 0.1 1.45 7 13 #> x[2,6] 1.3 1.3 1.4 1.3 0.1 1.48 4 13 #> x[1,7] -1.1 -1.1 -1.0 -1.0 0.0 1.38 7 13 #> x[2,7] -0.4 -0.4 -0.3 -0.4 0.1 1.30 5 13 #> x[1,8] -1.3 -1.2 -1.2 -1.2 0.1 1.25 6 13 #> x[2,8] -1.5 -1.5 -1.3 -1.4 0.1 1.58 6 13 #> x[1,9] -1.5 -1.4 -1.3 -1.4 0.1 0.98 7 13 #> x[2,9] 0.5 0.5 0.7 0.6 0.1 1.39 4 13 #> x[1,10] -0.5 -0.4 -0.3 -0.4 0.1 0.98 7 13 #> x[2,10] 0.5 0.6 0.7 0.6 0.1 1.39 4 13 #> x[1,11] 0.4 0.6 0.7 0.6 0.1 1.24 5 13 #> x[2,11] -0.1 0.0 0.2 0.0 0.1 2.06 3 13 #> x[1,12] 0.5 0.7 0.8 0.7 0.1 1.39 4 13 #> x[2,12] 0.2 0.3 0.5 0.3 0.1 2.06 3 13 #> x[1,13] 0.1 0.3 0.4 0.3 0.1 1.30 5 13 #> x[2,13] 1.1 1.2 1.4 1.2 0.1 2.06 3 13 #> x[1,14] -1.2 -1.1 -1.0 -1.1 0.1 1.45 5 13 #> x[2,14] 0.2 0.2 0.5 0.3 0.1 1.71 4 13 #> x[1,15] -0.1 0.1 0.1 0.0 0.1 1.45 5 13 #> x[2,15] 0.4 0.4 0.6 0.4 0.1 1.21 5 13 #> x[1,16] 0.0 0.2 0.2 0.2 0.1 1.71 5 13 #> x[2,16] 1.8 1.8 2.0 1.8 0.1 1.58 4 13 #> x[1,17] 1.2 1.3 1.4 1.3 0.1 1.58 4 13 #> x[2,17] 0.6 0.6 0.7 0.6 0.0 0.98 10 13 #> x[1,18] 2.4 2.6 2.7 2.6 0.1 1.87 4 13 #> x[2,18] -0.9 -0.8 -0.6 -0.8 0.1 1.47 4 13 #> x[1,19] 0.5 0.7 0.8 0.7 0.1 1.87 4 13 #> x[2,19] -1.5 -1.4 -1.3 -1.4 0.1 1.58 4 13 #> x[1,20] -0.4 -0.2 -0.1 -0.3 0.1 1.71 4 13 #> x[2,20] -3.1 -3.0 -2.8 -2.9 0.1 1.58 4 13 #> Z[1,1] -95.9 -95.0 -90.4 -94.4 2.2 2.06 3 13 #> Z[2,1] -1.1 -0.2 1.4 -0.1 0.8 1.04 9 13 #> Z[3,1] -0.3 0.4 1.1 0.5 0.5 1.27 5 13 #> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13 #> Z[2,2] -77.1 -38.5 1.1 -39.8 29.3 2.06 3 13 #> Z[3,2] -1.1 0.9 1.3 0.5 0.9 1.87 4 13 #> log_lik[1] -4.9 -4.9 -4.8 -4.9 0.0 1.87 4 13 #> log_lik[2] -10.7 -6.4 -4.7 -7.2 2.3 2.06 4 13 #> log_lik[3] -4.7 -4.7 -4.7 -4.7 0.0 1.18 9 13 #> log_lik[4] -4.8 -4.7 -4.7 -4.7 0.0 0.93 9 13 #> log_lik[5] -5.3 -4.9 -4.7 -5.0 0.2 2.06 4 13 #> log_lik[6] -4.7 -4.7 -4.7 -4.7 0.0 1.04 9 13 #> log_lik[7] -5.2 -5.0 -5.0 -5.1 0.1 1.14 9 13 #> log_lik[8] -5.8 -4.9 -4.7 -5.1 0.4 2.06 3 13 #> log_lik[9] -4.7 -4.7 -4.7 -4.7 0.0 1.11 8 13 #> log_lik[10] -5.1 -5.0 -4.8 -5.0 0.1 1.21 6 13 #> log_lik[11] -13.4 -6.9 -4.7 -8.1 3.4 2.06 4 13 #> log_lik[12] -4.7 -4.7 -4.7 -4.7 0.0 1.30 8 13 #> log_lik[13] -8.5 -8.2 -7.9 -8.1 0.2 1.37 12 13 #> log_lik[14] -4.8 -4.7 -4.7 -4.8 0.0 2.06 4 13 #> log_lik[15] -4.7 -4.7 -4.7 -4.7 0.0 1.05 9 13 #> log_lik[16] -4.8 -4.7 -4.7 -4.7 0.0 1.13 6 13 #> log_lik[17] -7.1 -5.3 -4.7 -5.7 0.9 2.06 3 13 #> log_lik[18] -4.7 -4.7 -4.7 -4.7 0.0 1.18 8 13 #> log_lik[19] -7.5 -7.1 -6.9 -7.2 0.2 1.27 12 13 #> log_lik[20] -5.0 -4.8 -4.7 -4.8 0.1 2.06 4 13 #> log_lik[21] -4.7 -4.7 -4.7 -4.7 0.0 1.05 9 13 #> log_lik[22] -8.7 -8.1 -7.8 -8.1 0.3 1.13 13 13 #> log_lik[23] -7.9 -5.6 -4.7 -6.0 1.2 2.06 4 13 #> log_lik[24] -4.7 -4.7 -4.7 -4.7 0.0 1.18 9 13 #> log_lik[25] -9.9 -9.2 -8.7 -9.2 0.5 0.95 13 13 #> log_lik[26] -5.1 -4.8 -4.7 -4.9 0.2 2.06 4 13 #> log_lik[27] -4.7 -4.7 -4.7 -4.7 0.0 1.05 9 13 #> log_lik[28] -5.3 -5.2 -5.0 -5.1 0.1 0.98 7 13 #> log_lik[29] -5.2 -4.8 -4.7 -4.9 0.2 2.06 4 13 #> log_lik[30] -4.7 -4.7 -4.7 -4.7 0.0 1.05 9 13 #> log_lik[31] -5.8 -5.5 -5.0 -5.5 0.3 1.24 5 13 #> log_lik[32] -4.7 -4.7 -4.7 -4.7 0.0 1.48 5 13 #> log_lik[33] -4.7 -4.7 -4.7 -4.7 0.0 1.04 9 13 #> log_lik[34] -6.1 -5.7 -5.2 -5.7 0.3 1.47 4 13 #> log_lik[35] -4.8 -4.7 -4.7 -4.7 0.0 2.06 4 13 #> log_lik[36] -4.7 -4.7 -4.7 -4.7 0.0 1.04 10 13 #> log_lik[37] -5.1 -4.9 -4.8 -4.9 0.1 1.45 4 13 #> log_lik[38] -6.6 -5.2 -4.7 -5.5 0.7 2.06 4 13 #> log_lik[39] -4.7 -4.7 -4.7 -4.7 0.0 1.11 8 13 #> log_lik[40] -7.9 -7.4 -6.9 -7.3 0.4 1.32 5 13 #> log_lik[41] -4.8 -4.7 -4.7 -4.7 0.0 2.06 4 13 #> log_lik[42] -4.7 -4.7 -4.7 -4.7 0.0 1.05 10 13 #> log_lik[43] -4.8 -4.7 -4.7 -4.7 0.0 1.00 12 13 #> log_lik[44] -5.0 -4.8 -4.7 -4.8 0.1 2.06 4 13 #> log_lik[45] -4.7 -4.7 -4.7 -4.7 0.0 1.04 9 13 #> log_lik[46] -4.9 -4.8 -4.7 -4.8 0.1 1.71 4 13 #> log_lik[47] -9.5 -6.0 -4.7 -6.7 1.9 2.06 4 13 #> log_lik[48] -4.7 -4.7 -4.7 -4.7 0.0 1.18 9 13 #> log_lik[49] -9.2 -8.9 -7.8 -8.7 0.5 1.71 4 13 #> log_lik[50] -5.3 -4.9 -4.7 -5.0 0.2 2.06 4 13 #> log_lik[51] -4.7 -4.7 -4.7 -4.7 0.0 1.07 8 13 #> log_lik[52] -21.3 -20.1 -16.3 -19.6 1.7 1.71 4 13 #> log_lik[53] -5.9 -4.9 -4.7 -5.1 0.4 2.06 4 13 #> log_lik[54] -4.7 -4.7 -4.7 -4.7 0.0 1.11 8 13 #> log_lik[55] -6.1 -5.8 -5.2 -5.8 0.3 1.87 4 13 #> log_lik[56] -8.1 -5.4 -4.7 -6.0 1.3 2.06 4 13 #> log_lik[57] -4.7 -4.7 -4.7 -4.7 0.0 1.05 9 13 #> log_lik[58] -5.1 -4.8 -4.8 -4.9 0.1 1.71 4 13 #> log_lik[59] -18.6 -8.0 -4.7 -10.1 5.4 2.06 3 13 #> log_lik[60] -4.7 -4.7 -4.7 -4.7 0.0 1.30 7 13 #> xstar[1,1] -4.4 -0.1 1.4 -0.5 2.4 0.93 13 13 #> xstar[2,1] -4.3 -3.0 -1.3 -2.9 1.1 1.03 13 13 #> sigma[1] 44.0 44.6 45.1 44.6 0.4 1.04 9 13 #> nu[1] 2.4 2.4 2.5 2.4 0.0 1.19 8 13 #> lp__ -7979.1 -5627.7 -4448.7 -6026.9 1321.7 2.06 3 13 #> #> For each parameter, Bulk_ESS and Tail_ESS are crude measures of #> effective sample size for bulk and tail quantities respectively (an ESS > 100 #> per chain is considered good), and Rhat is the potential scale reduction #> factor on rank normalized split chains (at convergence, Rhat <= 1.05).
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
m$summary
#> model num_trends looic cor error converge #> 1 1 1 2194.503 equal student-t FALSE #> 2 2 2 3030.814 equal student-t FALSE
m$best_model
#> NULL
# }