A generic wrapper around enw_posterior() with
opinionated defaults to extract the posterior prediction for the
nowcast ("pp_inf_obs" from the stan code). The functionality of
this function can be used directly on the output of epinowcast() using
the supplied summary.epinowcast() method.
Usage
enw_nowcast_summary(
fit,
obs,
max_delay = NULL,
timestep = "day",
probs = c(0.05, 0.2, 0.35, 0.5, 0.65, 0.8, 0.95)
)Arguments
- fit
A
cmdstanrfit object.- 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().- max_delay
Maximum delay to which nowcasts should be summarised. 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.
- timestep
The timestep to used. This can be a string ("day", "week", "month") or a numeric whole number representing the number of days.
- probs
A vector of numeric probabilities to produce quantile summaries for. By default these are the 5%, 20%, 80%, and 95% quantiles which are also the minimum set required for plotting functions to work.
Value
A data.frame summarising the model posterior nowcast prediction.
This uses observed data where available and the posterior prediction
where not.
See also
Functions used for postprocessing of model fits
build_ord_obs(),
enw_add_latest_obs_to_nowcast(),
enw_nowcast_samples(),
enw_posterior(),
enw_pp_summary(),
enw_quantiles_to_long(),
enw_summarise_samples(),
subset_obs()
Examples
fit <- enw_example("nowcast")
enw_nowcast_summary(
fit$fit[[1]],
fit$latest[[1]],
fit$max_delay
)
#> 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
#> 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 0.000000 0.0000
#> 2: 1 18 0.000000000 167.426 167 1.316912 1.4826
#> 3: 1 17 0.000000000 135.812 136 1.867121 1.4826
#> 4: 1 16 0.000000000 141.255 141 2.341679 2.2239
#> 5: 1 15 0.007194245 145.884 146 3.085460 2.9652
#> 6: 1 14 0.000000000 103.681 103 3.072243 2.9652
#> 7: 1 13 0.000000000 62.762 62 2.461588 1.4826
#> 8: 1 12 0.000000000 185.138 185 3.827742 4.4478
#> 9: 1 11 0.000000000 255.903 255 6.410514 5.9304
#> 10: 1 10 0.004219409 267.488 267 7.975931 7.4130
#> 11: 1 9 0.000000000 236.164 236 8.459235 8.8956
#> 12: 1 8 0.015873016 230.867 230 10.092332 10.3782
#> 13: 1 7 0.040000000 165.026 165 10.254095 10.3782
#> 14: 1 6 0.010204082 129.307 128 8.532969 8.8956
#> 15: 1 5 0.012396694 292.749 292 11.792076 11.8608
#> 16: 1 4 0.017937220 293.296 292 15.992941 14.8260
#> 17: 1 3 0.019801980 291.404 290 19.436887 19.2738
#> 18: 1 2 0.070175439 295.703 292 28.861250 28.1694
#> 19: 1 1 0.383928571 309.456 304 51.295236 47.4432
#> 20: 1 0 1.000000000 381.243 371 103.890978 96.3690
#> cum_prop_reported delay prop_reported mean median sd mad
#> q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail
#> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 149.00 149.0 149 149 149 149.0 149.00 NA NA NA
#> 2: 166.00 166.0 167 167 168 168.0 170.00 0.9988498 997.2790 946.6047
#> 3: 133.00 134.0 135 136 136 137.0 139.00 0.9997035 1015.7372 1017.4859
#> 4: 138.00 139.0 140 141 142 143.0 145.00 1.0017705 901.5890 958.7197
#> 5: 141.00 143.0 144 146 147 148.0 151.05 0.9984520 1097.0842 1044.2543
#> 6: 99.00 101.0 102 103 105 106.0 109.00 0.9997410 1034.2365 913.6061
#> 7: 59.00 61.0 62 62 63 65.0 67.00 0.9987681 1012.1885 904.7496
#> 8: 180.00 182.0 183 185 186 188.0 192.00 1.0064762 885.6025 907.2186
#> 9: 246.00 250.0 253 255 258 261.0 267.00 1.0014583 933.4472 918.6322
#> 10: 255.00 261.0 264 267 270 274.0 281.00 1.0052639 1044.4533 1055.2781
#> 11: 223.00 229.0 232 236 239 243.0 252.00 1.0041063 1039.8102 897.5389
#> 12: 216.00 222.0 226 230 234 239.0 249.00 1.0026046 1018.9795 919.8161
#> 13: 149.95 156.8 160 165 168 173.0 183.00 0.9991933 1090.1636 782.0808
#> 14: 116.00 122.0 126 128 132 136.0 144.05 1.0000500 1023.7823 888.2873
#> 15: 275.00 283.0 288 292 297 303.0 313.00 0.9993709 1145.8224 968.8335
#> 16: 270.00 280.0 286 292 298 306.0 320.00 1.0008511 1056.0595 914.9342
#> 17: 263.00 274.0 284 290 298 306.0 325.00 0.9992936 1248.9469 968.8798
#> 18: 254.95 271.0 282 292 304 319.0 348.00 1.0008356 1412.5892 1036.8252
#> 19: 237.00 266.8 285 304 323 349.0 406.00 0.9988372 1195.0907 985.8485
#> 20: 240.85 293.8 332 371 405 459.2 569.05 0.9993392 1349.1168 1029.2555
#> q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail