Get the loadings from a DFA as a data frame
dfa_loadings(rotated_modelfit, names = NULL, summary = TRUE, conf_level = 0.95)Output from rotate_trends.
An optional vector of names for plotting the loadings.
Logical. Should the full posterior densities be returned? Defaults to TRUE.
Confidence level for credible intervals. Defaults to 0.95.
A data frame with the following columns:
name is an identifier for each loading, trend is the trend for the
loading, median is the posterior median loading, lower is the lower CI,
upper is the upper CI, and prob_diff0 is the probability the loading is
different than 0. When summary = FALSE, there is no lower or upper
columns and instead there are columns chain and draw.
plot_loadings fit_dfa rotate_trends
set.seed(42)
s <- sim_dfa(num_trends = 2, num_ts = 4, num_years = 10)
# only 1 chain and 180 iterations used so example runs quickly:
m <- fit_dfa(y = s$y_sim, num_trends = 2, iter = 50, chains = 1)
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 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: 0.034 seconds (Warm-up)
#> Chain 1: 0.388 seconds (Sampling)
#> Chain 1: 0.422 seconds (Total)
#> Chain 1:
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://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
#> 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
#> 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] -1.5 -0.7 0.5 -0.6 0.7 1.37 6 13
#> x[2,1] -1.0 -0.2 0.6 -0.2 0.6 1.58 9 13
#> x[1,2] -1.7 0.0 0.9 -0.4 1.1 1.87 4 13
#> x[2,2] 0.2 0.5 1.2 0.6 0.4 1.21 12 13
#> x[1,3] -1.6 0.0 0.8 -0.3 1.0 2.06 4 13
#> x[2,3] -1.1 -0.2 0.3 -0.4 0.5 2.06 4 13
#> x[1,4] -1.5 -0.4 0.7 -0.4 0.9 1.87 4 13
#> x[2,4] -0.1 0.5 1.3 0.5 0.5 1.03 10 13
#> x[1,5] -0.9 -0.2 0.7 -0.1 0.6 0.95 13 13
#> x[2,5] -1.4 -0.6 1.3 -0.3 1.1 1.87 6 13
#> x[1,6] -1.2 -0.3 0.3 -0.4 0.5 1.06 7 13
#> x[2,6] -0.2 0.0 0.6 0.1 0.3 1.09 13 13
#> x[1,7] -2.2 -1.2 0.1 -1.1 0.9 1.71 4 13
#> x[2,7] -1.2 -0.1 0.9 -0.2 0.7 1.39 13 13
#> x[1,8] -1.7 -0.8 0.5 -0.7 0.8 1.32 5 13
#> x[2,8] -1.8 0.2 1.4 0.0 1.0 1.87 13 13
#> x[1,9] -1.2 -0.2 0.7 -0.2 0.6 1.15 8 13
#> x[2,9] -1.6 0.5 1.2 0.1 1.0 2.06 9 13
#> x[1,10] -1.4 -0.7 0.2 -0.7 0.6 1.48 4 13
#> x[2,10] -1.2 -0.4 0.3 -0.5 0.5 0.99 13 13
#> Z[1,1] -3.6 0.7 3.6 0.3 2.4 2.06 13 13
#> Z[2,1] -0.6 0.5 1.4 0.3 0.7 1.01 11 13
#> Z[3,1] -1.5 -0.5 1.3 -0.3 1.0 1.87 4 13
#> Z[4,1] -0.8 0.0 1.9 0.3 0.9 1.39 5 13
#> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13
