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 0.000116 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.16 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: 0.006 seconds (Warm-up)
#> Chain 1: 0.013 seconds (Sampling)
#> Chain 1: 0.019 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] 0.1 0.2 0.2 0.2 0.0 2.06 3 13
#> x[2,1] 0.4 0.5 0.5 0.4 0.0 1.48 4 13
#> x[1,2] 0.2 0.4 0.4 0.3 0.1 2.06 3 13
#> x[2,2] 1.3 1.5 1.5 1.4 0.1 1.87 4 13
#> x[1,3] -0.1 0.1 0.1 0.1 0.1 2.06 3 13
#> x[2,3] 1.1 1.3 1.3 1.2 0.1 2.06 4 13
#> x[1,4] 1.0 1.2 1.2 1.1 0.1 2.06 3 13
#> x[2,4] 1.5 1.7 1.7 1.6 0.1 2.06 4 13
#> x[1,5] 2.5 2.8 2.9 2.8 0.1 2.06 3 13
#> x[2,5] 1.0 1.2 1.3 1.2 0.1 2.06 4 13
#> x[1,6] 1.2 1.5 1.5 1.4 0.1 2.06 3 13
#> x[2,6] 0.0 0.3 0.3 0.2 0.1 2.06 4 13
#> x[1,7] 2.8 3.1 3.1 3.0 0.1 2.06 3 13
#> x[2,7] -0.2 0.0 0.1 0.0 0.1 2.06 4 13
#> x[1,8] 1.0 1.3 1.3 1.2 0.1 2.06 3 13
#> x[2,8] 0.3 0.5 0.5 0.4 0.1 2.06 4 13
#> x[1,9] 0.0 0.3 0.3 0.2 0.1 2.06 3 13
#> x[2,9] -1.7 -1.5 -1.5 -1.6 0.1 2.06 4 13
#> x[1,10] 1.4 1.5 1.5 1.5 0.1 2.06 3 13
#> x[2,10] -1.6 -1.3 -1.3 -1.4 0.1 2.06 4 13
#> x[1,11] 0.8 0.9 0.9 0.9 0.0 2.06 3 13
#> x[2,11] -1.6 -1.3 -1.3 -1.4 0.1 2.06 4 13
#> x[1,12] -0.5 -0.5 -0.4 -0.5 0.0 1.19 11 13
#> x[2,12] -2.0 -1.7 -1.6 -1.7 0.2 2.06 4 13
#> x[1,13] -1.3 -1.3 -1.2 -1.3 0.0 2.06 4 13
#> x[2,13] -0.9 -0.5 -0.5 -0.6 0.2 2.06 4 13
#> x[1,14] -1.3 -1.3 -1.2 -1.3 0.1 2.06 3 13
#> x[2,14] -0.7 -0.3 -0.3 -0.4 0.2 2.06 4 13
#> x[1,15] 0.4 0.4 0.6 0.4 0.1 2.06 3 13
#> x[2,15] -0.1 0.3 0.4 0.2 0.2 2.06 4 13
#> x[1,16] 0.0 0.0 0.3 0.1 0.1 2.06 3 13
#> x[2,16] 1.1 1.7 1.7 1.5 0.3 2.06 4 13
#> x[1,17] -1.2 -1.2 -0.9 -1.1 0.1 2.06 3 13
#> x[2,17] 2.2 2.8 2.8 2.6 0.3 2.06 3 13
#> x[1,18] -1.9 -1.9 -1.6 -1.8 0.2 2.06 3 13
#> x[2,18] 0.9 1.5 1.5 1.3 0.3 2.06 3 13
#> x[1,19] -1.1 -1.1 -0.7 -1.0 0.2 2.06 3 13
#> x[2,19] 0.9 1.5 1.5 1.3 0.3 2.06 3 13
#> x[1,20] -1.2 -1.1 -0.8 -1.0 0.2 2.06 3 13
#> x[2,20] -0.1 0.5 0.6 0.3 0.3 2.06 3 13
#> Z[1,1] -98.5 -98.5 -98.5 -98.5 0.0 1.87 4 13
#> Z[2,1] -39.9 -39.6 -34.8 -38.4 2.1 1.87 4 13
#> Z[3,1] 13.2 15.8 15.9 15.1 1.1 1.87 4 13
#> Z[4,1] 53.5 57.1 57.3 56.1 1.6 2.06 3 13
#> Z[1,2] 0.0 0.0 0.0 0.0 0.0 1.00 13 13
#> Z[2,2] -93.5 -93.5 -93.3 -93.5 0.1 2.06 4 13
#> Z[3,2] -40.4 -40.2 -36.3 -39.2 1.7 2.06 4 13
#> Z[4,2] -40.9 -40.7 -36.9 -39.7 1.7 2.06 4 13
#> log_lik[1] -12.9 -11.3 -4.0 -9.6 3.8 2.06 3 13
#> log_lik[2] -72.7 -63.6 -12.9 -51.2 26.0 2.06 3 13
#> log_lik[3] -10.7 -9.7 -4.4 -8.4 2.8 2.06 3 13
#> log_lik[4] -5.3 -5.1 -3.8 -4.8 0.7 2.06 4 13
#> log_lik[5] -45.3 -39.0 -6.7 -31.3 16.6 2.06 3 13
#> log_lik[6] -661.6 -581.0 -109.2 -465.2 239.5 2.06 3 13
#> log_lik[7] -86.6 -76.2 -16.2 -61.6 30.5 2.06 3 13
#> log_lik[8] -47.7 -42.8 -12.1 -35.1 15.5 2.06 3 13
#> log_lik[9] -8.2 -6.6 -3.1 -6.1 2.1 2.06 4 13
#> log_lik[10] -453.0 -393.0 -64.3 -313.2 168.5 2.06 3 13
#> log_lik[11] -75.9 -66.5 -13.8 -53.7 26.9 2.06 3 13
#> log_lik[12] -59.8 -53.9 -15.2 -44.2 19.5 2.06 3 13
#> log_lik[13] -407.5 -353.7 -57.4 -282.0 151.4 2.06 3 13
#> log_lik[14] -1171.1 -1018.3 -173.9 -813.3 432.3 2.06 3 13
#> log_lik[15] -69.9 -60.9 -13.3 -49.4 24.6 2.06 3 13
#> log_lik[16] -3.2 -2.4 -2.4 -2.6 0.3 2.06 4 13
#> log_lik[17] -2212.5 -1947.9 -379.0 -1562.9 794.7 2.06 3 13
#> log_lik[18] -1511.6 -1311.1 -208.2 -1043.8 564.4 2.06 3 13
#> log_lik[19] -3.6 -3.4 -3.1 -3.4 0.2 2.06 4 13
#> log_lik[20] -352.2 -310.7 -60.8 -249.4 126.2 2.06 3 13
#> log_lik[21] -601.4 -521.2 -80.8 -414.7 225.2 2.06 3 13
#> log_lik[22] -218.1 -180.3 -15.5 -142.4 87.4 2.06 3 13
#> log_lik[23] -5.6 -5.5 -4.1 -5.2 0.6 1.58 4 13
#> log_lik[24] -153.2 -134.8 -25.2 -108.0 55.3 2.06 3 13
#> log_lik[25] -2598.8 -2292.8 -454.9 -1840.8 930.0 2.06 3 13
#> log_lik[26] -465.7 -391.7 -40.3 -309.0 183.7 2.06 3 13
#> log_lik[27] -61.5 -56.1 -14.2 -45.6 20.4 2.06 3 13
#> log_lik[28] -847.7 -750.8 -147.9 -602.0 303.4 2.06 3 13
#> log_lik[29] -448.9 -389.3 -62.0 -310.3 167.1 2.06 3 13
#> log_lik[30] -271.0 -224.7 -23.9 -178.6 106.5 2.06 3 13
#> log_lik[31] -3.2 -2.4 -2.4 -2.7 0.4 2.06 4 13
#> log_lik[32] -76.7 -68.4 -14.2 -55.0 27.0 2.06 3 13
#> log_lik[33] -21.9 -17.8 -3.2 -14.6 8.0 2.06 3 13
#> log_lik[34] -454.7 -430.2 -156.8 -358.1 130.1 2.06 3 13
#> log_lik[35] -115.9 -105.8 -27.1 -85.8 38.5 2.06 3 13
#> log_lik[36] -163.8 -146.4 -29.5 -117.3 58.0 2.06 3 13
#> log_lik[37] -638.5 -563.2 -112.4 -452.8 227.3 2.06 3 13
#> log_lik[38] -104.8 -103.5 -60.5 -92.0 19.0 1.71 6 13
#> log_lik[39] -167.5 -152.6 -36.7 -123.4 56.6 2.06 3 13
#> log_lik[40] -561.7 -503.0 -107.0 -404.4 196.8 2.06 3 13
#> log_lik[41] -208.5 -183.6 -38.7 -148.5 73.0 2.06 3 13
#> log_lik[42] -204.4 -199.3 -89.9 -169.9 50.0 2.06 3 13
#> log_lik[43] -124.7 -114.7 -31.0 -93.5 40.5 2.06 3 13
