plot_loadings(
rotated_modelfit,
names = NULL,
facet = TRUE,
violin = TRUE,
conf_level = 0.95,
threshold = NULL
)

## Arguments

rotated_modelfit Output from rotate_trends(). An optional vector of names for plotting the loadings. Logical. Should there be a separate facet for each trend? Defaults to TRUE. Logical. Should the full posterior densities be shown as a violin plot? Defaults to TRUE. Confidence level for credible intervals. Defaults to 0.95. Numeric (0-1). Optional for plots, if included, only plot loadings who have Pr(<0) or Pr(>0) > threshold. For example threshold = 0.8 would only display estimates where 80% of posterior density was above/below zero. Defaults to NULL (not used).

plot_trends fit_dfa rotate_trends

## Examples

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 3.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.33 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: 0.024212 seconds (Warm-up)
#> Chain 1:                0.292711 seconds (Sampling)
#> Chain 1:                0.316923 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]        -1.5  -0.7   0.8  -0.6  0.8  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   0.2   0.9  -0.3  1.1  2.06        4       13
#> x[2,2]         0.2   0.5   1.2   0.6  0.4  1.16       12       13
#> x[1,3]        -1.6   0.0   0.8  -0.4  1.0  2.06        4       13
#> x[2,3]        -1.4  -0.2   0.3  -0.4  0.6  2.06        4       13
#> x[1,4]        -1.5  -0.4   0.7  -0.4  0.9  2.06        4       13
#> x[2,4]        -0.1   0.6   1.3   0.6  0.5  1.06       11       13
#> x[1,5]        -0.7  -0.2   0.7  -0.1  0.5  0.95       11       13
#> x[2,5]        -1.4  -0.6   1.5  -0.3  1.2  1.87        6       13
#> x[1,6]        -0.9  -0.3   0.3  -0.3  0.5  1.16        6       13
#> x[2,6]        -0.2   0.1   0.6   0.1  0.3  1.45       10       13
#> x[1,7]        -2.2  -1.2   0.3  -1.1  0.9  1.71        4       13
#> x[2,7]        -1.1  -0.1   0.9  -0.1  0.6  1.47       13       13
#> x[1,8]        -1.7  -0.8   0.5  -0.7  0.8  1.32        5       13
#> x[2,8]        -1.1   0.2   1.4   0.2  0.8  1.71       13       13
#> x[1,9]        -1.3  -0.2   0.7  -0.2  0.6  1.15        8       13
#> x[2,9]        -1.2   0.5   1.2   0.2  0.9  2.06        9       13
#> x[1,10]       -1.4  -0.7   0.2  -0.7  0.6  1.48        5       13
#> x[2,10]       -1.0  -0.4   0.3  -0.4  0.4  0.99       13       13
#> Z[1,1]        -3.6   0.3   3.6   0.2  2.4  2.06       13       13
#> Z[2,1]        -0.5   0.5   1.4   0.3  0.7  0.95       13       13
#> Z[3,1]        -1.5  -0.5   1.0  -0.4  0.9  1.87        4       13
#> Z[4,1]        -0.8   0.1   1.9   0.4  0.9  1.20        6       13
#> Z[1,2]         0.0   0.0   0.0   0.0  0.0  1.00       13       13
#> Z[2,2]        -4.2   1.0   6.2   0.6  3.9  1.18       13       13
#> Z[3,2]        -1.7  -0.7   0.5  -0.6  0.8  1.06        8       13
#> Z[4,2]        -1.2   0.9   1.8   0.6  1.1  0.97       13       13
