Assign a custom label to the variant of concern in the
output from fv_tidy_posterior().
Arguments
- posterior
A dataframe of posterior output as produced by
fv_tidy_posterior(). For forecast dates to be extracted data withvalue_type == "cases"must be present.- label
Character string indicating the new label to use for the variant of concern.
- target_label
A character string defaulting to "VOC". Indicates the current label for the variant of concern.
Value
A list of data frames as returned by `fv_tidy_posterior() but
with updated labels.
See also
Functions used for postprocessing of model fits
convert_to_stanfit(),
extract_draws(),
extract_forecast_dates(),
fv_extract_forecast(),
fv_posterior(),
fv_tidy_posterior(),
link_dates_with_posterior(),
link_obs_with_posterior(),
plot.fv_posterior(),
print.fv_posterior(),
quantiles_to_long(),
summary.fv_posterior()
Examples
p <- fv_example(strains = 2, type = "posterior")
p <- update_voc_label(p, "Delta")
summary(p, type = "cases")
#> variable date type obs observed forecast_start
#> 1: sim_cases[1] 2021-03-20 Combined 87328 TRUE FALSE
#> 2: sim_cases[2] 2021-03-27 Combined 109442 TRUE FALSE
#> 3: sim_cases[3] 2021-04-03 Combined 117965 TRUE FALSE
#> 4: sim_cases[4] 2021-04-10 Combined 107223 TRUE FALSE
#> 5: sim_cases[5] 2021-04-17 Combined 142664 TRUE FALSE
#> 6: sim_cases[6] 2021-04-24 Combined 145568 TRUE FALSE
#> 7: sim_cases[7] 2021-05-01 Combined 131887 TRUE FALSE
#> 8: sim_cases[8] 2021-05-08 Combined 107141 TRUE FALSE
#> 9: sim_cases[9] 2021-05-15 Combined 77261 TRUE FALSE
#> 10: sim_cases[10] 2021-05-22 Combined 57310 TRUE FALSE
#> 11: sim_cases[11] 2021-05-29 Combined 33052 TRUE FALSE
#> 12: sim_cases[12] 2021-06-05 Combined 22631 TRUE FALSE
#> 13: sim_cases[13] 2021-06-12 Combined 15553 TRUE FALSE
#> 14: sim_cases[14] 2021-06-19 Combined 7659 TRUE FALSE
#> 15: sim_cases[15] 2021-06-26 Combined 5033 TRUE TRUE
#> 16: sim_cases[16] 2021-07-03 Combined NA FALSE FALSE
#> 17: sim_cases[17] 2021-07-10 Combined NA FALSE FALSE
#> 18: sim_cases[18] 2021-07-17 Combined NA FALSE FALSE
#> 19: sim_cases[19] 2021-07-24 Combined NA FALSE FALSE
#> 20: sim_voc_cases[1] 2021-04-17 Delta NA TRUE FALSE
#> 21: sim_voc_cases[2] 2021-04-24 Delta NA TRUE FALSE
#> 22: sim_voc_cases[3] 2021-05-01 Delta NA TRUE FALSE
#> 23: sim_voc_cases[4] 2021-05-08 Delta NA TRUE FALSE
#> 24: sim_voc_cases[5] 2021-05-15 Delta NA TRUE FALSE
#> 25: sim_voc_cases[6] 2021-05-22 Delta NA TRUE FALSE
#> 26: sim_voc_cases[7] 2021-05-29 Delta NA TRUE FALSE
#> 27: sim_voc_cases[8] 2021-06-05 Delta NA TRUE FALSE
#> 28: sim_voc_cases[9] 2021-06-12 Delta NA TRUE TRUE
#> 29: sim_voc_cases[10] 2021-06-19 Delta NA FALSE FALSE
#> 30: sim_voc_cases[11] 2021-06-26 Delta NA FALSE FALSE
#> 31: sim_voc_cases[12] 2021-07-03 Delta NA FALSE FALSE
#> 32: sim_voc_cases[13] 2021-07-10 Delta NA FALSE FALSE
#> 33: sim_voc_cases[14] 2021-07-17 Delta NA FALSE FALSE
#> 34: sim_voc_cases[15] 2021-07-24 Delta NA FALSE FALSE
#> 35: sim_nvoc_cases[1] 2021-03-20 non-Delta NA TRUE FALSE
#> 36: sim_nvoc_cases[2] 2021-03-27 non-Delta NA TRUE FALSE
#> 37: sim_nvoc_cases[3] 2021-04-03 non-Delta NA TRUE FALSE
#> 38: sim_nvoc_cases[4] 2021-04-10 non-Delta NA TRUE FALSE
#> 39: sim_nvoc_cases[5] 2021-04-17 non-Delta NA TRUE FALSE
