summary method for class "fv_tidy_posterior". Can be used to
filter the posterior for variables of interest, to return forecasts only, and
to summarise using the data.table method
Usage
# S3 method for fv_posterior
summary(object, type = "model", forecast = FALSE, as_dt = FALSE, ...)Arguments
- object
An object of the class
fv_posterioras returned byfv_tidy_posterior().- type
A character string used to filter the summarised output and defaulting to "model". Current options are: "model" which returns a summary of key model parameters, "cases" which returns summarised cases, "voc_frac" which returns summarised estimates of the fraction of cases that have the variant of concern, "voc_advantage" that returns summarised estimates of the the transmission advantage of the variant of concern, "growth" which returns summarised variant specific and overall growth rates, "rt" which returns summarised variant specific and overall reproduction number estimates, "raw" which returns a raw posterior summary, and "all" which returns all tidied posterior estimates.
- forecast
Logical defaults to
FALSE. Shouldfv_extract_forecast()be used to return only forecasts rather than complete posterior.- as_dt
Logical defaults to
FALSE. Once any filtering has been applied shouldsummary()fall back to using the defaultdata.tablemethod.- ...
Additional summary arguments.
Value
A summary data.table table unless type "all" is used in which case the output is still of type "fv_posterior".
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(),
update_voc_label()
Examples
posterior <- fv_example(strains = 2, type = "posterior")
# case summary
summary(posterior, 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 VOC NA TRUE FALSE
#> 21: sim_voc_cases[2] 2021-04-24 VOC NA TRUE FALSE
#> 22: sim_voc_cases[3] 2021-05-01 VOC NA TRUE FALSE
#> 23: sim_voc_cases[4] 2021-05-08 VOC NA TRUE FALSE
#> 24: sim_voc_cases[5] 2021-05-15 VOC NA TRUE FALSE
#> 25: sim_voc_cases[6] 2021-05-22 VOC NA TRUE FALSE
#> 26: sim_voc_cases[7] 2021-05-29 VOC NA TRUE FALSE
#> 27: sim_voc_cases[8] 2021-06-05 VOC NA TRUE FALSE
#> 28: sim_voc_cases[9] 2021-06-12 VOC NA TRUE TRUE
#> 29: sim_voc_cases[10] 2021-06-19 VOC NA FALSE FALSE
#> 30: sim_voc_cases[11] 2021-06-26 VOC NA FALSE FALSE
#> 31: sim_voc_cases[12] 2021-07-03 VOC NA FALSE FALSE
#> 32: sim_voc_cases[13] 2021-07-10 VOC NA FALSE FALSE
#> 33: sim_voc_cases[14] 2021-07-17 VOC NA FALSE FALSE
#> 34: sim_voc_cases[15] 2021-07-24 VOC NA FALSE FALSE
#> 35: sim_nvoc_cases[1] 2021-03-20 non-VOC NA TRUE FALSE
#> 36: sim_nvoc_cases[2] 2021-03-27 non-VOC NA TRUE FALSE
#> 37: sim_nvoc_cases[3] 2021-04-03 non-VOC NA TRUE FALSE
#> 38: sim_nvoc_cases[4] 2021-04-10 non-VOC NA TRUE FALSE
#> 39: sim_nvoc_cases[5] 2021-04-17 non-VOC NA TRUE FALSE
#> 40: sim_nvoc_cases[6] 2021-04-24 non-VOC NA TRUE FALSE
#> 41: sim_nvoc_cases[7] 2021-05-01 non-VOC NA TRUE FALSE
#> 42: sim_nvoc_cases[8] 2021-05-08 non-VOC NA TRUE FALSE
#> 43: sim_nvoc_cases[9] 2021-05-15 non-VOC NA TRUE FALSE
#> 44: sim_nvoc_cases[10] 2021-05-22 non-VOC NA TRUE FALSE
#> 45: sim_nvoc_cases[11] 2021-05-29 non-VOC NA TRUE FALSE
#> 46: sim_nvoc_cases[12] 2021-06-05 non-VOC NA TRUE FALSE
#> 47: sim_nvoc_cases[13] 2021-06-12 non-VOC NA TRUE TRUE
#> 48: sim_nvoc_cases[14] 2021-06-19 non-VOC NA FALSE FALSE
#> 49: sim_nvoc_cases[15] 2021-06-26 non-VOC NA FALSE FALSE
#> 50: sim_nvoc_cases[16] 2021-07-03 non-VOC NA FALSE FALSE
#> 51: sim_nvoc_cases[17] 2021-07-10 non-VOC NA FALSE FALSE
#> 52: sim_nvoc_cases[18] 2021-07-17 non-VOC NA FALSE FALSE
