summary method for class "epinowcast".
Arguments
- object
A
data.tableoutput fromepinowcast().- type
Character string indicating the summary to return; enforced by
base::match.arg(). Supported options are:"nowcast" which summarises nowcast posterior with
enw_nowcast_summary(),"nowcast_samples" which samples latest with
enw_nowcast_samples(),"fit" which returns the summarised
cmdstanrfit withenw_posterior(),"posterior_prediction" which returns summarised posterior predictions for the observations after fitting using
enw_pp_summary().
- ...
Additional arguments passed to summary specified by
type.
See also
summary epinowcast
Other epinowcast:
epinowcast(),
plot.epinowcast()
Examples
nowcast <- enw_example("nowcast")
# Summarise nowcast posterior
summary(nowcast, type = "nowcast")
#> reference_date report_date .group max_confirm location age_group confirm
#> 1: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> 2: 2021-08-04 2021-08-22 1 166 DE 00+ 166
#> 3: 2021-08-05 2021-08-22 1 133 DE 00+ 133
#> 4: 2021-08-06 2021-08-22 1 137 DE 00+ 137
#> 5: 2021-08-07 2021-08-22 1 139 DE 00+ 139
#> 6: 2021-08-08 2021-08-22 1 97 DE 00+ 97
#> 7: 2021-08-09 2021-08-22 1 58 DE 00+ 58
#> 8: 2021-08-10 2021-08-22 1 175 DE 00+ 175
#> 9: 2021-08-11 2021-08-22 1 233 DE 00+ 233
#> 10: 2021-08-12 2021-08-22 1 237 DE 00+ 237
#> 11: 2021-08-13 2021-08-22 1 204 DE 00+ 204
#> 12: 2021-08-14 2021-08-22 1 189 DE 00+ 189
#> 13: 2021-08-15 2021-08-22 1 125 DE 00+ 125
#> 14: 2021-08-16 2021-08-22 1 98 DE 00+ 98
#> 15: 2021-08-17 2021-08-22 1 242 DE 00+ 242
#> 16: 2021-08-18 2021-08-22 1 223 DE 00+ 223
#> 17: 2021-08-19 2021-08-22 1 202 DE 00+ 202
#> 18: 2021-08-20 2021-08-22 1 171 DE 00+ 171
#> 19: 2021-08-21 2021-08-22 1 112 DE 00+ 112
#> 20: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> cum_prop_reported delay prop_reported mean median sd mad q5
#> 1: 1 19 0.000000000 149.000 149.0 0.000000 0.0000 149
#> 2: 1 18 0.000000000 167.453 167.0 1.291301 1.4826 166
#> 3: 1 17 0.000000000 135.677 135.0 1.853674 1.4826 133
#> 4: 1 16 0.000000000 140.896 141.0 2.169768 2.9652 138
#> 5: 1 15 0.007194245 145.222 145.0 2.760133 2.9652 141
#> 6: 1 14 0.000000000 103.248 103.0 2.893591 2.9652 99
#> 7: 1 13 0.000000000 62.742 62.0 2.412313 2.9652 59
#> 8: 1 12 0.000000000 185.065 185.0 3.687054 4.4478 180
#> 9: 1 11 0.000000000 255.574 255.0 6.079925 5.9304 246
#> 10: 1 10 0.004219409 266.480 266.0 7.480345 7.4130 255
#> 11: 1 9 0.000000000 235.082 235.0 7.671123 7.4130 224
#> 12: 1 8 0.015873016 229.150 229.0 9.532280 10.3782 215
#> 13: 1 7 0.040000000 163.327 163.0 8.941478 8.8956 149
#> 14: 1 6 0.010204082 130.404 130.0 8.343284 8.8956 118
#> 15: 1 5 0.012396694 299.890 299.0 12.795923 13.3434 280
#> 16: 1 4 0.017937220 303.138 301.0 17.375755 17.0499 278
#> 17: 1 3 0.019801980 311.131 309.0 24.503638 23.7216 275
#> 18: 1 2 0.070175439 318.522 315.0 34.923992 34.0998 270
#> 19: 1 1 0.383928571 334.231 327.5 53.967621 51.1497 258
#> 20: 1 0 1.000000000 322.430 306.0 93.868922 80.8017 198
#> q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail
#> 1: 149.0 149.00 149.0 149.00 149.0 149.00 NA NA NA
#> 2: 166.0 167.00 167.0 168.00 168.0 170.00 1.0002481 858.3035 887.5883