#> Z[2,2] -4.2 0.9 6.2 0.5 3.9 1.33 13 13
#> Z[3,2] -1.6 -0.7 0.5 -0.6 0.8 1.01 10 13
#> Z[4,2] -1.2 0.8 1.5 0.5 1.0 0.97 13 13
#> log_lik[1] -4.3 -1.5 -0.7 -2.1 1.4 1.87 4 13
#> log_lik[2] -4.3 -1.3 -0.6 -1.9 1.4 1.87 4 13
#> log_lik[3] -4.3 -1.2 -0.7 -2.0 1.4 2.06 4 13
#> log_lik[4] -4.3 -1.1 -0.6 -2.0 1.5 2.06 4 13
#> log_lik[5] -4.3 -1.5 -0.8 -2.1 1.4 1.87 4 13
#> log_lik[6] -4.3 -1.3 -0.7 -2.0 1.4 1.71 4 13
#> log_lik[7] -4.3 -1.6 -0.8 -2.1 1.4 1.71 4 13
#> log_lik[8] -4.7 -1.4 -0.6 -2.4 1.7 1.87 4 13
#> log_lik[9] -4.6 -3.5 -2.2 -3.4 1.0 0.91 12 13
#> log_lik[10] -4.3 -2.3 -0.7 -2.7 1.3 1.10 8 13
#> log_lik[11] -4.3 -2.1 -0.6 -2.5 1.4 1.32 4 13
#> log_lik[12] -4.3 -1.6 -0.8 -2.2 1.4 1.47 4 13
#> log_lik[13] -4.3 -1.4 -0.8 -2.1 1.4 1.87 4 13
#> log_lik[14] -4.3 -1.5 -0.6 -2.0 1.5 1.87 4 13
#> log_lik[15] -4.3 -1.5 -0.6 -2.0 1.4 2.06 4 13
#> log_lik[16] -4.3 -1.5 -0.7 -2.2 1.4 1.87 4 13
#> log_lik[17] -4.3 -1.3 -0.6 -1.9 1.5 2.06 4 13
#> log_lik[18] -4.4 -2.0 -0.6 -2.2 1.4 2.06 4 13
#> log_lik[19] -4.3 -1.9 -0.6 -2.1 1.4 2.06 4 13
#> log_lik[20] -4.3 -1.7 -0.7 -2.1 1.4 2.06 4 13
#> log_lik[21] -4.3 -1.1 -0.5 -1.9 1.5 2.06 4 13
#> log_lik[22] -4.3 -1.2 -0.6 -1.8 1.5 2.06 4 13
#> log_lik[23] -4.3 -2.0 -0.7 -2.1 1.4 1.58 4 13
#> log_lik[24] -4.3 -1.3 -0.6 -1.9 1.5 2.06 4 13
#> log_lik[25] -4.4 -2.2 -0.6 -2.3 1.6 1.71 4 13
#> log_lik[26] -4.3 -1.5 -0.7 -2.0 1.4 1.58 4 13
#> log_lik[27] -4.3 -1.6 -0.8 -2.1 1.4 2.06 4 13
#> log_lik[28] -4.3 -1.6 -0.8 -2.1 1.4 1.87 4 13
#> log_lik[29] -4.3 -1.5 -0.6 -2.2 1.4 1.47 4 13
#> log_lik[30] -4.3 -1.6 -0.6 -2.0 1.4 1.58 4 13
#> log_lik[31] -4.3 -1.1 -0.5 -1.8 1.5 2.06 3 13
#> log_lik[32] -4.3 -1.4 -0.9 -2.0 1.3 1.87 4 13
#> log_lik[33] -4.3 -1.6 -0.7 -2.1 1.3 1.71 4 13
#> log_lik[34] -4.3 -1.6 -0.6 -2.1 1.4 2.06 4 13
#> log_lik[35] -4.3 -1.0 -0.5 -1.8 1.5 2.06 4 13
#> log_lik[36] -4.3 -1.0 -0.7 -1.9 1.4 2.06 4 13
#> log_lik[37] -4.3 -2.0 -0.7 -2.2 1.4 1.87 4 13
#> log_lik[38] -4.3 -1.2 -0.6 -1.9 1.4 2.06 4 13
#> log_lik[39] -4.3 -1.4 -0.9 -2.0 1.3 1.87 4 13
#> log_lik[40] -4.3 -1.2 -0.6 -1.9 1.5 2.06 3 13
#> xstar[1,1] -2.0 -1.1 1.3 -0.7 1.2 0.95 10 13
#> xstar[2,1] -1.8 -0.4 1.2 -0.3 1.2 1.12 8 13
#> sigma[1] 0.7 1.0 29.7 7.5 11.8 2.06 3 13
#> lp__ -203.1 -43.1 -21.1 -79.1 72.6 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).
r <- rotate_trends(m)
loadings <- dfa_loadings(r, summary = TRUE)
loadings <- dfa_loadings(r, summary = FALSE)