A generic wrapper around posterior::summarise_draws()
with
opinionated defaults.
Usage
enw_posterior(fit, variables = NULL, probs = c(0.05, 0.2, 0.8, 0.95), ...)
Arguments
- fit
A
cmdstanr
fit object.- variables
A character vector of variables to return posterior summaries for. By default summaries for all parameters are returned.
- probs
A vector of numeric probabilities to produce quantile summaries for. By default these are the 5%, 20%, 80%, and 95% quantiles which are also the minimum set required for plotting functions to work.
- ...
Additional arguments that may be passed but will not be used.
See also
Functions used for postprocessing of model fits
enw_add_latest_obs_to_nowcast()
,
enw_nowcast_samples()
,
enw_nowcast_summary()
,
enw_pp_summary()
,
enw_quantiles_to_long()
,
enw_summarise_samples()
Examples
fit <- enw_example("nowcast")
enw_posterior(fit$fit[[1]], variables = "expr_beta")
#> variable mean median sd mad q5
#> <char> <num> <num> <num> <num> <num>
#> 1: expr_beta[1] 0.32247472 0.338387000 0.5599104 0.5401784 -0.5884213
#> 2: expr_beta[2] -0.33517948 -0.329458500 0.5515760 0.5133994 -1.2521760
#> 3: expr_beta[3] -0.82458037 -0.830468000 0.5664859 0.5410890 -1.7149765
#> 4: expr_beta[4] 0.50017845 0.507985500 0.5548193 0.5656964 -0.4416455
#> 5: expr_beta[5] -0.49211128 -0.501705500 0.5506490 0.5623101 -1.3512810
#> 6: expr_beta[6] -0.21819429 -0.206166000 0.5510473 0.5370378 -1.1559205
#> 7: expr_beta[7] 1.71806636 1.729265000 0.5462492 0.5460119 0.8004145
#> 8: expr_beta[8] 0.32300834 0.307873500 0.5308181 0.5497788 -0.5055089
#> 9: expr_beta[9] 0.26073828 0.282569500 0.5012190 0.4990157 -0.6089277
#> 10: expr_beta[10] -0.37001865 -0.382536500 0.5316492 0.5411119 -1.2536980
#> 11: expr_beta[11] -0.16254693 -0.149461500 0.5293063 0.5118632 -1.0445990
#> 12: expr_beta[12] -0.57731811 -0.573319500 0.5481950 0.5329161 -1.4757775
#> 13: expr_beta[13] -1.19988748 -1.213040000 0.5883767 0.6189744 -2.1268635
#> 14: expr_beta[14] 1.53836898 1.563060000 0.5998243 0.5683769 0.4959207
#> 15: expr_beta[15] 1.77050210 1.748155000 0.5322216 0.5316604 0.9741486
#> 16: expr_beta[16] -0.23216867 -0.258784500 0.4707397 0.4703319 -0.9805193
#> 17: expr_beta[17] -0.21833087 -0.242596000 0.4769162 0.4754961 -0.9622282
#> 18: expr_beta[18] -0.19196526 -0.195510000 0.4794046 0.5002166 -0.9928552
#> 19: expr_beta[19] -0.57075420 -0.556655500 0.5066780 0.4952225 -1.3583375
#> 20: expr_beta[20] -0.34326445 -0.337585500 0.5160784 0.5057089 -1.2166530
#> 21: expr_beta[21] 1.65891978 1.651225000 0.5281567 0.5072419 0.7690231
#> 22: expr_beta[22] 0.42427424 0.412209500 0.4715924 0.4421669 -0.3293036
#> 23: expr_beta[23] -0.28568970 -0.295981000 0.4512625 0.4222486 -1.0215700
#> 24: expr_beta[24] -0.32155423 -0.324554500 0.4691444 0.4423797 -1.1283835
#> 25: expr_beta[25] -0.09773168 -0.120236000 0.4602765 0.4401335 -0.8718705
#> 26: expr_beta[26] -0.88791656 -0.865780000 0.4925783 0.4473286 -1.7588625
#> 27: expr_beta[27] -0.66798334 -0.659061000 0.5198224 0.5064465 -1.5324715
#> 28: expr_beta[28] 2.08750148 2.055500000 0.5381331 0.5322608 1.2505040
#> 29: expr_beta[29] 1.61456236 1.605955000 0.5373865 0.5060633 0.7811348
#> 30: expr_beta[30] 0.11775303 0.100548500 0.4773478 0.4689397 -0.6268959
#> 31: expr_beta[31] -0.65807533 -0.642011000 0.4920458 0.4700153 -1.4376195
#> 32: expr_beta[32] -0.24204215 -0.240013000 0.4854801 0.4898703 -1.0065790
#> 33: expr_beta[33] -0.63662301 -0.630302500 0.5123658 0.4981299 -1.4756840