#> 53: sim_nvoc_cases[19] 2021-07-24 non-VOC 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 of the case summary
summary(posterior, type = "cases", as_dt = TRUE)
#> variable date type obs
#> Length:53 Min. :2021-03-20 Length:53 Min. : 5033
#> Class :character 1st Qu.:2021-04-24 Class :character 1st Qu.: 27842
#> Mode :character Median :2021-05-29 Mode :character Median : 87328
#> Mean :2021-05-25 Mean : 77848
#> 3rd Qu.:2021-06-26 3rd Qu.:113704
#> Max. :2021-07-24 Max. :145568
#> NA's :38
#> observed forecast_start mean median
#> Mode :logical Mode :logical Min. : 221 Min. : 194
#> FALSE:16 FALSE:50 1st Qu.: 1950 1st Qu.: 1670
#> TRUE :37 TRUE :3 Median : 8280 Median : 8220
#> Mean : 44054 Mean : 43670
#> 3rd Qu.:103000 3rd Qu.:102000
#> Max. :138000 Max. :137000
#>
#> sd mad q5 q20
#> Min. : 40.4 Min. : 37.1 Min. : 33 Min. : 85
#> 1st Qu.: 508.0 1st Qu.: 442.0 1st Qu.: 980 1st Qu.: 1240
#> Median : 2050.0 Median : 1190.0 Median : 6580 Median : 7400
#> Mean : 7040.5 Mean : 5987.3 Mean : 33450 Mean : 38715
#> 3rd Qu.:14900.0 3rd Qu.:13200.0 3rd Qu.: 78900 3rd Qu.: 91100
#> Max. :21200.0 Max. :18800.0 Max. :105000 Max. :122000
#>
#> q80 q95 rhat ess_bulk
#> Min. : 253 Min. : 291 Min. :0.9990 Min. :1600
#> 1st Qu.: 2290 1st Qu.: 2970 1st Qu.:1.0000 1st Qu.:1810
#> Median : 9010 Median : 10200 Median :1.0000 Median :1920
#> Mean : 48940 Mean : 55618 Mean :0.9999 Mean :1928
#> 3rd Qu.:113000 3rd Qu.:127000 3rd Qu.:1.0000 3rd Qu.:2050
#> Max. :153000 Max. :172000 Max. :1.0000 Max. :2340
#>
#> ess_tail
#> Min. :1190
#> 1st Qu.:1520
#> Median :1660
#> Mean :1641
#> 3rd Qu.:1760
#> Max. :2000
#>
# case forecast only
summary(posterior, type = "cases", forecast = TRUE)
#> type date horizon forecast_start mean median sd mad q5 q20
#> 1: Combined 2021-07-03 1 FALSE 3470 3320 990 830 2160 2710
#> 2: Combined 2021-07-10 2 FALSE 2620 2360 1310 1000 1130 1630
#> 3: Combined 2021-07-17 3 FALSE 2300 1790 1890 1100 529 1050
#> 4: Combined 2021-07-24 4 FALSE 2330 1450 2930 1190 259 673
#> 5: VOC 2021-06-19 1 FALSE 2100 2050 504 450 1380 1700
#> 6: VOC 2021-06-26 2 FALSE 1950 1880 505 429 1260 1560
#> 7: VOC 2021-07-03 3 FALSE 1810 1720 618 524 980 1320
#> 8: VOC 2021-07-10 4 FALSE 1730 1550 907 696 687 1030
#> 9: VOC 2021-07-17 5 FALSE 1780 1380 1500 872 395 789
#> 10: VOC 2021-07-24 6 FALSE 2010 1240 2520 1020 220 572
#> 11: non-VOC 2021-06-19 1 FALSE 6180 6090 1060 941 4660 5350
#> 12: non-VOC 2021-06-26 2 FALSE 3180 3120 660 549 2260 2670
#> 13: non-VOC 2021-07-03 3 FALSE 1660 1590 537 440 938 1240
#> 14: non-VOC 2021-07-10 4 FALSE 895 790 499 355 352 531
#> 15: non-VOC 2021-07-17 5 FALSE 514 388 453 259 117 218
#> 16: non-VOC 2021-07-24 6 FALSE 328 194 456 170 33 85
#> q80 q95
#> 1: 4130 5280
#> 2: 3380 5150
#> 3: 3090 5790
#> 4: 3150 7380
#> 5: 2450 2970
#> 6: 2290 2800
#> 7: 2240 2970
#> 8: 2290 3400
#> 9: 2400 4580
#> 10: 2740 6400
#> 11: 6930 7930
#> 12: 3630 4330
#> 13: 2000 2640
#> 14: 1170 1790
#> 15: 702 1310
#> 16: 438 1010
# voc fraction summary
summary(posterior, type = "voc_frac")
#> variable date type obs observed forecast_start mean
#> 1: frac_voc[1] 2021-04-17 VOC 0.001465917 TRUE FALSE 0.00168
#> 2: frac_voc[2] 2021-04-24 VOC 0.007085917 TRUE FALSE 0.00302
#> 3: frac_voc[3] 2021-05-01 VOC 0.015085025 TRUE FALSE 0.00542
#> 4: frac_voc[4] 2021-05-08 VOC 0.019443217 TRUE FALSE 0.00971
#> 5: frac_voc[5] 2021-05-15 VOC 0.026459592 TRUE FALSE 0.01740
#> 6: frac_voc[6] 2021-05-22 VOC 0.032900194 TRUE FALSE 0.03090
#> 7: frac_voc[7] 2021-05-29 VOC 0.038084020 TRUE FALSE 0.05450