#> 40: sim_nvoc_cases[6] 2021-04-24 non-Delta NA TRUE FALSE
#> 41: sim_nvoc_cases[7] 2021-05-01 non-Delta NA TRUE FALSE
#> 42: sim_nvoc_cases[8] 2021-05-08 non-Delta NA TRUE FALSE
#> 43: sim_nvoc_cases[9] 2021-05-15 non-Delta NA TRUE FALSE
#> 44: sim_nvoc_cases[10] 2021-05-22 non-Delta NA TRUE FALSE
#> 45: sim_nvoc_cases[11] 2021-05-29 non-Delta NA TRUE FALSE
#> 46: sim_nvoc_cases[12] 2021-06-05 non-Delta NA TRUE FALSE
#> 47: sim_nvoc_cases[13] 2021-06-12 non-Delta NA TRUE TRUE
#> 48: sim_nvoc_cases[14] 2021-06-19 non-Delta NA FALSE FALSE
#> 49: sim_nvoc_cases[15] 2021-06-26 non-Delta NA FALSE FALSE
#> 50: sim_nvoc_cases[16] 2021-07-03 non-Delta NA FALSE FALSE
#> 51: sim_nvoc_cases[17] 2021-07-10 non-Delta NA FALSE FALSE
#> 52: sim_nvoc_cases[18] 2021-07-17 non-Delta NA FALSE FALSE
#> 53: sim_nvoc_cases[19] 2021-07-24 non-Delta NA FALSE FALSE
#> variable date type obs observed forecast_start
#> mean median sd mad q5 q20 q80 q95 rhat ess_bulk
#> 1: 89700 89100 13800.0 12200.0 68400 79200 100000 112000 1.000 1630
#> 2: 103000 102000 14900.0 13200.0 78900 91100 113000 127000 1.000 2030
#> 3: 114000 113000 17800.0 14700.0 87900 101000 126000 144000 1.000 1810
#> 4: 122000 121000 19200.0 16200.0 92500 107000 135000 154000 1.000 1820
#> 5: 133000 133000 20800.0 18000.0 101000 117000 149000 168000 1.000 1830
#> 6: 138000 137000 21200.0 18800.0 105000 122000 153000 172000 1.000 1860
#> 7: 129000 128000 20000.0 17400.0 97200 114000 143000 161000 1.000 1980
#> 8: 107000 106000 16700.0 13800.0 82300 94100 118000 133000 1.000 2230
#> 9: 79700 78900 12500.0 10700.0 61200 70400 88700 100000 0.999 2100
#> 10: 55400 54900 8090.0 7300.0 42600 49000 61500 69300 1.000 2090
#> 11: 35800 35500 5490.0 4620.0 27700 31800 39600 44700 1.000 2180
#> 12: 22900 22700 3420.0 2880.0 17800 20300 25200 28400 1.000 2050
#> 13: 14200 14100 2050.0 1700.0 11100 12700 15600 17500 1.000 1830
#> 14: 8280 8220 1170.0 971.0 6580 7400 9010 10200 1.000 1950
#> 15: 5130 5040 833.0 678.0 3990 4510 5670 6590 1.000 1960
#> 16: 3470 3320 990.0 830.0 2160 2710 4130 5280 1.000 1800
#> 17: 2620 2360 1310.0 1000.0 1130 1630 3380 5150 1.000 1850
#> 18: 2300 1790 1890.0 1100.0 529 1050 3090 5790 1.000 1970
#> 19: 2330 1450 2930.0 1190.0 259 673 3150 7380 1.000 1970
#> 20: 221 219 40.4 37.1 160 189 253 291 1.000 2000
#> 21: 417 411 82.6 72.6 301 353 475 559 0.999 1600
#> 22: 692 684 125.0 114.0 501 594 785 902 1.000 1780
#> 23: 1030 1010 203.0 177.0 735 871 1170 1380 1.000 2050
#> 24: 1380 1360 266.0 245.0 991 1160 1580 1840 0.999 2340
#> 25: 1710 1670 355.0 320.0 1200 1430 1970 2330 1.000 2230
#> 26: 1960 1910 437.0 372.0 1360 1630 2260 2690 1.000 2100
#> 27: 2150 2110 478.0 434.0 1480 1770 2510 3010 1.000 1770
#> 28: 2230 2180 508.0 442.0 1490 1840 2600 3120 1.000 1680
#> 29: 2100 2050 504.0 450.0 1380 1700 2450 2970 1.000 1790
#> 30: 1950 1880 505.0 429.0 1260 1560 2290 2800 1.000 1650
#> 31: 1810 1720 618.0 524.0 980 1320 2240 2970 1.000 1750
#> 32: 1730 1550 907.0 696.0 687 1030 2290 3400 1.000 1790
#> 33: 1780 1380 1500.0 872.0 395 789 2400 4580 1.000 2010
#> 34: 2010 1240 2520.0 1020.0 220 572 2740 6400 1.000 2000
#> 35: 89700 89100 13800.0 12200.0 68400 79200 100000 112000 1.000 1630
#> 36: 103000 102000 14900.0 13200.0 78900 91100 113000 127000 1.000 2030
