Rotate the trends from a DFA

rotate_trends(fitted_model, conf_level = 0.95, invert = FALSE)

Arguments

fitted_model

Output from fit_dfa().

conf_level

Probability level for CI.

invert

Whether to invert the trends and loadings for plotting purposes

Examples

set.seed(42)
s <- sim_dfa(num_trends = 1, num_years = 20, num_ts = 3)
# only 1 chain and 800 iterations used so example runs quickly:
m <- fit_dfa(y = s$y_sim, iter = 50, chains = 1)
#> 
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.39 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: 0.007536 seconds (Warm-up)
#> Chain 1:                0.109812 seconds (Sampling)
#> Chain 1:                0.117348 seconds (Total)
#> Chain 1: 
#> Warning: There were 16 divergent transitions after warmup. See
#> https://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
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.11, 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]        0.3   0.3   0.7   0.4 0.2  2.19       13       13
#> x[1,2]       -0.2   0.0   0.0   0.0 0.1  1.72        4       13
#> x[1,3]        0.2   0.2   0.9   0.5 0.3  2.19        4       13
#> x[1,4]        0.5   0.6   1.0   0.7 0.2  2.19        6       13
#> x[1,5]       -0.2   0.5   1.0   0.4 0.4  2.19       13       13
#> x[1,6]        0.4   1.3   1.4   1.1 0.4  2.19        8       13
#> x[1,7]        0.3   1.0   1.0   0.8 0.3  2.10        6       13
#> x[1,8]        0.5   0.7   1.1   0.7 0.3  2.19       13       13
#> x[1,9]        0.4   0.5   1.4   0.7 0.6  1.62       12       13
#> x[1,10]       1.5   1.6   2.5   1.8 0.5  2.19       12       13
#> x[1,11]       2.0   2.7   2.9   2.6 0.4  2.08        6       13
#> x[1,12]       0.8   2.2   2.3   1.8 0.6  2.10        4       13
#> x[1,13]       0.0   1.2   1.3   0.8 0.6  2.10        7       13
#> x[1,14]      -0.9  -0.2   0.1  -0.3 0.4  2.08        6       13
#> x[1,15]      -1.0  -1.0  -0.3  -0.7 0.3  2.08        4       13
#> x[1,16]      -2.2  -2.0  -1.3  -1.8 0.4  2.10        4       13
#> x[1,17]      -2.6  -2.4  -2.0  -2.3 0.2  2.08        9       13
#> x[1,18]      -2.4  -2.0  -1.3  -1.9 0.5  1.18        8       13
#> x[1,19]      -1.9  -1.7  -0.7  -1.4 0.5  2.08        7       13
#> x[1,20]      -1.4  -1.2   0.0  -0.8 0.5  2.10        8       13
#> Z[1,1]       -0.9  -0.7  -0.6  -0.7 0.1  1.20       13       13
#> Z[2,1]       -0.7  -0.4  -0.2  -0.4 0.2  1.46       13       13
#> Z[3,1]       -0.8  -0.5  -0.4  -0.6 0.1  1.72        4       13
#> log_lik[1]   -0.7  -0.4  -0.4  -0.5 0.1  2.19       13       13
#> log_lik[2]   -2.4  -2.1  -1.8  -2.1 0.2  1.95       13       13
#> log_lik[3]   -1.0  -0.6  -0.6  -0.7 0.2  1.18        8       13
#> log_lik[4]   -0.8  -0.5  -0.4  -0.5 0.1  2.08       12       13
#> log_lik[5]   -0.8  -0.5  -0.5  -0.6 0.1  2.08       13       13
#> log_lik[6]   -0.7  -0.5  -0.4  -0.5 0.1  2.08       12       13
#> log_lik[7]   -0.7  -0.6  -0.4  -0.6 0.1  2.19       13       13
#> log_lik[8]   -2.2  -1.3  -1.2  -1.5 0.4  1.95        6       13
#> log_lik[9]   -2.4  -1.1  -1.0  -1.4 0.5  2.10        4       13
#> log_lik[10]  -0.7  -0.4  -0.4  -0.5 0.1  2.19        4       13
#> log_lik[11]  -0.7  -0.4  -0.4  -0.5 0.1  2.10        9       13
#> log_lik[12]  -0.8  -0.7  -0.3  -0.6 0.2  1.62       13       13
#> log_lik[13]  -1.3  -0.7  -0.7  -0.8 0.3  1.33        5       13