#> 8: frac_voc[8] 2021-06-05 VOC 0.085600907 TRUE FALSE 0.09430
#> 9: frac_voc[9] 2021-06-12 VOC 0.166666667 TRUE TRUE 0.15800
#> 10: frac_voc[10] 2021-06-19 VOC NA FALSE FALSE 0.25300
#> 11: frac_voc[11] 2021-06-26 VOC NA FALSE FALSE 0.37800
#> 12: frac_voc[12] 2021-07-03 VOC NA FALSE FALSE 0.52000
#> 13: frac_voc[13] 2021-07-10 VOC NA FALSE FALSE 0.65800
#> 14: frac_voc[14] 2021-07-17 VOC NA FALSE FALSE 0.77400
#> 15: frac_voc[15] 2021-07-24 VOC NA FALSE FALSE 0.85800
#> median sd mad q5 q20 q80 q95 rhat ess_bulk
#> 1: 0.00166 0.000226 0.000203 0.00136 0.00150 0.00185 0.00207 1.000 1410
#> 2: 0.00298 0.000352 0.000324 0.00250 0.00273 0.00327 0.00362 1.000 1560
#> 3: 0.00538 0.000572 0.000530 0.00457 0.00495 0.00585 0.00637 1.000 1720
#> 4: 0.00965 0.001000 0.000944 0.00821 0.00891 0.01050 0.01140 1.000 1860
#> 5: 0.01730 0.001900 0.001750 0.01460 0.01590 0.01880 0.02050 1.000 1970
#> 6: 0.03070 0.003790 0.003340 0.02530 0.02800 0.03370 0.03740 1.000 1990
#> 7: 0.05420 0.007570 0.006670 0.04350 0.04840 0.05990 0.06750 1.000 2020
#> 8: 0.09360 0.014600 0.012700 0.07300 0.08280 0.10500 0.11900 0.999 1900
#> 9: 0.15700 0.026000 0.022700 0.12000 0.13800 0.17700 0.20300 1.000 1800
#> 10: 0.25000 0.041700 0.036300 0.19100 0.21900 0.28300 0.32500 1.000 1710
#> 11: 0.37600 0.057800 0.051400 0.29000 0.33000 0.42100 0.47500 1.000 1640
#> 12: 0.52000 0.067800 0.060800 0.41200 0.46500 0.57300 0.63100 1.000 1570
#> 13: 0.66200 0.067500 0.060800 0.54800 0.60500 0.71200 0.76300 1.000 1530
#> 14: 0.77800 0.058100 0.051600 0.67600 0.72900 0.82000 0.86000 1.000 1500
#> 15: 0.86300 0.044800 0.038400 0.78100 0.82500 0.89300 0.92100 1.000 1480
#> ess_tail
#> 1: 1140
#> 2: 1190
#> 3: 1340
#> 4: 1640
#> 5: 1540
#> 6: 1400
#> 7: 1340
#> 8: 1380
#> 9: 1290
#> 10: 1240
#> 11: 1180
#> 12: 1190
#> 13: 1210
#> 14: 1220
#> 15: 1240
# voc advantage summary
summary(posterior, type = "voc_advantage")
#> variable date type observed forecast_start mean median
#> 1: voc_advantage[1] 2021-04-17 VOC TRUE FALSE 1.588493 1.588493
#> 2: voc_advantage[2] 2021-04-24 VOC TRUE FALSE 1.588493 1.588493
#> 3: voc_advantage[3] 2021-05-01 VOC TRUE FALSE 1.588493 1.588493
#> 4: voc_advantage[4] 2021-05-08 VOC TRUE FALSE 1.588493 1.588493
#> 5: voc_advantage[5] 2021-05-15 VOC TRUE FALSE 1.588493 1.588493
#> 6: voc_advantage[6] 2021-05-22 VOC TRUE FALSE 1.588493 1.588493
#> 7: voc_advantage[7] 2021-05-29 VOC TRUE FALSE 1.588493 1.588493
#> 8: voc_advantage[8] 2021-06-05 VOC TRUE TRUE 1.588493 1.588493
#> 9: voc_advantage[9] 2021-06-12 VOC FALSE FALSE 1.588493 1.588493
#> 10: voc_advantage[10] 2021-06-19 VOC FALSE FALSE 1.588493 1.588493
#> 11: voc_advantage[11] 2021-06-26 VOC FALSE FALSE 1.588493 1.588493
#> 12: voc_advantage[12] 2021-07-03 VOC FALSE FALSE 1.588493 1.588493
#> 13: voc_advantage[13] 2021-07-10 VOC FALSE FALSE 1.588493 1.588493
#> 14: voc_advantage[14] 2021-07-17 VOC FALSE FALSE 1.588493 1.588493
#> sd mad q5 q20 q80 q95 rhat ess_bulk ess_tail
#> 1: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 2: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 3: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 4: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 5: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 6: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 7: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 8: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 9: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 10: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 11: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 12: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 13: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
