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