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().
- max_delay
Maximum delay to which nowcasts should be summarised, in units of the timestep used during preprocessing. Must be equal (default) or larger than the modelled maximum delay. If it is larger, then nowcasts for unmodelled dates are added by assuming that case counts beyond the modelled maximum delay are fully observed.
- ...
Additional arguments passed to summary specified by
type.
See also
summary epinowcast
Other epinowcast:
epinowcast(),
plot.enw_preprocess_data(),
plot.epinowcast(),
print.enw_preprocess_data(),
print.epinowcast(),
print.summary.enw_preprocess_data(),
summary.enw_preprocess_data()
Examples
nowcast <- enw_example("nowcast")
# Summarise nowcast posterior
summary(nowcast, type = "nowcast")
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> 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
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> cum_prop_reported delay prop_reported mean median sd mad
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1 19 0.000000000 149.000 149.0 0.000000 0.0000
#> 2: 1 18 0.000000000 167.570 167.0 1.341976 1.4826
#> 3: 1 17 0.000000000 135.841 136.0 1.839865 1.4826
#> 4: 1 16 0.000000000 141.576 141.0 2.333167 2.9652
#> 5: 1 15 0.007194245 146.294 146.0 2.986383 2.9652
#> 6: 1 14 0.000000000 104.040 104.0 3.024822 2.9652
#> 7: 1 13 0.000000000 62.986 63.0 2.453125 2.9652
#> 8: 1 12 0.000000000 185.872 186.0 3.783640 4.4478
#> 9: 1 11 0.000000000 257.301 257.0 6.031150 5.9304
#> 10: 1 10 0.004219409 268.618 268.0 7.433797 7.4130
#> 11: 1 9 0.000000000 237.750 237.0 7.647090 7.4130
#> 12: 1 8 0.015873016 232.124 231.0 9.302750 8.8956
#> 13: 1 7 0.040000000 164.968 164.0 8.833516 8.8956
#> 14: 1 6 0.010204082 129.748 129.0 7.402790 7.4130
#> 15: 1 5 0.012396694 298.836 298.0 11.534477 10.3782
#> 16: 1 4 0.017937220 299.495 299.0 15.778623 16.3086
#> 17: 1 3 0.019801980 300.872 299.0 19.534206 17.7912
#> 18: 1 2 0.070175439 301.149 299.5 25.061901 25.9455
#> 19: 1 1 0.383928571 310.948 304.5 44.585526 40.7715
#> 20: 1 0 1.000000000 322.429 310.5 77.062219 71.9061
#> cum_prop_reported delay prop_reported mean median sd mad
#> <num> <num> <num> <num> <num> <num> <num>
#> q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 149.00 149 149 149.0 149 149.0 149.00 NA NA NA
#> 2: 166.00 166 167 167.0 168 169.0 170.00 1.0018294 931.5700 943.7805
#> 3: 133.00 134 135 136.0 136 137.0 139.00 0.9999078 1110.6783 824.4184
#> 4: 138.00 139 140 141.0 142 143.0 146.00 0.9995363 976.8520 941.6408
#> 5: 142.00 144 145 146.0 147 149.0 151.00 1.0049074 1016.3094 1075.3542
#> 6: 99.95 102 103 104.0 105 107.0 109.00 1.0009570 952.3883 907.9565
#> 7: 60.00 61 62 63.0 64 65.0 67.00 0.9997744 939.2724 900.4309
#> 8: 180.00 183 184 186.0 187 189.0 192.00 1.0024075 963.8494 867.5415
#> 9: 248.00 252 255 257.0 259 262.0 267.00 0.9990347 1110.7851 1000.2993
#> 10: 257.00 262 265 268.0 271 275.0 281.05 1.0002420 896.3291 823.6561
#> 11: 226.00 231 234 237.0 240 244.0 251.05 1.0009314 1054.2097 821.3613
#> 12: 218.00 224 228 231.0 235 240.0 249.00 0.9990366 1066.2114 898.9808
#> 13: 152.00 157 161 164.0 168 172.0 180.05 1.0017740 827.0492 776.8752
#> 14: 118.00 124 127 129.0 132 136.0 143.00 1.0016858 940.2680 896.0489
#> 15: 282.00 289 294 298.0 302 308.0 319.05 1.0011654 1137.5067 958.8055
#> 16: 275.00 285 292 299.0 304 312.0 328.00 0.9999211 961.7466 971.0490
#> 17: 272.00 285 292 299.0 306 316.0 334.05 1.0007756 1114.9192 893.4042
#> 18: 263.00 279 290 299.5 309 321.2 347.00 0.9985244 1210.6829 900.7933
#> 19: 250.00 273 289 304.5 323 345.2 396.00 1.0000264 1166.8854 988.3457
#> 20: 214.95 256 285 310.5 343 384.0 463.10 1.0017480 1320.3486 883.1070
#> q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
# Nowcast posterior samples
summary(nowcast, type = "nowcast_samples")
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> 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
#> <num> <num> <num> <int> <int> <int> <num>
#> 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 2 496 996 254
