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, in units of the timestep used during preprocessing. 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") or a numeric whole number representing the number of days. Note that "month" is not currently supported in user-facing functions and will throw an error if used.
- 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 0.000000 0.0000
#> 2: 1 18 0.000000000 167.480 167.0 1.403414 1.4826
#> 3: 1 17 0.000000000 135.797 136.0 1.858291 1.4826
#> 4: 1 16 0.000000000 141.272 141.0 2.414507 2.9652
#> 5: 1 15 0.007194245 145.764 145.0 3.070461 2.9652
#> 6: 1 14 0.000000000 103.614 103.0 2.892987 2.9652
#> 7: 1 13 0.000000000 62.838 63.0 2.595475 2.9652
#> 8: 1 12 0.000000000 185.149 185.0 3.655155 2.9652
#> 9: 1 11 0.000000000 256.191 256.0 7.084540 7.4130
#> 10: 1 10 0.004219409 267.189 266.0 8.269563 7.4130
#> 11: 1 9 0.000000000 236.218 235.0 8.635453 8.8956
#> 12: 1 8 0.015873016 230.463 230.0 10.395995 10.3782
#> 13: 1 7 0.040000000 165.184 165.0 9.660925 8.8956
#> 14: 1 6 0.010204082 129.535 129.0 8.499354 8.8956
#> 15: 1 5 0.012396694 293.236 292.0 11.922603 11.8608
#> 16: 1 4 0.017937220 293.392 292.0 15.981231 14.8260
#> 17: 1 3 0.019801980 291.537 290.0 20.271200 20.7564
#> 18: 1 2 0.070175439 295.258 292.5 29.356997 27.4281
#> 19: 1 1 0.383928571 310.440 304.0 50.020660 47.4432
#> 20: 1 0 1.000000000 384.142 368.0 115.713601 97.8516
#> 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: 149.00 149 149.00 149.0 149 149.0 149.00 NA NA NA
#> 2: 166.00 166 167.00 167.0 168 168.0 170.00 1.0011717 1032.9494 1005.1434
#> 3: 133.00 134 135.00 136.0 136 137.0 139.00 1.0023790 927.8688 880.2070
#> 4: 138.00 139 140.00 141.0 142 143.0 146.00 0.9997357 1024.0598 943.1194
#> 5: 141.00 143 144.00 145.0 147 148.0 151.00 1.0009523 1192.6161 903.2221
#> 6: 99.00 101 102.00 103.0 105 106.0 109.00 1.0056338 1020.5322 1049.4998
#> 7: 59.00 61 62.00 63.0 64 65.0 68.00 0.9989559 1026.9068 976.3448
#> 8: 180.00 182 183.00 185.0 186 188.0 192.00 1.0008924 874.1838 752.9787
#> 9: 246.00 250 253.00 256.0 258 262.0 269.00 1.0019152 840.3245 950.3159
#> 10: 255.00 260 264.00 266.0 270 274.0 282.00 0.9991677 1046.7181 969.9759
#> 11: 224.00 229 232.00 235.0 239 243.0 252.00 1.0025829 884.3839 943.4474
#> 12: 215.00 222 226.00 230.0 234 239.0 249.00 1.0004028 857.4908 808.3249
#> 13: 150.00 157 161.00 165.0 168 173.0 182.00 1.0063929 942.5139 877.8069
#> 14: 117.00 122 125.00 129.0 132 136.0 144.00 1.0015131 1034.7024 934.5488
#> 15: 276.00 283 288.00 292.0 297 302.0 315.00 1.0036606 886.4727 919.5960
#> 16: 269.00 280 286.00 292.0 299 306.0 321.00 0.9996327 1073.2110 1004.6946
#> 17: 262.00 274 282.00 290.0 298 308.0 327.05 0.9994497 1455.4791 1067.2793
#> 18: 252.00 271 282.00 292.5 304 317.0 350.00 0.9986679 1075.3975 898.2947
#> 19: 238.95 269 287.00 304.0 324 349.0 396.10 0.9996761 1254.5799 923.0215
#> 20: 235.00 288 329.65 368.0 406 462.2 602.00 1.0026839 1616.8851 595.4893
#> q5 q20 q35 q50 q65 q80 q95 rhat ess_bulk ess_tail