#> 3: 134.0 135.00 135.0 136.00 137.0 139.00 0.9986872 784.2638 908.5946
#> 4: 139.0 140.00 141.0 142.00 143.0 145.00 0.9999757 986.1909 904.5245
#> 5: 143.0 144.00 145.0 146.00 147.0 150.00 1.0007676 865.5054 947.2221
#> 6: 101.0 102.00 103.0 104.00 106.0 109.00 0.9991973 893.3750 835.9871
#> 7: 61.0 62.00 62.0 63.00 65.0 67.00 1.0036034 926.5402 861.1900
#> 8: 182.0 183.00 185.0 186.00 188.0 191.00 0.9989872 1097.4089 937.1445
#> 9: 251.0 253.00 255.0 258.00 261.0 266.00 0.9991648 1111.3917 1021.2936
#> 10: 260.0 263.00 266.0 269.00 272.0 279.05 1.0073591 1059.0036 983.0723
#> 11: 228.0 232.00 235.0 237.35 241.0 248.00 1.0012317 975.8115 927.8796
#> 12: 221.0 225.00 229.0 232.00 237.0 246.00 0.9999872 1084.7105 951.3275
#> 13: 155.0 159.00 163.0 166.00 171.0 178.00 0.9994459 1041.2300 974.4463
#> 14: 123.0 127.00 130.0 133.00 137.0 145.00 1.0012796 935.7652 1050.2310
#> 15: 289.0 294.00 299.0 304.00 310.2 322.00 1.0017171 1130.4067 958.2443
#> 16: 288.8 295.00 301.0 308.00 317.0 334.05 1.0055266 848.0051 763.8073
#> 17: 291.0 300.65 309.0 317.35 329.0 356.00 1.0105072 1083.0271 1028.2641
#> 18: 288.8 303.00 315.0 330.00 343.2 383.00 1.0047446 1063.6034 939.8372
#> 19: 288.8 308.00 327.5 347.00 375.0 436.00 1.0005068 1007.8998 874.2774
#> 20: 247.8 276.00 306.0 340.35 390.2 493.05 1.0003640 1366.8407 840.3280
# Nowcast posterior samples
summary(nowcast, type = "nowcast_samples")
#> reference_date report_date .group max_confirm location age_group confirm
#> 1: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> 2: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> 3: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> 4: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> 5: 2021-08-03 2021-08-22 1 149 DE 00+ 149
#> ---
#> 19996: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> 19997: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> 19998: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> 19999: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> 20000: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> cum_prop_reported delay prop_reported .chain .iteration .draw sample
#> 1: 1 19 0 1 1 1 149
#> 2: 1 19 0 1 2 2 149
#> 3: 1 19 0 1 3 3 149
#> 4: 1 19 0 1 4 4 149
#> 5: 1 19 0 1 5 5 149
#> ---
#> 19996: 1 0 1 2 496 996 409
#> 19997: 1 0 1 2 497 997 362
#> 19998: 1 0 1 2 498 998 272
#> 19999: 1 0 1 2 499 999 247
#> 20000: 1 0 1 2 500 1000 189
# Nowcast model fit
summary(nowcast, type = "fit")
#> variable mean median sd mad
#> 1: lp__ -1362.7433500 -1362.4000000 6.7742130 6.5753310
#> 2: expr_lelatent_int[1,1] 4.1434508 4.1380450 0.1539449 0.1552134
#> 3: expr_beta[1] 0.3257798 0.3108810 0.4838097 0.4978898
#> 4: expr_beta[2] -0.2857518 -0.2857245 0.4811320 0.4351924
#> 5: expr_beta[3] -0.6982430 -0.6893515 0.5035263 0.4989661
#> ---
#> 852: pp_inf_obs[16,1] 303.1380000 301.0000000 17.3757553 17.0499000
#> 853: pp_inf_obs[17,1] 311.1310000 309.0000000 24.5036378 23.7216000
#> 854: pp_inf_obs[18,1] 318.5220000 315.0000000 34.9239918 34.0998000
#> 855: pp_inf_obs[19,1] 334.2310000 327.5000000 53.9676212 51.1497000
