Get the fitted values from a DFA as a data frame
dfa_fitted(modelfit, conf_level = 0.95, names = NULL)
modelfit | Output from |
---|---|
conf_level | Probability level for CI. |
names | 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 0.00013 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 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: 0.013603 seconds (Warm-up) #> Chain 1: 0.132311 seconds (Sampling) #> Chain 1: 0.145914 seconds (Total) #> Chain 1:#> Warning: There were 11 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 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] -0.5 -0.1 0.2 -0.1 0.3 1.71 4 13 #> x[2,1] 0.3 1.1 1.5 1.0 0.5 2.06 4 13 #> x[1,2] -1.0 -0.7 -0.2 -0.7 0.3 1.12 13 13 #> x[2,2] 0.5 0.9 1.1 0.9 0.2 1.09 7 13 #> x[1,3] -2.2 -1.4 -0.7 -1.5 0.5 0.95 13 13 #> x[2,3] 1.3 1.6 1.9 1.6 0.2 2.06 4 13 #> x[1,4] -1.0 -0.7 0.6 -0.4 0.6 1.87 5 13 #> x[2,4] 1.1 2.3 2.5 1.9 0.6 2.06 4 13 #> x[1,5] -0.2 0.2 1.9 0.7 0.8 2.06 6 13 #> x[2,5] 1.5 2.3 2.7 2.2 0.4 0.92 13 13 #> x[1,6] -2.0 -1.7 0.0 -1.2 0.9 2.06 6 13 #> x[2,6] 0.4 1.4 2.1 1.3 0.7 1.58 4 13 #> x[1,7] -0.6 -0.4 2.0 0.4 1.1 1.87 5 13 #> x[2,7] -0.1 0.5 1.4 0.6 0.6 1.58 5 13 #> x[1,8] -0.6 -0.4 2.0 0.4 1.1 1.87 5 13 #> x[2,8] -0.6 -0.3 0.6 -0.1 0.5 1.30 6 13 #> x[1,9] -0.3 0.6 2.7 1.0 1.1 2.06 4 13 #> x[2,9] -0.8 0.3 1.1 0.1 0.7 1.58 5 13 #> x[1,10] 0.8 1.4 3.1 1.7 0.8 1.87 5 13 #> x[2,10] -1.8 -0.9 0.0 -0.9 0.6 1.58 5 13 #> x[1,11] 0.8 1.3 3.0 1.6 0.8 2.06 7 13 #> x[2,11] -0.4 1.2 2.2 1.0 0.9 1.87 4 13 #> x[1,12] -1.0 -0.1 1.2 0.0 0.8 1.33 5 13 #> x[2,12] -1.7 -0.4 0.2 -0.5 0.7 2.06 4 13 #> x[1,13] -0.6 0.0 1.7 0.2 0.8 1.47 9 13 #> x[2,13] -2.3 -1.1 -0.4 -1.2 0.6 1.25 7 13 #> x[1,14] -0.5 0.7 1.3 0.6 0.6 1.16 13 13 #> x[2,14] -1.7 -1.1 -0.4 -1.0 0.5 1.09 13 13 #> x[1,15] -0.7 1.1 1.6 0.9 1.0 1.13 13 13 #> x[2,15] -1.4 -0.8 0.1 -0.7 0.5 1.14 7 13 #> x[1,16] -1.0 1.2 1.7 0.9 1.4 1.20 9 13 #> x[2,16] -2.4 -1.8 -1.3 -1.8 0.4 0.95 13 13 #> x[1,17] -1.1 1.4 2.0 1.1 1.6 1.19 8 13 #> x[2,17] -3.6 -2.9 -2.2 -2.9 0.5 1.05 9 13 #> x[1,18] -2.0 1.0 1.9 0.6 1.9 1.87 4 13 #> x[2,18] -2.9 -2.2 -1.1 -2.1 0.7 1.16 6 13 #> x[1,19] -2.6 0.6 1.3 -0.1 1.9 2.06 4 13 #> x[2,19] -3.9 -2.9 -2.1 -3.0 0.7 1.13 8 13 #> x[1,20] -1.6 1.5 2.7 1.1 1.9 2.06 4 13 #> x[2,20] -3.2 -2.1 -1.3 -2.2 0.7 1.13 