#> 14: 0.0308 0.0297 1.5297 1.557595 1.618732 1.65214 1 1270 1250
# growth summary
summary(posterior, type = "growth")
#> variable date type observed forecast_start mean
#> 1: com_r[1] 2021-03-20 Combined TRUE FALSE 0.10450000
#> 2: com_r[2] 2021-03-27 Combined TRUE FALSE 0.07935714
#> 3: com_r[3] 2021-04-03 Combined TRUE FALSE 0.05256429
#> 4: com_r[4] 2021-04-10 Combined TRUE FALSE 0.07016429
#> 5: com_r[5] 2021-04-17 Combined TRUE FALSE 0.02867857
#> 6: com_r[6] 2021-04-24 Combined TRUE FALSE -0.05617857
#> 7: com_r[7] 2021-05-01 Combined TRUE FALSE -0.14614286
#> 8: com_r[8] 2021-05-08 Combined TRUE FALSE -0.22628571
#> 9: com_r[9] 2021-05-15 Combined TRUE FALSE -0.28128571
#> 10: com_r[10] 2021-05-22 Combined TRUE FALSE -0.33550000
#> 11: com_r[11] 2021-05-29 Combined TRUE FALSE -0.34257143
#> 12: com_r[12] 2021-06-05 Combined TRUE FALSE -0.35907143
#> 13: com_r[13] 2021-06-12 Combined TRUE FALSE -0.39757143
#> 14: com_r[14] 2021-06-19 Combined TRUE TRUE -0.34885714
#> 15: com_r[15] 2021-06-26 Combined FALSE FALSE -0.29700000
#> 16: com_r[16] 2021-07-03 Combined FALSE FALSE -0.24121429
#> 17: com_r[17] 2021-07-10 Combined FALSE FALSE -0.19014286
#> 18: com_r[18] 2021-07-17 Combined FALSE FALSE -0.15635714
#> 19: voc_r[1] 2021-04-17 VOC TRUE FALSE 0.49028571
#> 20: voc_r[2] 2021-04-24 VOC TRUE FALSE 0.40385714
#> 21: voc_r[3] 2021-05-01 VOC TRUE FALSE 0.31192857
#> 22: voc_r[4] 2021-05-08 VOC TRUE FALSE 0.22864286
#> 23: voc_r[5] 2021-05-15 VOC TRUE FALSE 0.16735714
#> 24: voc_r[6] 2021-05-22 VOC TRUE FALSE 0.10214286
#> 25: voc_r[7] 2021-05-29 VOC TRUE FALSE 0.07613571
#> 26: voc_r[8] 2021-06-05 VOC TRUE TRUE 0.02962143
#> 27: voc_r[9] 2021-06-12 VOC FALSE FALSE -0.05272143
#> 28: voc_r[10] 2021-06-19 VOC FALSE FALSE -0.06222857
#> 29: voc_r[11] 2021-06-26 VOC FALSE FALSE -0.07613571
#> 30: voc_r[12] 2021-07-03 VOC FALSE FALSE -0.08485714
#> 31: voc_r[13] 2021-07-10 VOC FALSE FALSE -0.08721429
#> 32: voc_r[14] 2021-07-17 VOC FALSE FALSE -0.09192857
#> 33: r[1] 2021-03-20 non-VOC TRUE FALSE 0.10450000
#> 34: r[2] 2021-03-27 non-VOC TRUE FALSE 0.07935714
#> 35: r[3] 2021-04-03 non-VOC TRUE FALSE 0.05256429
#> 36: r[4] 2021-04-10 non-VOC TRUE FALSE 0.07016429
#> 37: r[5] 2021-04-17 non-VOC TRUE FALSE 0.02726429
#> 38: r[6] 2021-04-24 non-VOC TRUE FALSE -0.05869286
#> 39: r[7] 2021-05-01 non-VOC TRUE FALSE -0.15085714
#> 40: r[8] 2021-05-08 non-VOC TRUE FALSE -0.23414286
#> 41: r[9] 2021-05-15 non-VOC TRUE FALSE -0.29542857
#> 42: r[10] 2021-05-22 non-VOC TRUE FALSE -0.36064286
#> 43: r[11] 2021-05-29 non-VOC TRUE FALSE -0.38657143
#> 44: r[12] 2021-06-05 non-VOC TRUE TRUE -0.43292857
#> 45: r[13] 2021-06-12 non-VOC FALSE FALSE -0.51542857
#> 46: r[14] 2021-06-19 non-VOC FALSE FALSE -0.52485714
#> 47: r[15] 2021-06-26 non-VOC FALSE FALSE -0.53900000
#> 48: r[16] 2021-07-03 non-VOC FALSE FALSE -0.54764286
#> 49: r[17] 2021-07-10 non-VOC FALSE FALSE -0.54921429
#> 50: r[18] 2021-07-17 non-VOC FALSE FALSE -0.55471429
#> variable date type observed forecast_start mean
#> median sd mad q5 q20 q80 q95
#> 1: 0.10607143 0.0762 0.0721 0.006961429 0.05790714 0.151642857 0.20035714
#> 2: 0.08014286 0.0637 0.0569 -0.001367143 0.04250714 0.117071429 0.15635714
#> 3: 0.05704286 0.0713 0.0600 -0.045728571 0.01304286 0.094285714 0.13514286
#> 4: 0.06733571 0.0763 0.0627 -0.027421429 0.02757857 0.113142857 0.17285714
