Get the fitted values from a DFA as a data frame
dfa_fitted(modelfit, conf_level = 0.95, names = NULL)
Output from fit_dfa
.
Probability level for CI.
Optional vector of names for time series labels. Should be same length as the number of time series.
A data frame with the following columns: ID
is an identifier for each time series, time
is the time step, y
is the observed values standardized to mean 0 and unit variance, estimate
is the mean fitted value, lower
is the lower CI, and upper
is the upper CI.
predicted plot_fitted fit_dfa
# \donttest{
y <- sim_dfa(num_trends = 2, num_years = 20, num_ts = 4)
m <- fit_dfa(y = y$y_sim, num_trends = 2, iter = 50, chains = 1)
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000126 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.26 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: 0.011398 seconds (Warm-up)
#> Chain 1: 0.042844 seconds (Sampling)
#> Chain 1: 0.054242 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] -0.8 -0.8 -0.7 -0.8 0.0 2.06 4 13
#> x[2,1] -0.6 -0.6 -0.6 -0.6 0.0 2.06 4 13
#> x[1,2] -1.4 -1.4 -1.4 -1.4 0.0 2.06 3 13
#> x[2,2] -0.7 -0.6 -0.5 -0.6 0.0 2.06 4 13
#> x[1,3] -0.2 -0.2 -0.1 -0.2 0.0 2.06 3 13
#> x[2,3] 0.0 0.1 0.1 0.1 0.1 2.06 4 13
#> x[1,4] -0.3 -0.2 -0.2 -0.2 0.0 2.06 4 13
#> x[2,4] -0.8 -0.7 -0.6 -0.7 0.1 2.06 4 13
#> x[1,5] 1.6 1.6 1.6 1.6 0.0 2.06 4 13
#> x[2,5] -1.1 -0.9 -0.8 -1.0 0.1 2.06 4 13
#> x[1,6] 1.5 1.5 1.5 1.5 0.0 2.06 4 13
#> x[2,6] 0.2 0.4 0.4 0.3 0.1 2.06 4 13
#> x[1,7] -0.3 -0.2 -0.2 -0.2 0.0 2.06 4 13
#> x[2,7] -1.7 -1.4 -1.3 -1.5 0.1 2.06 3 13
#> x[1,8] -1.2 -1.1 -1.1 -1.1 0.1 2.06 3 13
#> x[2,8] -0.3 0.0 0.0 -0.1 0.1 2.06 4 13
#> x[1,9] -1.1 -1.0 -1.0 -1.0 0.1 2.06 3 13
#> x[2,9] -1.7 -1.4 -1.4 -1.5 0.1 2.06 4 13
#> x[1,10] -0.4 -0.3 -0.3 -0.3 0.0 2.06 4 13
#> x[2,10] -1.9 -1.7 -1.6 -1.7 0.1 2.06 4 13
#> x[1,11] -0.4 -0.4 -0.3 -0.4 0.0 2.06 4 13
#> x[2,11] -0.8 -0.7 -0.6 -0.7 0.1 2.06 4 13
#> x[1,12] -0.5 -0.4 -0.4 -0.4 0.0 2.06 4 13
#> x[2,12] 0.5 0.5 0.6 0.5 0.0 2.06 4 13
#> x[1,13] 0.8 0.8 0.8 0.8 0.0 2.06 4 13
#> x[2,13] 0.4 0.5 0.5 0.5 0.0 1.48 4 13
#> x[1,14] -0.9 -0.9 -0.9 -0.9 0.0 2.06 4 13
#> x[2,14] 0.3 0.4 0.4 0.4 0.0 1.24 5 13
#> x[1,15] -0.6 -0.6 -0.6 -0.6 0.0 1.87 7 13
#> x[2,15] 0.7 0.8 0.9 0.8 0.1 2.06 3 13
#> x[1,16] 0.4 0.4 0.5 0.4 0.0 1.18 7 13
#> x[2,16] 0.4 0.5 0.6 0.5 0.1 2.06 3 13
#> x[1,17] 1.7 1.7 1.8 1.7 0.0 1.07 6 13
#> x[2,17] 1.3 1.5 1.6 1.4 0.1 2.06 3 13
#> x[1,18] 1.1 1.1 1.2 1.1 0.0 1.39 4 13
