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
.check_primarycensored(),
.delay_draw_columns(),
.discretise_parametric_pmf(),
build_ord_obs(),
enw_nowcast_samples(),
enw_nowcast_summary(),
enw_posterior(),
enw_posterior_delay(),
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
#> <IDat> <num> <int> <int> <IDat> <int>
#> 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 167.570 167.0
#> 3: DE 00+ 1 17 0.000000000 135.841 136.0
#> 4: DE 00+ 1 16 0.000000000 141.576 141.0
#> 5: DE 00+ 1 15 0.007194245 146.294 146.0
#> 6: DE 00+ 1 14 0.000000000 104.040 104.0
#> 7: DE 00+ 1 13 0.000000000 62.986 63.0
#> 8: DE 00+ 1 12 0.000000000 185.872 186.0
#> 9: DE 00+ 1 11 0.000000000 257.301 257.0
#> 10: DE 00+ 1 10 0.004219409 268.618 268.0
#> 11: DE 00+ 1 9 0.000000000 237.750 237.0
#> 12: DE 00+ 1 8 0.015873016 232.124 231.0
#> 13: DE 00+ 1 7 0.040000000 164.968 164.0
#> 14: DE 00+ 1 6 0.010204082 129.748 129.0
#> 15: DE 00+ 1 5 0.012396694 298.836 298.0
#> 16: DE 00+ 1 4 0.017937220 299.495 299.0
#> 17: DE 00+ 1 3 0.019801980 300.872 299.0
#> 18: DE 00+ 1 2 0.070175439 301.149 299.5
#> 19: DE 00+ 1 1 0.383928571 310.948 304.5
#> 20: DE 00+ 1 0 1.000000000 322.429 310.5
#> location age_group cum_prop_reported delay prop_reported mean median
#> <fctr> <fctr> <num> <num> <num> <num> <num>
#> 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 149.00 149 149 149.0 149 149.0 149.00 NA
#> 2: 1.341976 1.4826 166.00 166 167 167.0 168 169.0 170.00 1.0018294
#> 3: 1.839865 1.4826 133.00 134 135 136.0 136 137.0 139.00 0.9999078
#> 4: 2.333167 2.9652 138.00 139 140 141.0 142 143.0 146.00 0.9995363
#> 5: 2.986383 2.9652 142.00 144 145 146.0 147 149.0 151.00 1.0049074
#> 6: 3.024822 2.9652 99.95 102 103 104.0 105 107.0 109.00 1.0009570
#> 7: 2.453125 2.9652 60.00 61 62 63.0 64 65.0 67.00 0.9997744
#> 8: 3.783640 4.4478 180.00 183 184 186.0 187 189.0 192.00 1.0024075
#> 9: 6.031150 5.9304 248.00 252 255 257.0 259 262.0 267.00 0.9990347
#> 10: 7.433797 7.4130 257.00 262 265 268.0 271 275.0 281.05 1.0002420
#> 11: 7.647090 7.4130 226.00 231 234 237.0 240 244.0 251.05 1.0009314
#> 12: 9.302750 8.8956 218.00 224 228 231.0 235 240.0 249.00 0.9990366
#> 13: 8.833516 8.8956 152.00 157 161 164.0 168 172.0 180.05 1.0017740
#> 14: 7.402790 7.4130 118.00 124 127 129.0 132 136.0 143.00 1.0016858
#> 15: 11.534477 10.3782 282.00 289 294 298.0 302 308.0 319.05 1.0011654
#> 16: 15.778623 16.3086 275.00 285 292 299.0 304 312.0 328.00 0.9999211
#> 17: 19.534206 17.7912 272.00 285 292 299.0 306 316.0 334.05 1.0007756
#> 18: 25.061901 25.9455 263.00 279 290 299.5 309 321.2 347.00 0.9985244
#> 19: 44.585526 40.7715 250.00 273 289 304.5 323 345.2 396.00 1.0000264
#> 20: 77.062219 71.9061 214.95 256 285 310.5 343 384.0 463.10 1.0017480
#> sd mad q5 q20 q35 q50 q65 q80 q95 rhat
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> ess_bulk ess_tail
#> <num> <num>
#> 1: NA NA
#> 2: 931.5700 943.7805
#> 3: 1110.6783 824.4184
#> 4: 976.8520 941.6408
#> 5: 1016.3094 1075.3542
#> 6: 952.3883 907.9565
#> 7: 939.2724 900.4309
#> 8: 963.8494 867.5415
#> 9: 1110.7851 1000.2993
#> 10: 896.3291 823.6561
#> 11: 1054.2097 821.3613
#> 12: 1066.2114 898.9808
#> 13: 827.0492 776.8752
#> 14: 940.2680 896.0489
#> 15: 1137.5067 958.8055
#> 16: 961.7466 971.0490
#> 17: 1114.9192 893.4042
#> 18: 1210.6829 900.7933
#> 19: 1166.8854 988.3457
#> 20: 1320.3486 883.1070
#> ess_bulk ess_tail
#> <num> <num>