#> 5: 0.02647857 0.0731 0.0633 -0.057357143 -0.01422143 0.071264286 0.12021429
#> 6: -0.05672857 0.0670 0.0583 -0.141428571 -0.09428571 -0.018228571 0.02915000
#> 7: -0.14535714 0.0687 0.0598 -0.236500000 -0.18621429 -0.106857143 -0.05704286
#> 8: -0.22471429 0.0696 0.0595 -0.316642857 -0.26478571 -0.185428571 -0.13671429
#> 9: -0.28128571 0.0717 0.0586 -0.371642857 -0.32214286 -0.241214286 -0.18778571
#> 10: -0.33392857 0.0690 0.0583 -0.427428571 -0.37478571 -0.295428571 -0.25378571
#> 11: -0.34571429 0.0698 0.0592 -0.428214286 -0.38264286 -0.305642857 -0.24750000
#> 12: -0.36300000 0.0703 0.0615 -0.443928571 -0.40071429 -0.318214286 -0.26557143
#> 13: -0.39442857 0.0803 0.0715 -0.507571429 -0.44550000 -0.348071429 -0.30328571
#> 14: -0.34885714 0.1030 0.0966 -0.476928571 -0.41250000 -0.286785714 -0.21921429
#> 15: -0.29542857 0.1710 0.1600 -0.517785714 -0.40150000 -0.190142857 -0.07542857
#> 16: -0.24200000 0.2350 0.2200 -0.540571429 -0.38028571 -0.091928571 0.04635714
#> 17: -0.18935714 0.2840 0.2660 -0.553142857 -0.36378571 -0.004392143 0.16971429
#> 18: -0.15085714 0.3380 0.3070 -0.591642857 -0.35907143 0.046985714 0.26635714
#> 19: 0.48792857 0.0750 0.0665 0.400714286 0.44785714 0.534285714 0.58850000
#> 20: 0.40385714 0.0687 0.0609 0.315071429 0.36378571 0.446285714 0.48871429
#> 21: 0.31192857 0.0725 0.0663 0.215285714 0.26792857 0.357500000 0.40150000
#> 22: 0.23021429 0.0735 0.0678 0.130428571 0.18307143 0.274214286 0.31900000
#> 23: 0.16814286 0.0784 0.0668 0.067964286 0.12021429 0.210571429 0.26950000
#> 24: 0.10371429 0.0733 0.0658 0.005366429 0.05853571 0.146928571 0.18935714
#> 25: 0.07409286 0.0739 0.0640 -0.012650000 0.03182143 0.118642857 0.17600000
#> 26: 0.02757857 0.0724 0.0638 -0.058300000 -0.01272857 0.070242857 0.12571429
#> 27: -0.04957857 0.0802 0.0729 -0.162642857 -0.09900000 -0.003370714 0.04447143
#> 28: -0.06317143 0.1020 0.0955 -0.189357143 -0.12492857 -0.000181500 0.06859286
#> 29: -0.07637143 0.1700 0.1600 -0.293857143 -0.18071429 0.031664286 0.14535714
#> 30: -0.08485714 0.2340 0.2190 -0.388142857 -0.22314286 0.065057143 0.20664286
#> 31: -0.08642857 0.2840 0.2680 -0.454928571 -0.26085714 0.095071429 0.27185714
#> 32: -0.08485714 0.3380 0.3110 -0.528785714 -0.29542857 0.117071429 0.33707143
#> 33: 0.10607143 0.0762 0.0721 0.006961429 0.05790714 0.151642857 0.20035714
#> 34: 0.08014286 0.0637 0.0569 -0.001367143 0.04250714 0.117071429 0.15635714
#> 35: 0.05704286 0.0713 0.0600 -0.045728571 0.01304286 0.094285714 0.13514286
#> 36: 0.06733571 0.0763 0.0627 -0.027421429 0.02757857 0.113142857 0.17285714
#> 37: 0.02498571 0.0731 0.0633 -0.058614286 -0.01563571 0.069850000 0.11864286
#> 38: -0.05908571 0.0670 0.0582 -0.143785714 -0.09664286 -0.020742857 0.02647857
#> 39: -0.15007143 0.0687 0.0597 -0.241214286 -0.19092857 -0.110785714 -0.06160000
#> 40: -0.23335714 0.0697 0.0593 -0.324500000 -0.27264286 -0.193285714 -0.14378571
#> 41: -0.29542857 0.0717 0.0589 -0.385785714 -0.33707143 -0.254571429 -0.20271429
#> 42: -0.35907143 0.0691 0.0583 -0.451000000 -0.39992857 -0.321357143 -0.27735714
#> 43: -0.38971429 0.0703 0.0603 -0.470642857 -0.42664286 -0.347285714 -0.29071429
#> 44: -0.43607143 0.0723 0.0643 -0.520928571 -0.47692857 -0.390500000 -0.33785714
#> 45: -0.51307143 0.0831 0.0758 -0.627785714 -0.56571429 -0.463571429 -0.41564286
#> 46: -0.52642857 0.1070 0.1010 -0.659214286 -0.59007143 -0.460428571 -0.38185714
#> 47: -0.53900000 0.1730 0.1650 -0.762142857 -0.64585714 -0.430571429 -0.31821429
