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 149.000 149.0
#> 2: DE 00+ 1 18 0.000000000 167.480 167.0
#> 3: DE 00+ 1 17 0.000000000 135.797 136.0
#> 4: DE 00+ 1 16 0.000000000 141.272 141.0
#> 5: DE 00+ 1 15 0.007194245 145.764 145.0
#> 6: DE 00+ 1 14 0.000000000 103.614 103.0
#> 7: DE 00+ 1 13 0.000000000 62.838 63.0
#> 8: DE 00+ 1 12 0.000000000 185.149 185.0
#> 9: DE 00+ 1 11 0.000000000 256.191 256.0
#> 10: DE 00+ 1 10 0.004219409 267.189 266.0
#> 11: DE 00+ 1 9 0.000000000 236.218 235.0
#> 12: DE 00+ 1 8 0.015873016 230.463 230.0
#> 13: DE 00+ 1 7 0.040000000 165.184 165.0
#> 14: DE 00+ 1 6 0.010204082 129.535 129.0
#> 15: DE 00+ 1 5 0.012396694 293.236 292.0
#> 16: DE 00+ 1 4 0.017937220 293.392 292.0
#> 17: DE 00+ 1 3 0.019801980 291.537 290.0
#> 18: DE 00+ 1 2 0.070175439 295.258 292.5
#> 19: DE 00+ 1 1 0.383928571 310.440 304.0
#> 20: DE 00+ 1 0 1.000000000 384.142 368.0
#> 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 149.00 149 149.00 149.0 149 149.0 149.00 NA
#> 2: 1.403414 1.4826 166.00 166 167.00 167.0 168 168.0 170.00 1.0011717
#> 3: 1.858291 1.4826 133.00 134 135.00 136.0 136 137.0 139.00 1.0023790
#> 4: 2.414507 2.9652 138.00 139 140.00 141.0 142 143.0 146.00 0.9997357
#> 5: 3.070461 2.9652 141.00 143 144.00 145.0 147 148.0 151.00 1.0009523
#> 6: 2.892987 2.9652 99.00 101 102.00 103.0 105 106.0 109.00 1.0056338
#> 7: 2.595475 2.9652 59.00 61 62.00 63.0 64 65.0 68.00 0.9989559
#> 8: 3.655155 2.9652 180.00 182 183.00 185.0 186 188.0 192.00 1.0008924
#> 9: 7.084540 7.4130 246.00 250 253.00 256.0 258 262.0 269.00 1.0019152
#> 10: 8.269563 7.4130 255.00 260 264.00 266.0 270 274.0 282.00 0.9991677
#> 11: 8.635453 8.8956 224.00 229 232.00 235.0 239 243.0 252.00 1.0025829
#> 12: 10.395995 10.3782 215.00 222 226.00 230.0 234 239.0 249.00 1.0004028
#> 13: 9.660925 8.8956 150.00 157 161.00 165.0 168 173.0 182.00 1.0063929
#> 14: 8.499354 8.8956 117.00 122 125.00 129.0 132 136.0 144.00 1.0015131
#> 15: 11.922603 11.8608 276.00 283 288.00 292.0 297 302.0 315.00 1.0036606
#> 16: 15.981231 14.8260 269.00 280 286.00 292.0 299 306.0 321.00 0.9996327
#> 17: 20.271200 20.7564 262.00 274 282.00 290.0 298 308.0 327.05 0.9994497
#> 18: 29.356997 27.4281 252.00 271 282.00 292.5 304 317.0 350.00 0.9986679
#> 19: 50.020660 47.4432 238.95 269 287.00 304.0 324 349.0 396.10 0.9996761
#> 20: 115.713601 97.8516 235.00 288 329.65 368.0 406 462.2 602.00 1.0026839
#> sd mad q5 q20 q35 q50 q65 q80 q95 rhat
#> ess_bulk ess_tail
#> <num> <num>
#> 1: NA NA
#> 2: 1032.9494 1005.1434
#> 3: 927.8688 880.2070
#> 4: 1024.0598 943.1194
#> 5: 1192.6161 903.2221
#> 6: 1020.5322 1049.4998
#> 7: 1026.9068 976.3448
#> 8: 874.1838 752.9787
#> 9: 840.3245 950.3159
#> 10: 1046.7181 969.9759
#> 11: 884.3839 943.4474
#> 12: 857.4908 808.3249
#> 13: 942.5139 877.8069
#> 14: 1034.7024 934.5488
#> 15: 886.4727 919.5960
#> 16: 1073.2110 1004.6946
#> 17: 1455.4791 1067.2793
#> 18: 1075.3975 898.2947
#> 19: 1254.5799 923.0215
#> 20: 1616.8851 595.4893
#> ess_bulk ess_tail