#> 37: 114000 113000 17800.0 14700.0 87900 101000 126000 144000 1.000 1810
#> 38: 122000 121000 19200.0 16200.0 92500 107000 135000 154000 1.000 1820
#> 39: 133000 133000 20800.0 18100.0 101000 117000 148000 168000 1.000 1830
#> 40: 137000 137000 21200.0 18800.0 105000 121000 153000 171000 1.000 1860
#> 41: 128000 127000 20000.0 17400.0 96500 113000 142000 160000 1.000 1980
#> 42: 106000 105000 16600.0 13700.0 81300 93100 117000 132000 1.000 2230
#> 43: 78300 77500 12400.0 10700.0 60000 69100 87200 99000 0.999 2100
#> 44: 53600 53200 8020.0 7210.0 41000 47400 59800 67400 1.000 2090
#> 45: 33800 33500 5370.0 4500.0 25700 29800 37600 42500 1.000 2170
#> 46: 20700 20500 3320.0 2780.0 15800 18200 23000 26200 1.000 2080
#> 47: 12000 11800 1950.0 1650.0 9100 10500 13300 15100 1.000 1890
#> 48: 6180 6090 1060.0 941.0 4660 5350 6930 7930 1.000 1910
#> 49: 3180 3120 660.0 549.0 2260 2670 3630 4330 1.000 2010
#> 50: 1660 1590 537.0 440.0 938 1240 2000 2640 1.000 1920
#> 51: 895 790 499.0 355.0 352 531 1170 1790 1.000 1810
#> 52: 514 388 453.0 259.0 117 218 702 1310 1.000 1810
#> 53: 328 194 456.0 170.0 33 85 438 1010 1.000 1750
#> mean median sd mad q5 q20 q80 q95 rhat ess_bulk
#> ess_tail
#> 1: 1660
#> 2: 1750
#> 3: 1630
#> 4: 1580
#> 5: 1610
#> 6: 1520
#> 7: 1660
#> 8: 1980
#> 9: 1860
#> 10: 1810
#> 11: 1820
#> 12: 1820
#> 13: 1710
#> 14: 1720
#> 15: 1440
#> 16: 1540
#> 17: 1300
#> 18: 1460
#> 19: 1490
#> 20: 1760
#> 21: 1480
#> 22: 1660
#> 23: 1880
#> 24: 2000
#> 25: 1700
#> 26: 1820
#> 27: 1320
#> 28: 1190
#> 29: 1570
#> 30: 1270
#> 31: 1660
#> 32: 1500
#> 33: 1350
#> 34: 1480
#> 35: 1660
#> 36: 1750
#> 37: 1630
#> 38: 1580
#> 39: 1610
#> 40: 1520
#> 41: 1660
#> 42: 1970
#> 43: 1860
#> 44: 1820
#> 45: 1870
#> 46: 1790
#> 47: 1750
#> 48: 1670
#> 49: 1530
#> 50: 1290
#> 51: 1590
#> 52: 1700
#> 53: 1730
#> ess_tail
summary(p, type = "model")
#> variable clean_name exponentiated mean
#> 1: avg_voc_advantage Average Delta effect FALSE 0.589000
#> 2: beta Beta FALSE 0.230000
#> 3: init_cases[1] Initial cases FALSE 89800.000000
#> 4: init_voc_cases[1] Initial Delta cases FALSE 222.000000
#> 5: phi[1] Notification overdispersion FALSE 95.300000
#> 6: phi[2] Sequencing overdispersion FALSE 191.000000
#> 7: r_init Initial growth FALSE 0.133000
#> 8: r_scale Growth (sd) FALSE 0.118000
#> 9: voc_mod Initial Delta effect TRUE 1.802185
#> median sd mad q5 q20 q80
#> 1: 0.589000 3.08e-02 2.97e-02 0.541000 0.564000 0.613000
#> 2: 0.260000 4.08e-01 4.53e-01 -0.466000 -0.151000 0.621000
#> 3: 89600.000000 6.14e+03 5.82e+03 79900.000000 84800.000000 94600.000000
#> 4: 221.000000 2.27e+01 2.16e+01 186.000000 203.000000 240.000000
#> 5: 78.000000 7.42e+01 5.32e+01 21.200000 41.200000 135.000000
#> 6: 160.000000 1.33e+02 9.83e+01 44.500000 92.200000 268.000000
#> 7: 0.135000 7.62e-02 7.21e-02 0.008860 0.073700 0.193000
#> 8: 0.111000 5.58e-02 5.42e-02 0.044000 0.068600 0.161000
#> 9: 1.802185 3.08e-02 2.97e-02 1.717724 1.757689 1.845961
#> q95 rhat ess_bulk ess_tail
#> 1: 0.639000 1 1270 1250
#> 2: 0.846000 1 779 923
#> 3: 99900.000000 1 1360 1340
#> 4: 261.000000 1 1610 1060
#> 5: 231.000000 1 746 1030
#> 6: 443.000000 1 1760 1150
#> 7: 0.255000 1 950 923
#> 8: 0.218000 1 771 1310
#> 9: 1.894585 1 1270 1250