#> 34: expr_beta[34] -0.13428000 -0.133333500 0.5681001 0.5320414 -1.0641440
#> 35: expr_beta[35] 1.45873527 1.446325000 0.5648977 0.5513641 0.5702454
#> 36: expr_beta[36] 0.17596728 0.167394000 0.5543489 0.5432009 -0.6964099
#> 37: expr_beta[37] 0.01502973 0.008104375 0.5782420 0.5842489 -0.8798406
#> 38: expr_beta[38] 0.10388639 0.071395650 0.6512795 0.6383030 -0.9309701
#> 39: expr_beta[39] 0.18580796 0.168813500 0.6895821 0.6621166 -0.9171666
#> 40: expr_beta[40] 0.39259101 0.377317500 0.7767691 0.7296290 -0.8495955
#> variable mean median sd mad q5
#> q20 q80 q95 rhat ess_bulk ess_tail
#> <num> <num> <num> <num> <num> <num>
#> 1: -0.14270780 0.78065200 1.20346400 1.0021603 714.6681 609.9378
#> 2: -0.77719040 0.10379780 0.54302050 1.0036855 1262.6375 504.0825
#> 3: -1.29391200 -0.36525580 0.07783156 0.9999359 1116.2168 697.3545
#> 4: 0.03849216 0.97584900 1.40315350 1.0086691 1135.5286 615.6157
#> 5: -0.97335260 -0.03204564 0.41000290 1.0010007 1258.1778 861.2881
#> 6: -0.67434520 0.22287280 0.71500850 0.9999690 1048.9039 516.2610
#> 7: 1.25414000 2.16936400 2.62522350 0.9983015 1021.2042 699.9287
#> 8: -0.14248400 0.77871360 1.18683750 1.0003833 848.9079 770.8900
#> 9: -0.18209020 0.68633040 1.06368250 1.0044866 1007.6991 870.8076
#> 10: -0.80714900 0.08960668 0.46413145 1.0015626 1292.0185 740.9535
#> 11: -0.59539100 0.25483080 0.72313170 0.9995037 1004.0812 669.2008
#> 12: -1.01078400 -0.13058960 0.31369895 1.0026888 1111.7633 603.5306
#> 13: -1.73638600 -0.70285240 -0.22549755 1.0017319 918.0660 668.8652
#> 14: 1.04193200 2.00727400 2.52476650 1.0039410 811.5404 715.9051
#> 15: 1.31238000 2.21457000 2.69267250 1.0081784 671.7252 606.7500
#> 16: -0.63643420 0.14273940 0.58648660 0.9993838 902.3891 664.8678
#> 17: -0.61403460 0.19564420 0.59908500 1.0001883 942.6207 649.6672
#> 18: -0.59803760 0.22412580 0.57244125 1.0084288 955.0925 559.5933
#> 19: -0.98200240 -0.14096460 0.22648935 1.0031153 831.1255 650.9774
#> 20: -0.75997380 0.10367300 0.45689665 1.0016436 1029.9612 488.1878
#> 21: 1.22571600 2.09416000 2.51556550 0.9999525 913.1640 697.0549
#> 22: 0.03576674 0.82900780 1.19219400 1.0082323 845.4144 740.5203
#> 23: -0.63054160 0.06901830 0.43821110 1.0018118 964.1409 840.2770
#> 24: -0.69097480 0.05950174 0.43832860 1.0013270 1062.1889 837.0867
#> 25: -0.44475380 0.28423200 0.62355940 0.9997798 884.0591 541.5468
#> 26: -1.25291000 -0.49283920 -0.11463860 0.9997994 870.8012 604.8573
#> 27: -1.09106400 -0.22992720 0.17963435 1.0035222 1005.9316 869.3363
#> 28: 1.63731600 2.53761000 2.99754950 1.0008203 771.7465 705.7897
#> 29: 1.17005600 2.02111600 2.56786150 1.0045334 630.0200 529.4048
#> 30: -0.27930620 0.50096000 0.94497975 0.9987949 1054.5768 768.7437
#> 31: -1.07643800 -0.26095880 0.15197150 1.0001976 895.8805 739.0679
#> 32: -0.64508600 0.16545180 0.56508735 1.0022309 846.4045 609.9356
#> 33: -1.06041600 -0.19554720 0.20240210 1.0002922 1078.1239 756.8602
#> 34: -0.59636400 0.31713240 0.78808490 1.0014451 1021.8652 754.7591
#> 35: 0.99095740 1.93170800 2.39164050 1.0010707 1143.6083 677.2956
#> 36: -0.29490800 0.62365040 1.06787450 1.0016283 1050.9204 712.3969
#> 37: -0.48485900 0.49282200 0.98846190 1.0064016 1140.9861 709.5052
#> 38: -0.43604580 0.62166340 1.22410250 1.0010480 948.8004 695.3760
#> 39: -0.37672560 0.76397180 1.35751650 1.0008524 1133.5732 822.8682
#> 40: -0.22108880 1.04695400 1.75608150 0.9997342 1052.5630 747.7963
#> q20 q80 q95 rhat ess_bulk ess_tail