#> log_lik[44] -299.5 -270.8 -64.2 -219.2 101.6 2.06 3 13
#> log_lik[45] -59.7 -54.3 -16.0 -44.4 19.4 2.06 3 13
#> log_lik[46] -789.5 -737.5 -236.4 -607.9 240.4 2.06 3 13
#> log_lik[47] -96.5 -90.1 -28.4 -74.3 29.5 2.06 3 13
#> log_lik[48] -47.5 -45.2 -16.6 -37.8 13.1 2.06 4 13
#> log_lik[49] -455.3 -405.0 -90.7 -326.5 159.2 2.06 3 13
#> log_lik[50] -259.0 -250.1 -92.7 -208.0 72.2 2.06 3 13
#> log_lik[51] -4.9 -2.5 -2.4 -3.2 1.1 2.06 4 13
#> log_lik[52] -80.3 -66.3 -9.4 -53.3 30.7 2.06 3 13
#> log_lik[53] -492.5 -435.5 -86.7 -348.7 177.0 2.06 3 13
#> log_lik[54] -171.6 -168.8 -71.1 -142.2 43.6 2.06 3 13
#> log_lik[55] -4.9 -3.9 -3.8 -4.1 0.5 2.06 4 13
#> log_lik[56] -117.7 -97.2 -11.1 -77.4 46.1 2.06 3 13
#> log_lik[57] -35.3 -33.5 -20.6 -30.5 6.0 2.06 4 13
#> log_lik[58] -66.0 -48.5 -3.3 -40.1 26.4 2.06 4 13
#> log_lik[59] -4.5 -3.9 -3.1 -3.8 0.6 1.32 10 13
#> log_lik[60] -12.8 -5.0 -3.9 -6.8 3.7 2.06 4 13
#> log_lik[61] -7.6 -2.6 -2.4 -4.1 2.4 2.06 4 13
#> log_lik[62] -714.6 -602.2 -84.4 -480.7 273.1 2.06 3 13
#> log_lik[63] -128.8 -105.7 -11.2 -84.3 50.8 2.06 3 13
#> log_lik[64] -130.3 -105.0 -7.2 -83.0 53.0 2.06 3 13
#> log_lik[65] -384.3 -333.2 -47.0 -263.0 147.1 2.06 3 13
#> log_lik[66] -1252.1 -1071.4 -176.1 -857.8 467.0 2.06 3 13
#> log_lik[67] -476.0 -403.2 -50.3 -319.8 184.2 2.06 3 13
#> log_lik[68] -931.7 -793.9 -99.4 -627.6 361.0 2.06 3 13
#> log_lik[69] -998.0 -873.1 -147.7 -694.6 370.6 2.06 3 13
#> log_lik[70] -115.9 -90.0 -6.3 -72.3 46.9 2.06 4 13
#> log_lik[71] -231.4 -192.1 -18.6 -151.9 92.0 2.06 3 13
#> log_lik[72] -831.3 -705.2 -84.5 -557.2 324.1 2.06 3 13
#> log_lik[73] -325.0 -279.0 -33.1 -219.3 127.2 2.06 3 13
#> log_lik[74] -279.1 -226.2 -21.1 -180.0 111.4 2.06 3 13
#> log_lik[75] -173.7 -142.4 -12.6 -112.8 69.6 2.06 3 13
#> log_lik[76] -435.9 -362.5 -32.1 -285.3 174.9 2.06 3 13
#> log_lik[77] -373.8 -321.1 -39.8 -252.8 145.5 2.06 3 13
#> log_lik[78] -14.8 -3.3 -2.5 -6.2 5.3 2.06 10 13
#> log_lik[79] -48.7 -37.5 -3.3 -30.5 19.4 2.06 3 13
#> log_lik[80] -228.0 -184.8 -11.5 -145.5 93.4 2.06 3 13
#> xstar[1,1] -3.1 -0.9 0.8 -1.0 1.3 1.02 13 13
#> xstar[2,1] -0.1 0.8 1.8 0.9 0.7 1.09 13 13
#> sigma[1] 4.3 4.5 9.2 5.7 2.1 2.06 3 13
#> lp__ -40283.6 -36850.0 -17092.2 -32041.0 10029.9 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).
fitted <- dfa_fitted(m)
# }