#> x[2,18] -0.4 -0.3 -0.2 -0.3 0.1 2.06 3 13
#> x[1,19] -0.4 -0.4 -0.4 -0.4 0.0 1.71 4 13
#> x[2,19] 0.6 0.7 0.8 0.7 0.1 2.06 3 13
#> x[1,20] -1.0 -1.0 -1.0 -1.0 0.0 1.58 8 13
#> x[2,20] 0.0 0.2 0.3 0.2 0.1 2.06 3 13
#> Z[1,1] -99.8 -99.8 -99.8 -99.8 0.0 1.71 4 13
#> Z[2,1] -26.6 -20.0 -15.7 -21.4 4.1 2.06 3 13
#> Z[3,1] 27.5 34.0 41.9 35.3 5.4 2.06 3 13
#> Z[4,1] -31.5 -24.5 -19.8 -25.7 4.5 2.06 3 13
#> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13
#> Z[2,2] -98.9 -98.9 -98.9 -98.9 0.0 2.06 4 13
#> Z[3,2] -41.9 -33.9 -27.8 -35.3 5.1 2.06 3 13
#> Z[4,2] -11.6 -7.9 -5.7 -8.6 2.4 2.06 3 13
#> log_lik[1] -47.2 -22.0 -16.5 -28.4 11.5 2.06 3 13
#> log_lik[2] -49.3 -18.5 -13.8 -26.8 13.5 2.06 3 13
#> log_lik[3] -3.7 -3.6 -3.4 -3.6 0.1 2.06 4 13
#> log_lik[4] -10.6 -5.3 -4.4 -6.7 2.4 2.06 3 13
#> log_lik[5] -145.9 -63.1 -45.9 -84.1 37.1 2.06 3 13
#> log_lik[6] -72.8 -23.3 -15.6 -36.5 21.5 2.06 3 13
#> log_lik[7] -10.6 -6.0 -4.8 -7.2 2.2 2.06 3 13
#> log_lik[8] -22.7 -8.1 -5.8 -11.9 6.4 2.06 3 13
#> log_lik[9] -6.5 -4.6 -4.2 -5.0 0.9 2.06 3 13
#> log_lik[10] -4.0 -3.6 -3.3 -3.6 0.3 2.06 4 13
#> log_lik[11] -3.8 -3.7 -3.5 -3.7 0.1 2.06 4 13
#> log_lik[12] -3.6 -3.5 -3.5 -3.5 0.1 2.06 3 13
#> log_lik[13] -7.5 -4.8 -4.5 -5.6 1.2 2.06 3 13
#> log_lik[14] -57.3 -18.1 -12.8 -28.8 16.8 2.06 3 13
#> log_lik[15] -7.2 -4.2 -3.9 -5.0 1.2 2.06 3 13
#> log_lik[16] -5.4 -3.8 -3.8 -4.3 0.6 2.06 4 13
#> log_lik[17] -166.1 -76.8 -59.6 -100.2 39.6 2.06 3 13
#> log_lik[18] -34.0 -12.9 -11.0 -18.8 8.8 2.06 4 13
#> log_lik[19] -87.5 -24.3 -13.5 -40.8 27.7 2.06 3 13
#> log_lik[20] -11.9 -6.4 -5.2 -7.8 2.5 2.06 3 13
#> log_lik[21] -151.0 -70.0 -54.1 -91.0 35.7 2.06 3 13
#> log_lik[22] -24.2 -16.7 -13.5 -18.2 3.8 2.06 3 13
#> log_lik[23] -23.6 -7.9 -5.5 -12.0 6.8 2.06 3 13
#> log_lik[24] -19.3 -8.2 -6.0 -11.0 5.0 2.06 3 13
#> log_lik[25] -7.5 -4.9 -4.4 -5.6 1.2 2.06 3 13
#> log_lik[26] -201.0 -65.1 -45.2 -103.0 59.6 2.06 3 13
#> log_lik[27] -26.3 -8.3 -5.9 -13.1 7.7 2.06 3 13
#> log_lik[28] -7.9 -4.2 -3.9 -5.3 1.6 2.06 3 13
#> log_lik[29] -100.3 -40.5 -29.7 -56.7 26.9 2.06 3 13
#> log_lik[30] -28.2 -5.6 -4.1 -12.3 9.5 2.06 3 13
#> log_lik[31] -13.0 -7.5 -5.7 -8.8 2.7 2.06 3 13
#> log_lik[32] -14.4 -5.7 -4.6 -8.1 3.8 2.06 3 13
#> log_lik[33] -82.4 -32.2 -23.9 -46.3 22.4 2.06 3 13
#> log_lik[34] -264.1 -82.8 -55.4 -134.0 80.2 2.06 3 13
#> log_lik[35] -7.3 -4.2 -3.9 -5.0 1.3 2.06 3 13
#> log_lik[36] -22.3 -7.2 -5.2 -11.4 6.6 2.06 3 13
#> log_lik[37] -11.7 -5.9 -5.2 -7.9 2.6 2.06 3 13
