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"), ... )
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 |
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 |
# \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 FALSEm$best_model#> NULL# }