#> 48: -0.54607143 0.2370 0.2230 -0.856428571 -0.68907143 -0.394428571 -0.25378571
#> 49: -0.55157143 0.2860 0.2730 -0.919285714 -0.72521429 -0.366928571 -0.18857143
#> 50: -0.55000000 0.3400 0.3150 -1.005714286 -0.75978571 -0.344142857 -0.13042857
#> median sd mad q5 q20 q80 q95
#> rhat ess_bulk ess_tail
#> 1: 1.000 950 923
#> 2: 1.000 1530 1550
#> 3: 1.000 1190 1520
#> 4: 1.000 1050 1330
#> 5: 1.000 1540 1530
#> 6: 1.000 1920 1760
#> 7: 1.000 1920 1290
#> 8: 1.000 1640 1330
#> 9: 1.000 1930 1400
#> 10: 1.000 1910 1760
#> 11: 1.000 1760 1560
#> 12: 0.999 1460 1660
#> 13: 1.000 1690 1660
#> 14: 1.000 2140 1790
#> 15: 1.000 1780 1360
#> 16: 1.000 1840 1340
#> 17: 1.000 1930 1400
#> 18: 1.000 1830 1470
#> 19: 1.000 1700 1700
#> 20: 1.000 1880 1820
#> 21: 1.000 1850 1670
#> 22: 1.000 1860 1460
#> 23: 1.000 1660 1480
#> 24: 1.000 1970 1860
#> 25: 1.000 1560 1530
#> 26: 1.000 1420 1660
#> 27: 1.000 1740 1660
#> 28: 1.000 2140 1700
#> 29: 1.000 1790 1460
#> 30: 1.000 1840 1290
#> 31: 1.000 1900 1320
#> 32: 1.000 1800 1540
#> 33: 1.000 950 923
#> 34: 1.000 1530 1550
#> 35: 1.000 1190 1520
#> 36: 1.000 1050 1330
#> 37: 1.000 1540 1590
#> 38: 1.000 1920 1760
#> 39: 1.000 1910 1290
#> 40: 1.000 1640 1350
#> 41: 1.000 1940 1390
#> 42: 1.000 1880 1620
#> 43: 1.000 1830 1540
#> 44: 1.000 1540 1670
#> 45: 1.000 1530 1530
#> 46: 1.000 2110 1640
#> 47: 1.000 1780 1400
#> 48: 1.000 1790 1430
#> 49: 1.000 1850 1390
#> 50: 1.000 1740 1620
#> rhat ess_bulk ess_tail
# Rt summary
summary(posterior, type = "rt")
#> variable date type observed forecast_start mean median
#> 1: com_r[1] 2021-03-20 Combined TRUE FALSE 1.1101554 1.1119013
#> 2: com_r[2] 2021-03-27 Combined TRUE FALSE 1.0825909 1.0834418
#> 3: com_r[3] 2021-04-03 Combined TRUE FALSE 1.0539703 1.0587012
#> 4: com_r[4] 2021-04-10 Combined TRUE FALSE 1.0726844 1.0696545
#> 5: com_r[5] 2021-04-17 Combined TRUE FALSE 1.0290938 1.0268322
#> 6: com_r[6] 2021-04-24 Combined TRUE FALSE 0.9453703 0.9448505
#> 7: com_r[7] 2021-05-01 Combined TRUE FALSE 0.8640343 0.8647134
#> 8: com_r[8] 2021-05-08 Combined TRUE FALSE 0.7974902 0.7987444
#> 9: com_r[9] 2021-05-15 Combined TRUE FALSE 0.7548126 0.7548126
#> 10: com_r[10] 2021-05-22 Combined TRUE FALSE 0.7149805 0.7161049
#> 11: com_r[11] 2021-05-29 Combined TRUE FALSE 0.7099424 0.7077147
#> 12: com_r[12] 2021-06-05 Combined TRUE FALSE 0.6983245 0.6955864
#> 13: com_r[13] 2021-06-12 Combined TRUE FALSE 0.6719499 0.6740651
#> 14: com_r[14] 2021-06-19 Combined TRUE TRUE 0.7054939 0.7054939
#> 15: com_r[15] 2021-06-26 Combined FALSE FALSE 0.7430440 0.7442126
#> 16: com_r[16] 2021-07-03 Combined FALSE FALSE 0.7856732 0.7850562
#> 17: com_r[17] 2021-07-10 Combined FALSE FALSE 0.8268410 0.8274909
#> 18: com_r[18] 2021-07-17 Combined FALSE FALSE 0.8552537 0.8599705
#> 19: voc_r[1] 2021-04-17 VOC TRUE FALSE 1.6327827 1.6289385
#> 20: voc_r[2] 2021-04-24 VOC TRUE FALSE 1.4975900 1.4975900
#> 21: voc_r[3] 2021-05-01 VOC TRUE FALSE 1.3660571 1.3660571
#> 22: voc_r[4] 2021-05-08 VOC TRUE FALSE 1.2568931 1.2588697
#> 23: voc_r[5] 2021-05-15 VOC TRUE FALSE 1.1821764 1.1831056
#> 24: voc_r[6] 2021-05-22 VOC TRUE FALSE 1.1075417 1.1092835
#> 25: voc_r[7] 2021-05-29 VOC TRUE FALSE 1.0791090 1.0769068
#> 26: voc_r[8] 2021-06-05 VOC TRUE TRUE 1.0300645 1.0279624
#> 27: voc_r[9] 2021-06-12 VOC FALSE FALSE 0.9486442 0.9516304
#> 28: voc_r[10] 2021-06-19 VOC FALSE FALSE 0.9396681 0.9387825
#> 29: voc_r[11] 2021-06-26 VOC FALSE FALSE 0.9266904 0.9264720
#> 30: voc_r[12] 2021-07-03 VOC FALSE FALSE 0.9186435 0.9186435