#> log_lik[38] -262.0 -90.7 -66.2 -139.9 75.3 2.06 3 13
#> log_lik[39] -29.6 -9.8 -6.7 -15.0 8.6 2.06 3 13
#> log_lik[40] -9.9 -4.6 -4.0 -6.1 2.3 2.06 3 13
#> log_lik[41] -14.5 -7.1 -5.8 -9.4 3.4 2.06 3 13
#> log_lik[42] -59.9 -18.9 -14.1 -31.3 17.9 2.06 3 13
#> log_lik[43] -4.7 -3.8 -3.7 -4.0 0.4 2.06 3 13
#> log_lik[44] -6.0 -3.9 -3.8 -4.5 0.9 2.06 3 13
#> log_lik[45] -17.7 -8.5 -6.8 -11.3 4.2 2.06 3 13
#> log_lik[46] -11.0 -9.3 -8.8 -9.7 0.8 0.94 8 13
#> log_lik[47] -14.6 -7.0 -5.3 -9.0 3.4 2.06 3 13
#> log_lik[48] -3.6 -3.5 -3.4 -3.5 0.1 2.06 3 13
#> log_lik[49] -46.7 -24.6 -19.6 -29.3 10.0 2.06 3 13
#> log_lik[50] -32.5 -15.6 -11.5 -19.1 7.4 2.06 3 13
#> log_lik[51] -4.4 -3.9 -3.8 -4.1 0.3 2.06 3 13
#> log_lik[52] -9.8 -5.4 -4.5 -6.4 1.9 2.06 3 13
#> log_lik[53] -59.1 -25.9 -20.6 -36.0 14.8 2.06 3 13
#> log_lik[54] -5.4 -4.6 -4.3 -4.7 0.4 1.00 8 13
#> log_lik[55] -24.9 -9.1 -6.2 -13.2 6.9 2.06 3 13
#> log_lik[56] -6.7 -4.4 -4.1 -5.1 1.0 2.06 3 13
#> log_lik[57] -24.0 -12.4 -10.8 -16.3 5.3 2.06 3 13
#> log_lik[58] -36.8 -16.4 -11.7 -20.7 8.9 2.06 3 13
#> log_lik[59] -27.7 -9.6 -6.4 -14.4 7.9 2.06 3 13
#> log_lik[60] -3.7 -3.6 -3.4 -3.6 0.1 2.06 3 13
#> log_lik[61] -18.7 -10.2 -8.2 -11.8 3.7 2.06 3 13
#> log_lik[62] -36.8 -13.3 -8.3 -18.2 10.0 2.06 3 13
#> log_lik[63] -3.6 -3.5 -3.2 -3.4 0.2 2.06 3 13
#> log_lik[64] -6.2 -4.2 -3.9 -4.6 0.9 2.06 3 13
#> log_lik[65] -219.8 -96.5 -70.4 -126.3 54.6 2.06 3 13
#> log_lik[66] -276.2 -97.6 -60.2 -140.8 78.6 2.06 3 13
#> log_lik[67] -4.1 -4.0 -3.9 -4.0 0.1 1.71 4 13
#> log_lik[68] -38.8 -11.9 -7.2 -18.6 11.8 2.06 3 13
#> log_lik[69] -100.2 -43.9 -32.9 -57.7 24.7 2.06 3 13
#> log_lik[70] -4.8 -3.6 -3.3 -3.9 0.6 1.03 6 13
#> log_lik[71] -26.1 -11.1 -8.0 -15.1 6.9 2.06 3 13
#> log_lik[72] -10.9 -5.3 -4.4 -6.6 2.4 2.06 3 13
#> log_lik[73] -11.2 -7.6 -7.0 -8.8 1.7 2.06 3 13
#> log_lik[74] -34.4 -13.8 -9.3 -18.1 8.9 2.06 3 13
#> log_lik[75] -17.9 -7.1 -5.1 -9.7 4.7 2.06 3 13
#> log_lik[76] -3.7 -3.6 -3.1 -3.5 0.2 2.06 3 13
#> log_lik[77] -68.3 -33.2 -26.6 -43.4 16.0 2.06 3 13
#> log_lik[78] -4.0 -3.6 -3.1 -3.6 0.3 2.06 3 13
#> log_lik[79] -21.2 -7.8 -5.4 -11.3 5.9 2.06 3 13
#> log_lik[80] -8.7 -5.2 -4.6 -6.2 1.6 2.06 3 13
#> xstar[1,1] -2.6 -1.4 0.5 -1.2 1.1 0.92 13 13
#> xstar[2,1] -0.6 -0.1 1.5 0.2 0.8 1.38 9 13
#> sigma[1] 8.6 12.9 14.9 11.9 2.4 2.06 3 13
#> lp__ -15937.4 -12945.7 -12007.7 -13680.6 1464.3 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).
fitted <- dfa_fitted(m)
# }