Add the latest observations to the nowcast output. This is useful for plotting the nowcast against the latest observations.
Arguments
- nowcast
A
data.frameof nowcast output fromenw_nowcast_summary().- obs
An observation
data.framecontainingreference_datecolumns of the same length as the number of rows in the posterior and the most up to date observation for each date. This is used to align the posterior with the observations. The easiest source of this data is the output of latest output ofenw_preprocess_data()orenw_latest_data().
See also
Functions used for postprocessing of model fits
build_ord_obs(),
enw_nowcast_samples(),
enw_nowcast_summary(),
enw_posterior(),
enw_pp_summary(),
enw_quantiles_to_long(),
enw_summarise_samples(),
subset_obs()
Examples
fit <- enw_example("nowcast")
obs <- enw_example("obs")
nowcast <- summary(fit, type = "nowcast")
enw_add_latest_obs_to_nowcast(nowcast, obs)
#> Key: <reference_date, .group>
#> reference_date .group latest_confirm confirm report_date max_confirm
#> <IDat> <num> <int> <int> <IDat> <int>
#> 1: 2021-08-03 1 156 149 2021-08-22 149
#> 2: 2021-08-04 1 183 166 2021-08-22 166
#> 3: 2021-08-05 1 147 133 2021-08-22 133
#> 4: 2021-08-06 1 155 137 2021-08-22 137
#> 5: 2021-08-07 1 159 139 2021-08-22 139
#> 6: 2021-08-08 1 119 97 2021-08-22 97
#> 7: 2021-08-09 1 65 58 2021-08-22 58
#> 8: 2021-08-10 1 204 175 2021-08-22 175
#> 9: 2021-08-11 1 275 233 2021-08-22 233
#> 10: 2021-08-12 1 273 237 2021-08-22 237
#> 11: 2021-08-13 1 270 204 2021-08-22 204
#> 12: 2021-08-14 1 262 189 2021-08-22 189
#> 13: 2021-08-15 1 192 125 2021-08-22 125
#> 14: 2021-08-16 1 140 98 2021-08-22 98
#> 15: 2021-08-17 1 323 242 2021-08-22 242
#> 16: 2021-08-18 1 409 223 2021-08-22 223
#> 17: 2021-08-19 1 370 202 2021-08-22 202
#> 18: 2021-08-20 1 361 171 2021-08-22 171
#> 19: 2021-08-21 1 339 112 2021-08-22 112
#> 20: 2021-08-22 1 258 45 2021-08-22 45
#> reference_date .group latest_confirm confirm report_date max_confirm
#> location age_group cum_prop_reported delay prop_reported mean median
#> <fctr> <fctr> <num> <num> <num> <num> <num>
#> 1: DE 00+ 1 19 0.000000000 149.000 149
#> 2: DE 00+ 1 18 0.000000000 167.426 167
#> 3: DE 00+ 1 17 0.000000000 135.812 136
#> 4: DE 00+ 1 16 0.000000000 141.255 141
#> 5: DE 00+ 1 15 0.007194245 145.884 146
#> 6: DE 00+ 1 14 0.000000000 103.681 103
#> 7: DE 00+ 1 13 0.000000000 62.762 62
#> 8: DE 00+ 1 12 0.000000000 185.138 185
#> 9: DE 00+ 1 11 0.000000000 255.903 255
#> 10: DE 00+ 1 10 0.004219409 267.488 267
#> 11: DE 00+ 1 9 0.000000000 236.164 236
#> 12: DE 00+ 1 8 0.015873016 230.867 230
#> 13: DE 00+ 1 7 0.040000000 165.026 165
#> 14: DE 00+ 1 6 0.010204082 129.307 128
#> 15: DE 00+ 1 5 0.012396694 292.749 292
#> 16: DE 00+ 1 4 0.017937220 293.296 292
#> 17: DE 00+ 1 3 0.019801980 291.404 290
#> 18: DE 00+ 1 2 0.070175439 295.703 292
#> 19: DE 00+ 1 1 0.383928571 309.456 304
#> 20: DE 00+ 1 0 1.000000000 381.243 371
#> location age_group cum_prop_reported delay prop_reported mean median
#> sd mad q5 q20 q35 q50 q65 q80 q95 rhat
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0.000000 0.0000 149.00 149.0 149 149 149 149.0 149.00 NA
#> 2: 1.316912 1.4826 166.00 166.0 167 167 168 168.0 170.00 0.9988498
#> 3: 1.867121 1.4826 133.00 134.0 135 136 136 137.0 139.00 0.9997035
#> 4: 2.341679 2.2239 138.00 139.0 140 141 142 143.0 145.00 1.0017705
#> 5: 3.085460 2.9652 141.00 143.0 144 146 147 148.0 151.05 0.9984520
#> 6: 3.072243 2.9652 99.00 101.0 102 103 105 106.0 109.00 0.9997410
#> 7: 2.461588 1.4826 59.00 61.0 62 62 63 65.0 67.00 0.9987681
#> 8: 3.827742 4.4478 180.00 182.0 183 185 186 188.0 192.00 1.0064762
#> 9: 6.410514 5.9304 246.00 250.0 253 255 258 261.0 267.00 1.0014583
#> 10: 7.975931 7.4130 255.00 261.0 264 267 270 274.0 281.00 1.0052639
#> 11: 8.459235 8.8956 223.00 229.0 232 236 239 243.0 252.00 1.0041063
#> 12: 10.092332 10.3782 216.00 222.0 226 230 234 239.0 249.00 1.0026046
#> 13: 10.254095 10.3782 149.95 156.8 160 165 168 173.0 183.00 0.9991933
#> 14: 8.532969 8.8956 116.00 122.0 126 128 132 136.0 144.05 1.0000500
#> 15: 11.792076 11.8608 275.00 283.0 288 292 297 303.0 313.00 0.9993709
#> 16: 15.992941 14.8260 270.00 280.0 286 292 298 306.0 320.00 1.0008511
#> 17: 19.436887 19.2738 263.00 274.0 284 290 298 306.0 325.00 0.9992936
#> 18: 28.861250 28.1694 254.95 271.0 282 292 304 319.0 348.00 1.0008356
#> 19: 51.295236 47.4432 237.00 266.8 285 304 323 349.0 406.00 0.9988372
#> 20: 103.890978 96.3690 240.85 293.8 332 371 405 459.2 569.05 0.9993392
#> sd mad q5 q20 q35 q50 q65 q80 q95 rhat
#> ess_bulk ess_tail
#> <num> <num>
#> 1: NA NA
#> 2: 997.2790 946.6047
#> 3: 1015.7372 1017.4859
#> 4: 901.5890 958.7197
#> 5: 1097.0842 1044.2543
#> 6: 1034.2365 913.6061
#> 7: 1012.1885 904.7496
#> 8: 885.6025 907.2186
#> 9: 933.4472 918.6322
#> 10: 1044.4533 1055.2781
#> 11: 1039.8102 897.5389
#> 12: 1018.9795 919.8161
#> 13: 1090.1636 782.0808
#> 14: 1023.7823 888.2873
#> 15: 1145.8224 968.8335
#> 16: 1056.0595 914.9342
#> 17: 1248.9469 968.8798
#> 18: 1412.5892 1036.8252
#> 19: 1195.0907 985.8485
#> 20: 1349.1168 1029.2555
#> ess_bulk ess_tail