#> log_lik[1]    -4.3  -1.5  -0.6  -2.0  1.5  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  -0.6  -1.9  1.5  2.06        4       13
#> log_lik[4]    -4.3  -1.1  -0.5  -2.0  1.5  2.06        4       13
#> log_lik[5]    -4.3  -1.2  -0.8  -2.0  1.4  2.06        4       13
#> log_lik[6]    -4.3  -1.3  -0.6  -2.0  1.4  1.71        4       13
#> log_lik[7]    -4.3  -1.3  -0.6  -2.0  1.4  1.87        4       13
#> log_lik[8]    -4.7  -1.4  -0.5  -2.3  1.8  1.87        4       13
#> log_lik[9]    -5.7  -3.5  -2.2  -3.6  1.3  0.98       13       13
#> log_lik[10]   -4.3  -2.2  -0.5  -2.5  1.5  1.24        5       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  -0.6  -2.1  1.4  1.47        4       13
#> log_lik[13]   -4.3  -1.4  -0.6  -2.1  1.5  1.87        4       13
#> log_lik[14]   -4.3  -1.5  -0.5  -2.0  1.5  1.87        4       13
#> log_lik[15]   -4.3  -1.5  -0.6  -2.0  1.4  2.06        3       13
#> log_lik[16]   -4.3  -1.5  -0.6  -2.2  1.5  1.87        4       13
#> log_lik[17]   -4.3  -1.3  -0.5  -1.8  1.5  2.06        3       13
#> log_lik[18]   -4.4  -2.0  -0.5  -2.1  1.5  2.06        3       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  -0.5  -2.0  1.4  2.06        4       13
#> log_lik[21]   -4.3  -1.1  -0.5  -1.9  1.5  2.06        3       13
#> log_lik[22]   -4.3  -1.2  -0.6  -1.9  1.4  2.06        4       13
#> log_lik[23]   -4.3  -1.3  -0.7  -1.9  1.4  1.87        4       13
#> log_lik[24]   -4.3  -1.3  -0.7  -1.9  1.4  2.06        4       13
#> log_lik[25]   -4.4  -2.2  -0.6  -2.3  1.6  1.87        4       13
#> log_lik[26]   -4.3  -1.5  -0.7  -2.1  1.4  1.58        4       13
#> log_lik[27]   -4.3  -1.6  -0.6  -2.1  1.4  2.06        4       13
#> log_lik[28]   -4.3  -1.5  -0.9  -2.1  1.4  2.06        4       13
#> log_lik[29]   -4.3  -1.5  -0.9  -2.3  1.3  1.47        4       13
#> log_lik[30]   -4.3  -1.3  -0.7  -2.0  1.4  1.71        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  -0.6  -1.9  1.4  1.87        4       13
#> log_lik[33]   -4.3  -1.7  -1.0  -2.2  1.2  1.58        4       13
#> log_lik[34]   -4.3  -1.6  -0.7  -2.1  1.4  2.06        4       13
#> log_lik[35]   -4.3  -1.0  -0.5  -1.8  1.5  2.06        3       13
#> log_lik[36]   -4.3  -1.0  -0.6  -1.9  1.5  2.06        4       13
#> log_lik[37]   -4.3  -2.0  -0.7  -2.1  1.4  2.06        4       13
#> log_lik[38]   -4.3  -1.2  -0.5  -1.8  1.5  2.06        3       13
#> log_lik[39]   -4.3  -1.7  -0.6  -2.0  1.3  1.87        4       13
#> log_lik[40]   -4.3  -1.3  -0.6  -2.0  1.4  2.06        4       13
#> xstar[1,1]    -2.1  -0.5   1.3  -0.6  1.2  1.00        9       13
#> xstar[2,1]    -2.0  -0.4   1.2  -0.3  1.2  1.12        8       13
#> sigma[1]       0.6   1.0  29.7   7.5 11.8  2.06        3       13
#> lp__        -203.1 -43.1 -19.4 -78.8 72.9  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)
plot_loadings(r, violin = FALSE, facet = TRUE)
plot_loadings(r, violin = FALSE, facet = FALSE)
plot_loadings(r, violin = TRUE, facet = FALSE)
plot_loadings(r, violin = TRUE, facet = TRUE)