#> 856: pp_inf_obs[20,1] 322.4300000 306.0000000 93.8689218 80.8017000
#> q5 q20 q80 q95 rhat
#> 1: -1374.5120000 -1.367952e+03 -1357.3100000 -1.352125e+03 1.0201797
#> 2: 3.8906655 4.018240e+00 4.2758060 4.402856e+00 0.9997963
#> 3: -0.4580074 -9.366416e-02 0.7622114 1.104135e+00 1.0099929
#> 4: -1.1171430 -6.469084e-01 0.1029920 5.050463e-01 1.0006211
#> 5: -1.5294965 -1.135836e+00 -0.2657970 8.892956e-02 1.0001976
#> ---
#> 852: 278.0000000 2.888000e+02 317.0000000 3.340500e+02 1.0055266
#> 853: 275.0000000 2.910000e+02 329.0000000 3.560000e+02 1.0105072
#> 854: 270.0000000 2.888000e+02 343.2000000 3.830000e+02 1.0047446
#> 855: 258.0000000 2.888000e+02 375.0000000 4.360000e+02 1.0005068
#> 856: 198.0000000 2.478000e+02 390.2000000 4.930500e+02 1.0003640
#> ess_bulk ess_tail
#> 1: 223.5349 482.9928
#> 2: 1194.9226 825.8359
#> 3: 1159.2004 822.9336
#> 4: 1284.8243 642.2432
#> 5: 1285.1949 745.8015
#> ---
#> 852: 848.0051 763.8073
#> 853: 1083.0271 1028.2641
#> 854: 1063.6034 939.8372
#> 855: 1007.8998 874.2774
#> 856: 1366.8407 840.3280
# Posterior predictions
summary(nowcast, type = "posterior_prediction")
#> reference_date report_date .group max_confirm location age_group confirm
#> 1: 2021-07-13 2021-07-13 1 59 DE 00+ 21
#> 2: 2021-07-13 2021-07-14 1 59 DE 00+ 33
#> 3: 2021-07-13 2021-07-15 1 59 DE 00+ 36
#> 4: 2021-07-13 2021-07-16 1 59 DE 00+ 40
#> 5: 2021-07-13 2021-07-17 1 59 DE 00+ 43
#> ---
#> 626: 2021-08-20 2021-08-21 1 171 DE 00+ 159
#> 627: 2021-08-20 2021-08-22 1 171 DE 00+ 171
#> 628: 2021-08-21 2021-08-21 1 112 DE 00+ 69
#> 629: 2021-08-21 2021-08-22 1 112 DE 00+ 112
#> 630: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> cum_prop_reported delay new_confirm prop_reported mean median sd
#> 1: 0.3559322 0 21 0.35593220 23.512 22.0 9.740572
#> 2: 0.5593220 1 12 0.20338983 11.574 11.0 5.158016
#> 3: 0.6101695 2 3 0.05084746 6.492 6.0 3.540405
#> 4: 0.6779661 3 4 0.06779661 4.672 4.0 2.726509
#> 5: 0.7288136 4 3 0.05084746 2.765 3.0 1.896674
#> ---
#> 626: 0.9298246 1 61 0.35672515 47.044 43.5 19.194491
#> 627: 1.0000000 2 12 0.07017544 15.098 14.0 6.970867
#> 628: 0.6160714 0 69 0.61607143 109.043 100.0 45.345136
#> 629: 1.0000000 1 43 0.38392857 27.749 26.0 12.245814
#> 630: 1.0000000 0 45 1.00000000 52.151 49.0 23.805565
#> mad q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk
#> 1: 8.8956 10.00 15.0 19 22.0 26.00 31 42.00 0.9987800 1164.0127
#> 2: 4.4478 4.00 7.0 9 11.0 13.00 16 21.00 0.9988065 1016.1220
#> 3: 2.9652 1.00 3.0 5 6.0 7.00 9 13.00 1.0010941 942.2827
#> 4: 2.9652 1.00 2.0 3 4.0 5.00 7 10.00 1.0017034 817.9688
#> 5: 1.4826 0.00 1.0 2 3.0 3.00 4 6.00 1.0007183 982.1315
#> ---
#> 626: 17.0499 21.00 31.8 38 43.5 51.00 62 84.05 1.0056592 975.5135
#> 627: 5.9304 6.00 9.0 12 14.0 17.00 20 28.00 0.9986250 910.5990
#> 628: 38.5476 49.95 74.0 87 100.0 116.00 142 192.00 1.0011913 989.3871
#> 629: 11.8608 11.00 17.0 22 26.0 31.00 37 52.00 1.0000296 916.6473
#> 630: 22.2390 21.00 32.0 40 49.0 57.35 71 97.00 0.9997764 1035.8098
#> ess_tail
#> 1: 834.6738
#> 2: 1032.3776
#> 3: 975.7223
#> 4: 782.9234
#> 5: 914.5297
#> ---
#> 626: 913.8415
#> 627: 1033.0860
#> 628: 933.3896
#> 629: 922.1847
#> 630: 904.4781