#> log_lik[14]  -0.9  -0.6  -0.4  -0.6 0.2  2.08       12       13
#> log_lik[15]  -2.5  -1.1  -0.8  -1.3 0.6  2.19       13       13
#> log_lik[16]  -0.9  -0.5  -0.4  -0.5 0.2  1.18        9       13
#> log_lik[17]  -0.7  -0.5  -0.4  -0.5 0.1  1.77        8       13
#> log_lik[18]  -0.8  -0.6  -0.4  -0.6 0.1  1.88        4       13
#> log_lik[19]  -0.9  -0.8  -0.4  -0.7 0.2  1.63        7       13
#> log_lik[20]  -0.7  -0.6  -0.5  -0.6 0.1  2.08       13       13
#> log_lik[21]  -0.8  -0.5  -0.4  -0.6 0.1  1.88       11       13
#> log_lik[22]  -2.5  -2.0  -1.6  -2.0 0.3  1.51        8       13
#> log_lik[23]  -0.8  -0.6  -0.5  -0.6 0.1  2.08       13       13
#> log_lik[24]  -0.8  -0.5  -0.4  -0.5 0.2  1.77       10       13
#> log_lik[25]  -1.6  -0.6  -0.6  -0.8 0.6  1.33        5       13
#> log_lik[26]  -0.8  -0.5  -0.5  -0.6 0.1  1.95       13       13
#> log_lik[27]  -1.1  -1.0  -0.7  -1.0 0.1  1.30        5       13
#> log_lik[28]  -1.0  -0.5  -0.4  -0.6 0.3  2.19        7       13
#> log_lik[29]  -0.9  -0.7  -0.4  -0.7 0.2  1.03       13       13
#> log_lik[30]  -1.4  -1.0  -0.4  -0.9 0.4  1.72        4       13
#> log_lik[31]  -0.8  -0.5  -0.4  -0.6 0.2  1.00        9       13
#> log_lik[32]  -3.7  -2.3  -1.1  -2.3 0.9  1.03       13       13
#> log_lik[33]  -1.2  -0.7  -0.4  -0.8 0.3  1.05        8       13
#> log_lik[34]  -2.0  -0.9  -0.5  -1.0 0.6  2.08        4       13
#> log_lik[35]  -3.9  -2.2  -1.8  -2.4 0.7  1.50        5       13
#> log_lik[36]  -1.1  -0.5  -0.4  -0.6 0.3  1.18        7       13
#> log_lik[37]  -1.0  -0.6  -0.5  -0.7 0.2  1.51        5       13
#> log_lik[38]  -2.1  -1.1  -1.0  -1.3 0.4  2.19        4       13
#> log_lik[39]  -1.0  -0.5  -0.4  -0.6 0.2  2.19        4       13
#> log_lik[40]  -1.7  -0.6  -0.6  -0.9 0.4  2.19        4       13
#> log_lik[41]  -0.8  -0.5  -0.3  -0.5 0.1  1.46       13       13
#> log_lik[42]  -0.9  -0.5  -0.3  -0.6 0.2  2.08       13       13
#> log_lik[43]  -2.5  -1.2  -1.0  -1.5 0.6  1.77       13       13
#> log_lik[44]  -0.7  -0.5  -0.3  -0.5 0.1  1.18       12       13
#> log_lik[45]  -1.1  -0.8  -0.5  -0.8 0.2  1.58        7       13
#> log_lik[46]  -1.3  -0.5  -0.4  -0.8 0.3  2.08        4       13
#> log_lik[47]  -1.5  -1.2  -0.7  -1.1 0.3  1.46       10       13
#> log_lik[48]  -1.0  -0.7  -0.5  -0.8 0.2  1.12        8       13
#> log_lik[49]  -1.1  -0.5  -0.4  -0.6 0.3  1.77        7       13
#> log_lik[50]  -5.1  -4.3  -1.7  -4.0 1.2  1.19       13       13
#> log_lik[51]  -1.3  -0.9  -0.4  -0.9 0.3  1.09        8       13
#> log_lik[52]  -1.5  -0.7  -0.4  -0.8 0.4  1.41       13       13
#> log_lik[53]  -2.4  -1.7  -1.3  -1.8 0.4  1.18       11       13
#> log_lik[54]  -1.1  -0.8  -0.5  -0.8 0.2  0.99       13       13
#> log_lik[55]  -0.9  -0.6  -0.5  -0.6 0.1  1.33       13       13
#> log_lik[56]  -4.2  -3.4  -2.3  -3.3 0.7  2.19        4        5
#> log_lik[57]  -0.8  -0.5  -0.4  -0.5 0.1  1.62       13       13
#> log_lik[58]  -0.8  -0.7  -0.5  -0.7 0.1  0.97       13       13
#> log_lik[59]  -0.7  -0.6  -0.4  -0.6 0.1  2.08       13       13
#> log_lik[60]  -0.8  -0.5  -0.5  -0.6 0.1  1.30        6       13
#> xstar[1,1]   -2.7  -0.7   1.1  -0.7 1.2  1.09       13       13
#> sigma[1]      0.6   0.6   0.8   0.7 0.1  2.08       13       13
#> lp__        -59.5 -49.2 -46.5 -51.7 5.4  2.10        4       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)
plot_trends(r)