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
enw_nowcast_samples(),
enw_nowcast_summary(),
enw_posterior(),
enw_pp_summary(),
enw_quantiles_to_long(),
enw_summarise_samples()
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 135.789 136
#> 4: DE 00+ 1 16 0.000000000 141.288 141
#> 5: DE 00+ 1 15 0.007194245 146.019 146
#> 6: DE 00+ 1 14 0.000000000 103.853 104
#> 7: DE 00+ 1 13 0.000000000 62.766 63
#> 8: DE 00+ 1 12 0.000000000 185.035 185
#> 9: DE 00+ 1 11 0.000000000 256.031 256
#> 10: DE 00+ 1 10 0.004219409 267.692 267
#> 11: DE 00+ 1 9 0.000000000 236.057 235
#> 12: DE 00+ 1 8 0.015873016 231.407 231
#> 13: DE 00+ 1 7 0.040000000 164.876 164
#> 14: DE 00+ 1 6 0.010204082 128.858 128
#> 15: DE 00+ 1 5 0.012396694 293.309 292
#> 16: DE 00+ 1 4 0.017937220 293.042 292
#> 17: DE 00+ 1 3 0.019801980 291.478 290
#> 18: DE 00+ 1 2 0.070175439 293.178 291
#> 19: DE 00+ 1 1 0.383928571 308.518 302
#> 20: DE 00+ 1 0 1.000000000 375.644 363
#> 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 149.00 149 149.00 149 149.00 149 149.00 NA
#> 2: 1.318432 1.4826 166.00 166 167.00 167 168.00 168 170.00 0.9997778
#> 3: 1.854162 1.4826 133.00 134 135.00 136 136.00 137 139.00 1.0005878
#> 4: 2.378805 1.4826 138.00 139 140.00 141 142.00 143 146.00 1.0056729
#> 5: 3.079630 2.9652 141.00 143 145.00 146 147.00 148 152.00 1.0001025
#> 6: 2.969884 2.9652 100.00 101 102.65 104 105.00 106 109.00 0.9991163
#> 7: 2.432110 2.9652 59.00 61 62.00 63 63.00 65 67.00 1.0009067
#> 8: 3.847932 4.4478 179.00 182 183.00 185 186.00 188 192.00 1.0002531
#> 9: 6.498790 5.9304 246.00 251 253.00 256 258.00 261 268.00 0.9997137
#> 10: 7.713536 7.4130 256.00 261 265.00 267 270.00 274 282.00 1.0004951
#> 11: 8.508004 7.4130 223.00 229 233.00 235 239.00 243 251.00 0.9985724
#> 12: 10.083304 10.3782 216.00 222 227.00 231 234.00 240 250.00 1.0035756
#> 13: 9.848127 10.3782 150.00 157 160.00 164 168.00 173 181.00 1.0025186
#> 14: 8.134495 7.4130 117.00 122 125.00 128 131.00 135 143.00 1.0010009
#> 15: 11.938427 11.8608 276.00 283 288.00 292 297.00 303 315.00 1.0002316
#> 16: 15.799489 14.8260 268.00 280 286.00 292 299.00 306 320.00 1.0047058
#> 17: 21.108128 19.2738 260.00 274 282.00 290 298.35 308 330.00 1.0002334
#> 18: 29.440398 28.1694 250.00 269 280.00 291 301.00 316 344.00 0.9993186
#> 19: 51.151522 46.7019 239.00 267 286.00 302 322.00 343 399.10 0.9992178
#> 20: 103.424281 91.1799 233.95 292 327.00 363 397.00 447 565.05 0.9991666
#> sd mad q5 q20 q35 q50 q65 q80 q95 rhat
#> ess_bulk ess_tail
#> <num> <num>
#> 1: NA NA
#> 2: 1003.5643 828.2026
#> 3: 991.0805 916.0962
#> 4: 1041.2029 872.9772
#> 5: 991.1639 885.1056
#> 6: 982.3833 917.3468
#> 7: 911.2098 793.6687
#> 8: 1004.7790 972.0392
#> 9: 1025.3311 936.5679
#> 10: 1040.1417 1008.8448
#> 11: 1201.4893 863.9704
#> 12: 751.4194 655.0188
#> 13: 927.9122 920.9413
#> 14: 1080.4014 964.2083
#> 15: 1159.2978 984.6276
#> 16: 1087.6045 957.9921
#> 17: 1129.6391 914.6275
#> 18: 1126.3831 903.3288
#> 19: 1328.2832 998.7573
#> 20: 1554.0485 999.3402
#> ess_bulk ess_tail