#> 31: voc_r[13] 2021-07-10 VOC FALSE FALSE 0.9164807 0.9172011
#> 32: voc_r[14] 2021-07-17 VOC FALSE FALSE 0.9121703 0.9186435
#> 33: r[1] 2021-03-20 non-VOC TRUE FALSE 1.1101554 1.1119013
#> 34: r[2] 2021-03-27 non-VOC TRUE FALSE 1.0825909 1.0834418
#> 35: r[3] 2021-04-03 non-VOC TRUE FALSE 1.0539703 1.0587012
#> 36: r[4] 2021-04-10 non-VOC TRUE FALSE 1.0726844 1.0696545
#> 37: r[5] 2021-04-17 non-VOC TRUE FALSE 1.0276394 1.0253005
#> 38: r[6] 2021-04-24 non-VOC TRUE FALSE 0.9429964 0.9426260
#> 39: r[7] 2021-05-01 non-VOC TRUE FALSE 0.8599705 0.8606465
#> 40: r[8] 2021-05-08 non-VOC TRUE FALSE 0.7912488 0.7918707
#> 41: r[9] 2021-05-15 non-VOC TRUE FALSE 0.7442126 0.7442126
#> 42: r[10] 2021-05-22 non-VOC TRUE FALSE 0.6972280 0.6983245
#> 43: r[11] 2021-05-29 non-VOC TRUE FALSE 0.6793822 0.6772503
#> 44: r[12] 2021-06-05 non-VOC TRUE TRUE 0.6486068 0.6465715
#> 45: r[13] 2021-06-12 non-VOC FALSE FALSE 0.5972446 0.5986540
#> 46: r[14] 2021-06-19 non-VOC FALSE FALSE 0.5916399 0.5907109
#> 47: r[15] 2021-06-26 non-VOC FALSE FALSE 0.5833313 0.5833313
#> 48: r[16] 2021-07-03 non-VOC FALSE FALSE 0.5783114 0.5792209
#> 49: r[17] 2021-07-10 non-VOC FALSE FALSE 0.5774033 0.5760439
#> 50: r[18] 2021-07-17 non-VOC FALSE FALSE 0.5742363 0.5769498
#> variable date type observed forecast_start mean median
#> sd mad q5 q20 q80 q95 rhat ess_bulk
#> 1: 0.0762 0.0721 1.0069857 1.0596166 1.1637445 1.2218391 1.000 950
#> 2: 0.0637 0.0569 0.9986338 1.0434235 1.1241997 1.1692437 1.000 1530
#> 3: 0.0713 0.0600 0.9553012 1.0131283 1.0988737 1.1447003 1.000 1190
#> 4: 0.0763 0.0627 0.9729511 1.0279624 1.1197919 1.1886963 1.000 1050
#> 5: 0.0731 0.0633 0.9442568 0.9858792 1.0738650 1.1277385 1.000 1540
#> 6: 0.0670 0.0583 0.8681172 0.9100227 0.9819366 1.0295790 1.000 1920
#> 7: 0.0687 0.0598 0.7893859 0.8300957 0.8986540 0.9445536 1.000 1920
#> 8: 0.0696 0.0595 0.7285909 0.7673704 0.8307482 0.8722194 1.000 1640
#> 9: 0.0717 0.0586 0.6896005 0.7245947 0.7856732 0.8287923 1.000 1930
#> 10: 0.0690 0.0583 0.6521840 0.6874366 0.7442126 0.7758580 1.000 1910
#> 11: 0.0698 0.0592 0.6516718 0.6820564 0.7366497 0.7807502 1.000 1760
#> 12: 0.0703 0.0615 0.6415112 0.6698414 0.7274469 0.7667677 0.999 1460
#> 13: 0.0803 0.0715 0.6019557 0.6405039 0.7060484 0.7383881 1.000 1690
#> 14: 0.1030 0.0966 0.6206869 0.6619932 0.7506726 0.8031496 1.000 2140
#> 15: 0.1710 0.1600 0.5958384 0.6693153 0.8268410 0.9273460 1.000 1780
#> 16: 0.2350 0.2200 0.5824153 0.6836660 0.9121703 1.0474484 1.000 1840
#> 17: 0.2840 0.2660 0.5751394 0.6950401 0.9956175 1.1849662 1.000 1930
#> 18: 0.3380 0.3070 0.5534174 0.6983245 1.0481070 1.3052011 1.000 1830
#> 19: 0.0750 0.0665 1.4928907 1.5649551 1.7062291 1.8012845 1.000 1700
#> 20: 0.0687 0.0609 1.3703572 1.4387659 1.5624978 1.6302189 1.000 1880
#> 21: 0.0725 0.0663 1.2402162 1.3072538 1.4297506 1.4940641 1.000 1850
#> 22: 0.0735 0.0678 1.1393166 1.2009002 1.3154967 1.3757513 1.000 1860
#> 23: 0.0784 0.0668 1.0703271 1.1277385 1.2343832 1.3093096 1.000 1660
#> 24: 0.0733 0.0658 1.0053809 1.0602829 1.1582712 1.2084725 1.000 1970
#> 25: 0.0739 0.0640 0.9874297 1.0323331 1.1259677 1.1924381 1.000 1560
#> 26: 0.0724 0.0638 0.9433669 0.9873521 1.0727687 1.1339581 1.000 1420
#> 27: 0.0802 0.0729 0.8498947 0.9057427 0.9966350 1.0454751 1.000 1740
#> 28: 0.1020 0.0955 0.8274909 0.8825599 0.9998185 1.0710001 1.000 2140
#> 29: 0.1700 0.1600 0.7453830 0.8346738 1.0321709 1.1564525 1.000 1790
#> 30: 0.2340 0.2190 0.6783154 0.8000006 1.0672200 1.2295434 1.000 1840