#> 19997: 1 0 1 2 497 997 373
#> 19998: 1 0 1 2 498 998 412
#> 19999: 1 0 1 2 499 999 332
#> 20000: 1 0 1 2 500 1000 347
# Nowcast model fit
summary(nowcast, type = "fit")
#> variable mean median sd mad
#> <char> <num> <num> <num> <num>
#> 1: lp__ -1312.1722704 -1311.9705000 7.2740418 7.2530275
#> 2: expr_lelatent_int[1,1] 4.2953356 4.2982551 0.1407952 0.1400908
#> 3: expr_beta[1] -0.3209256 -0.3029958 0.4974060 0.5003899
#> 4: expr_beta[2] -0.7426578 -0.7537567 0.4890380 0.4876114
#> 5: expr_beta[3] 0.4907579 0.4852679 0.4909068 0.4815642
#> ---
#> 829: pp_inf_obs[16,1] 299.4950000 299.0000000 15.7786229 16.3086000
#> 830: pp_inf_obs[17,1] 300.8720000 299.0000000 19.5342059 17.7912000
#> 831: pp_inf_obs[18,1] 301.1490000 299.5000000 25.0619013 25.9455000
#> 832: pp_inf_obs[19,1] 310.9480000 304.5000000 44.5855264 40.7715000
#> 833: pp_inf_obs[20,1] 322.4290000 310.5000000 77.0622187 71.9061000
#> q5 q20 q80 q95 rhat
#> <num> <num> <num> <num> <num>
#> 1: -1324.8030150 -1317.9927800 -1305.9577000 -1.300480e+03 1.0022016
#> 2: 4.0710473 4.1780239 4.4131850 4.522491e+00 1.0047425
#> 3: -1.1751767 -0.7363925 0.0902879 4.850281e-01 1.0018321
#> 4: -1.5490582 -1.1367682 -0.3446364 6.348241e-02 1.0043698
#> 5: -0.3189382 0.1004458 0.9074385 1.247705e+00 0.9982835
#> ---
#> 829: 275.0000000 285.0000000 312.0000000 3.280000e+02 0.9999211
#> 830: 272.0000000 285.0000000 316.0000000 3.340500e+02 1.0007756
#> 831: 263.0000000 279.0000000 321.2000000 3.470000e+02 0.9985244
#> 832: 250.0000000 273.0000000 345.2000000 3.960000e+02 1.0000264
#> 833: 214.9500000 256.0000000 384.0000000 4.631000e+02 1.0017480
#> ess_bulk ess_tail
#> <num> <num>
#> 1: 223.4158 373.1034
#> 2: 884.3912 704.5825
#> 3: 878.4473 706.1199
#> 4: 741.5364 691.1377
#> 5: 733.0923 665.0849
#> ---
#> 829: 961.7466 971.0490
#> 830: 1114.9192 893.4042
#> 831: 1210.6829 900.7933
#> 832: 1166.8854 988.3457
#> 833: 1320.3486 883.1070
# Posterior predictions
summary(nowcast, type = "posterior_prediction")
#> reference_date report_date .group max_confirm location age_group confirm
#> <IDat> <IDat> <num> <int> <fctr> <fctr> <int>
#> 1: 2021-07-14 2021-07-14 1 72 DE 00+ 22
#> 2: 2021-07-14 2021-07-15 1 72 DE 00+ 34
#> 3: 2021-07-14 2021-07-16 1 72 DE 00+ 38
#> 4: 2021-07-14 2021-07-17 1 72 DE 00+ 43
#> 5: 2021-07-14 2021-07-18 1 72 DE 00+ 43
#> ---
#> 606: 2021-08-20 2021-08-21 1 171 DE 00+ 159
#> 607: 2021-08-20 2021-08-22 1 171 DE 00+ 171
#> 608: 2021-08-21 2021-08-21 1 112 DE 00+ 69
#> 609: 2021-08-21 2021-08-22 1 112 DE 00+ 112
#> 610: 2021-08-22 2021-08-22 1 45 DE 00+ 45
#> cum_prop_reported delay new_confirm prop_reported mean median sd
#> <num> <num> <int> <num> <num> <num> <num>
#> 1: 0.3055556 0 22 0.30555556 26.383 25 9.581969
#> 2: 0.4722222 1 12 0.16666667 15.233 15 6.050729
#> 3: 0.5277778 2 4 0.05555556 7.678 7 3.636969
#> 4: 0.5972222 3 5 0.06944444 3.944 4 2.286502
#> 5: 0.5972222 4 0 0.00000000 1.400 1 1.259995
#> ---
#> 606: 0.9298246 1 61 0.35672515 55.768 54 18.172904
#> 607: 1.0000000 2 12 0.07017544 12.969 13 5.157387
#> 608: 0.6160714 0 69 0.61607143 94.223 91 30.188319
#> 609: 1.0000000 1 43 0.38392857 32.499 31 11.903684
#> 610: 1.0000000 0 45 1.00000000 47.577 45 17.455107
#> mad q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 8.8956 13 18 22 25 29 33.0 44.05 1.0004729 1061.6093
#> 2: 5.9304 7 10 12 15 17 20.0 27.00 0.9988477 1023.0243
#> 3: 2.9652 2 5 6 7 9 10.0 14.00 1.0020368 880.4481
#> 4: 2.9652 1 2 3 4 4 6.0 8.00 0.9988907 1042.2961
#> 5: 1.4826 0 0 1 1 2 2.0 4.00 1.0058021 947.5482
#> ---
#> 606: 17.7912 29 41 47 54 61 69.2 88.05 1.0017605 1121.7985
#> 607: 5.9304 6 8 11 13 14 17.0 22.00 0.9995106 941.1128
#> 608: 28.1694 52 69 80 91 102 117.0 148.00 1.0000863 950.7711
#> 609: 10.3782 17 22 27 31 35 41.0 55.00 1.0022798 866.9334
#> 610: 16.3086 25 33 39 45 52 60.2 79.00 1.0013025 1030.5884
#> ess_tail
#> <num>
#> 1: 991.5120
#> 2: 970.0040
#> 3: 852.4398
#> 4: 972.7787
#> 5: 928.1070
#> ---
#> 606: 944.1552
#> 607: 1010.1847
#> 608: 896.0739
#> 609: 748.8873
#> 610: 807.8255