8 13 #> Z[1,1] -1.9 0.4 0.7 0.0 1.5 2.06 4 13 #> Z[2,1] -0.6 0.1 0.3 0.0 0.3 1.32 7 13 #> Z[3,1] -0.6 0.3 0.6 0.2 0.4 1.24 10 13 #> Z[4,1] -0.3 0.1 0.6 0.1 0.3 1.19 13 13 #> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13 #> Z[2,2] -0.7 -0.3 0.0 -0.3 0.3 1.58 13 13 #> Z[3,2] 0.2 0.4 0.8 0.4 0.3 0.96 11 13 #> Z[4,2] -0.6 -0.3 0.3 -0.3 0.4 1.19 13 13 #> log_lik[1] -3.4 -1.4 -1.2 -1.8 1.3 0.95 13 13 #> log_lik[2] -3.1 -0.8 -0.6 -1.2 1.5 1.12 13 13 #> log_lik[3] -3.2 -0.7 -0.5 -1.2 1.5 2.06 9 13 #> log_lik[4] -3.4 -1.3 -0.9 -1.7 1.3 1.15 7 13 #> log_lik[5] -4.3 -1.9 -1.3 -2.3 1.2 1.19 6 13 #> log_lik[6] -3.7 -1.5 -0.9 -1.7 1.3 1.06 13 13 #> log_lik[7] -3.3 -0.8 -0.6 -1.3 1.5 1.07 13 13 #> log_lik[8] -4.1 -2.2 -1.8 -2.5 1.1 2.06 13 13 #> log_lik[9] -4.0 -1.2 -0.6 -1.8 1.4 1.47 5 13 #> log_lik[10] -4.0 -1.2 -0.5 -1.6 1.5 1.01 13 13 #> log_lik[11] -4.8 -1.1 -0.6 -1.6 1.6 0.99 11 13 #> log_lik[12] -3.5 -1.4 -0.6 -1.6 1.4 0.94 13 13 #> log_lik[13] -3.6 -1.0 -0.8 -1.5 1.4 1.71 5 13 #> log_lik[14] -3.5 -0.7 -0.6 -1.2 1.5 2.06 13 13 #> log_lik[15] -3.5 -0.9 -0.6 -1.4 1.4 1.71 13 13 #> log_lik[16] -3.2 -0.8 -0.6 -1.2 1.5 1.05 11 13 #> log_lik[17] -4.9 -2.3 -1.9 -2.8 1.2 1.45 13 13 #> log_lik[18] -3.4 -0.9 -0.6 -1.4 1.4 1.58 13 13 #> log_lik[19] -3.5 -1.0 -0.5 -1.4 1.5 1.30 13 13 #> log_lik[20] -4.2 -1.4 -0.7 -1.7 1.4 1.37 13 13 #> log_lik[21] -3.8 -1.3 -0.5 -1.6 1.4 1.09 10 13 #> log_lik[22] -3.3 -0.8 -0.5 -1.3 1.5 0.92 13 13 #> log_lik[23] -4.1 -1.3 -0.7 -1.7 1.4 0.96 13 13 #> log_lik[24] -4.3 -2.0 -1.0 -2.1 1.4 1.38 13 13 #> log_lik[25] -3.8 -1.0 -0.8 -1.6 1.4 1.58 4 13 #> log_lik[26] -3.1 -0.7 -0.6 -1.2 1.5 1.24 7 13 #> log_lik[27] -3.4 -1.0 -0.6 -1.4 1.4 1.71 13 13 #> log_lik[28] -3.3 -0.7 -0.6 -1.2 1.5 1.33 5 13 #> log_lik[29] -4.6 -1.4 -1.2 -2.1 1.4 2.06 4 13 #> log_lik[30] -4.5 -1.7 -1.2 -2.0 1.3 1.21 6 13 #> log_lik[31] -3.8 -0.9 -0.6 -1.4 1.5 0.98 13 13 #> log_lik[32] -4.0 -1.0 -0.8 -1.6 1.4 1.19 6 13 #> log_lik[33] -4.2 -1.5 -1.0 -2.0 1.4 1.58 4 13 #> log_lik[34] -4.7 -1.7 -1.1 -2.1 1.4 1.21 13 13 #> log_lik[35] -7.6 -2.6 -1.6 -3.4 2.3 1.18 8 13 #> log_lik[36] -3.6 -0.8 -0.6 -1.3 1.5 2.06 4 13 #> log_lik[37] -3.1 -0.8 -0.6 -1.2 1.5 1.00 13 13 #> log_lik[38] -4.5 -1.4 -0.8 -1.8 1.5 1.06 13 13 #> log_lik[39] -10.9 -5.5 -2.7 -5.9 3.8 1.16 9 13 #> log_lik[40] -3.1 -0.7 -0.6 -1.2 1.5 2.06 4 13 #> log_lik[41] -3.1 -0.6 -0.6 -1.1 1.5 1.32 13 13 #> log_lik[42] -3.7 -1.2 -0.7 -1.6 1.4 1.15 11 13 #> log_lik[43] -3.8 -0.9 -0.6 -1.4 1.5 1.04 13 13 #> log_lik[44] -4.1 -1.6 -0.8 -2.0 1.3 1.15 13 13 #> log_lik[45] -3.5 -1.2 -0.6 -1.5 1.4 1.03 13 13 #> log_lik[46] -3.1 -0.9 -0.6 -1.3 1.4 1.48 13 13 #> log_lik[47] -3.1 -0.7 -0.6 -1.1 1.5 1.04 13 13 #> log_lik[48] -3.2 -0.9 -0.5 -1.3 1.5 1.58 13 13 #> log_lik[49] -4.0 -1.7 -1.2 -2.1 1.3 1.12 13 13 #> log_lik[50] -3.9 -1.9 -0.9 -2.0 1.3 2.06 13 13 #> log_lik[51] -4.8 -2.0 -1.2 -2.5 1.3 0.95 13 13 #> log_lik[52] -3.5 -1.2 -0.8 -1.6 1.4 1.24 13 13 #> log_lik[53] -3.1 -0.7 -0.5 -1.1 1.5 2.06 13 13 #> log_lik[54] -3.1 -0.7 -0.5 -1.1 1.5 1.30 13 13 #> log_lik[55] -3.1 -0.7 -0.6 -1.1 1.5 1.30 13 13 #> log_lik[56] -3.1 -0.6 -0.5 -1.1 1.5 1.71 13 13 #> log_lik[57] -3.2 -0.8 -0.6 -1.2 1.5 2.06 4 13 #> log_lik[58] -3.5 -0.9 -0.6 -1.4 1.4 1.87 4 13 #> log_lik[59] -3.2 -0.8 -0.6 -1.3 1.5 1.00 13 13 #> log_lik[60] -3.1 -0.7 -0.6 -1.2 1.5 1.37 6 13 #> log_lik[61] -3.2 -0.7 -0.6 -1.2 1.5 2.06 4 13 #> log_lik[62] -3.1 -1.0 -0.5 -1.2 1.5 1.06 13 13 #> log_lik[63] -3.5 -1.1 -0.7 -1.5 1.4 1.20 13 13 #> log_lik[64] -3.1 -0.8 -0.6 -1.2 1.5 1.00 13 13 #> log_lik[65] -3.4 -1.0 -0.6 -1.4 1.4 2.06 4 13 #> log_lik[66] -3.1 -0.9 -0.6 -1.2 1.5 1.48 5 13 #> log_lik[67] -3.3 -1.0 -0.6 -1.4 1.4 2.06 4 13 #> log_lik[68] -3.3 -0.7 -0.6 -1.2 1.5 1.45 13 13 #> log_lik[69] -3.5 -0.9 -0.6 -1.5 1.4 2.06 4 13 #> log_lik[70] -3.4 -1.1 -0.6 -1.4 1.4 0.98 13 13 #> log_lik[71] -3.4 -1.2 -0.6 -1.5 1.4 1.12 11 13 #> log_lik[72] -3.1 -0.7 -0.6 -1.1 1.5 1.24 6 13 #> log_lik[73] -3.8 -1.3 -0.8 -1.8 1.4 2.06 4 13 #> log_lik[74] -3.4 -0.7 -0.5 -1.2 1.5 1.58 13 13 #> log_lik[75] -3.2 -0.8 -0.6 -1.2 1.5 1.05 11 13 #> log_lik[76] -3.9 -0.8 -0.6 -1.4 1.5 1.37 13 13 #> log_lik[77] -3.2 -0.8 -0.6 -1.3 1.5 1.30 6 13 #> log_lik[78] -3.6 -1.2 -0.6 -1.4 1.4 1.24 13 13 #> log_lik[79] -4.8 -1.4 -0.8 -1.9 1.5 0.94 13 13 #> log_lik[80] -3.1 -0.7 -0.6 -1.2 1.5 1.58 13 13 #> xstar[1,1] -2.2 2.0 3.5 1.5 2.2 2.06 4 13 #> xstar[2,1] -4.0 -2.2 -0.6 -2.3 1.1 1.04 11 13 #> sigma[1] 0.7 0.8 68.8 13.8 47.0 1.71 13 13 #> lp__ -277.2 -83.7 -77.0 -119.4 117.0 1.87 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).fitted <- dfa_fitted(m) # }