#> 31: 0.2840 0.2680 0.6344933 0.7703910 1.0997374 1.3123995 1.000 1900
#> 32: 0.3380 0.3110 0.5893201 0.7442126 1.1241997 1.4008391 1.000 1800
#> 33: 0.0762 0.0721 1.0069857 1.0596166 1.1637445 1.2218391 1.000 950
#> 34: 0.0637 0.0569 0.9986338 1.0434235 1.1241997 1.1692437 1.000 1530
#> 35: 0.0713 0.0600 0.9553012 1.0131283 1.0988737 1.1447003 1.000 1190
#> 36: 0.0763 0.0627 0.9729511 1.0279624 1.1197919 1.1886963 1.000 1050
#> 37: 0.0731 0.0633 0.9430705 0.9844859 1.0723473 1.1259677 1.000 1540
#> 38: 0.0670 0.0582 0.8660733 0.9078802 0.9794708 1.0268322 1.000 1920
#> 39: 0.0687 0.0597 0.7856732 0.8261916 0.8951305 0.9402589 1.000 1910
#> 40: 0.0697 0.0593 0.7228887 0.7613647 0.8242464 0.8660733 1.000 1640
#> 41: 0.0717 0.0589 0.6799162 0.7138578 0.7752487 0.8165115 1.000 1940
#> 42: 0.0691 0.0583 0.6369908 0.6703679 0.7251642 0.7577838 1.000 1880
#> 43: 0.0703 0.0603 0.6246006 0.6526966 0.7066034 0.7477293 1.000 1830
#> 44: 0.0723 0.0643 0.5939687 0.6206869 0.6767184 0.7132972 1.000 1540
#> 45: 0.0831 0.0758 0.5337724 0.5679543 0.6290331 0.6599159 1.000 1530
#> 46: 0.1070 0.1010 0.5172576 0.5542877 0.6310132 0.6825926 1.000 2110
#> 47: 0.1730 0.1650 0.4666654 0.5242130 0.6501375 0.7274469 1.000 1780
#> 48: 0.2370 0.2230 0.4246761 0.5020420 0.6740651 0.7758580 1.000 1790
#> 49: 0.2860 0.2730 0.3988038 0.4842208 0.6928591 0.8281413 1.000 1850
#> 50: 0.3400 0.3150 0.3657833 0.4677667 0.7088277 0.8777192 1.000 1740
#> sd mad q5 q20 q80 q95 rhat ess_bulk
#> ess_tail
#> 1: 923
#> 2: 1550
#> 3: 1520
#> 4: 1330
#> 5: 1530
#> 6: 1760
#> 7: 1290
#> 8: 1330
#> 9: 1400
#> 10: 1760
#> 11: 1560
#> 12: 1660
#> 13: 1660
#> 14: 1790
#> 15: 1360
#> 16: 1340
#> 17: 1400
#> 18: 1470
#> 19: 1700
#> 20: 1820
#> 21: 1670
#> 22: 1460
#> 23: 1480
#> 24: 1860
#> 25: 1530
#> 26: 1660
#> 27: 1660
#> 28: 1700
#> 29: 1460
#> 30: 1290
#> 31: 1320
#> 32: 1540
#> 33: 923
#> 34: 1550
#> 35: 1520
#> 36: 1330
#> 37: 1590
#> 38: 1760
#> 39: 1290
#> 40: 1350
#> 41: 1390
#> 42: 1620
#> 43: 1540
#> 44: 1670
#> 45: 1530
#> 46: 1640
#> 47: 1400
#> 48: 1430
#> 49: 1390
#> 50: 1620
#> ess_tail
# model parameter summary
summary(posterior, type = "model")
#> variable clean_name exponentiated mean
#> 1: avg_voc_advantage Average VOC 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 VOC 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 VOC 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
# raw posterior values
summary(posterior, type = "raw")
#> variable mean median sd mad q5 q20
#> 1: lp__ -3360.000 -3360.000 4.6500 4.5500 -3.37e+03 -3.36e+03
#> 2: r_init 0.133 0.135 0.0762 0.0721 8.86e-03 7.37e-02
#> 3: r_scale 0.118 0.111 0.0558 0.0542 4.40e-02 6.86e-02
#> 4: beta 0.230 0.260 0.4080 0.4530 -4.66e-01 -1.51e-01
#> 5: eta[1] -0.285 -0.308 0.8170 0.7920 -1.59e+00 -9.71e-01
#> ---
#> 278: log_lik[11] -14.700 -14.600 0.6930 0.6230 -1.60e+01 -1.52e+01
#> 279: log_lik[12] -13.800 -13.800 0.5790 0.5440 -1.48e+01 -1.43e+01
#> 280: log_lik[13] -13.500 -13.400 0.8670 0.7770 -1.51e+01 -1.41e+01
#> 281: log_lik[14] -8.160 -8.070 0.5980 0.4910 -9.20e+00 -8.56e+00
#> 282: log_lik[15] -7.730 -7.590 0.6970 0.5320 -9.01e+00 -8.15e+00
#> q80 q95 rhat ess_bulk ess_tail
#> 1: -3360.000 -3350.000 1.01 529 816
#> 2: 0.193 0.255 1.00 950 923
#> 3: 0.161 0.218 1.00 771 1310
#> 4: 0.621 0.846 1.00 779 923
#> 5: 0.380 1.050 1.00 1130 1060
#> ---
#> 278: -14.100 -13.700 1.00 1300 1500
#> 279: -13.400 -13.000 1.01 915 1160
#> 280: -12.700 -12.300 1.00 1220 1490
#> 281: -7.690 -7.420 1.00 1070 1250
#> 282: -7.200 -6.880 1.00 910 1320