Uses the observed variable returned by
fv_tidy_posterior() to return posterior predictions
for forecast dates only.
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.
Value
A data.frame of forecasts in the format returned
by fv_tidy_posterior() but with fitting variables dropped.
See also
Functions used for postprocessing of model fits
convert_to_stanfit(),
extract_draws(),
extract_forecast_dates(),
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(),
update_voc_label()
Examples
p <- fv_example(strains = 2, type = "posterior")
fv_extract_forecast(p)
#> Available value types: cases, voc_frac, voc_advantage, growth, rt
#> value_type type date horizon forecast_start mean
#> 1: cases Combined 2021-07-03 1 FALSE 3470.00000000
#> 2: cases Combined 2021-07-10 2 FALSE 2620.00000000
#> 3: cases Combined 2021-07-17 3 FALSE 2300.00000000
#> 4: cases Combined 2021-07-24 4 FALSE 2330.00000000
#> 5: cases VOC 2021-06-19 1 FALSE 2100.00000000
#> 6: cases VOC 2021-06-26 2 FALSE 1950.00000000
#> 7: cases VOC 2021-07-03 3 FALSE 1810.00000000
#> 8: cases VOC 2021-07-10 4 FALSE 1730.00000000
#> 9: cases VOC 2021-07-17 5 FALSE 1780.00000000
#> 10: cases VOC 2021-07-24 6 FALSE 2010.00000000
#> 11: cases non-VOC 2021-06-19 1 FALSE 6180.00000000
#> 12: cases non-VOC 2021-06-26 2 FALSE 3180.00000000
#> 13: cases non-VOC 2021-07-03 3 FALSE 1660.00000000
#> 14: cases non-VOC 2021-07-10 4 FALSE 895.00000000
#> 15: cases non-VOC 2021-07-17 5 FALSE 514.00000000
#> 16: cases non-VOC 2021-07-24 6 FALSE 328.00000000
#> 17: voc_frac VOC 2021-06-19 1 FALSE 0.25300000
#> 18: voc_frac VOC 2021-06-26 2 FALSE 0.37800000
#> 19: voc_frac VOC 2021-07-03 3 FALSE 0.52000000
#> 20: voc_frac VOC 2021-07-10 4 FALSE 0.65800000
#> 21: voc_frac VOC 2021-07-17 5 FALSE 0.77400000
#> 22: voc_frac VOC 2021-07-24 6 FALSE 0.85800000
#> 23: voc_advantage VOC 2021-06-12 1 FALSE 1.58849291
#> 24: voc_advantage VOC 2021-06-19 2 FALSE 1.58849291
#> 25: voc_advantage VOC 2021-06-26 3 FALSE 1.58849291
#> 26: voc_advantage VOC 2021-07-03 4 FALSE 1.58849291
#> 27: voc_advantage VOC 2021-07-10 5 FALSE 1.58849291
#> 28: voc_advantage VOC 2021-07-17 6 FALSE 1.58849291
#> 29: growth Combined 2021-06-26 1 FALSE -0.29700000
#> 30: growth Combined 2021-07-03 2 FALSE -0.24121429
#> 31: growth Combined 2021-07-10 3 FALSE -0.19014286
#> 32: growth Combined 2021-07-17 4 FALSE -0.15635714
#> 33: growth VOC 2021-06-12 1 FALSE -0.05272143
#> 34: growth VOC 2021-06-19 2 FALSE -0.06222857
#> 35: growth VOC 2021-06-26 3 FALSE -0.07613571
#> 36: growth VOC 2021-07-03 4 FALSE -0.08485714
#> 37: growth VOC 2021-07-10 5 FALSE -0.08721429
#> 38: growth VOC 2021-07-17 6 FALSE -0.09192857
#> 39: growth non-VOC 2021-06-12 1 FALSE -0.51542857
#> 40: growth non-VOC 2021-06-19 2 FALSE -0.52485714
#> 41: growth non-VOC 2021-06-26 3 FALSE -0.53900000
#> 42: growth non-VOC 2021-07-03 4 FALSE -0.54764286
#> 43: growth non-VOC 2021-07-10 5 FALSE -0.54921429
#> 44: growth non-VOC 2021-07-17 6 FALSE -0.55471429
#> 45: rt Combined 2021-06-26 1 FALSE 0.74304401
#> 46: rt Combined 2021-07-03 2 FALSE 0.78567325
#> 47: rt Combined 2021-07-10 3 FALSE 0.82684101
#> 48: rt Combined 2021-07-17 4 FALSE 0.85525369
#> 49: rt VOC 2021-06-12 1 FALSE 0.94864424
#> 50: rt VOC 2021-06-19 2 FALSE 0.93966808
#> 51: rt VOC 2021-06-26 3 FALSE 0.92669043
#> 52: rt VOC 2021-07-03 4 FALSE 0.91864351
#> 53: rt VOC 2021-07-10 5 FALSE 0.91648069
#> 54: rt VOC 2021-07-17 6 FALSE 0.91217030
#> 55: rt non-VOC 2021-06-12 1 FALSE 0.59724458
#> 56: rt non-VOC 2021-06-19 2 FALSE 0.59163988
#> 57: rt non-VOC 2021-06-26 3 FALSE 0.58333129
#> 58: rt non-VOC 2021-07-03 4 FALSE 0.57831137
#> 59: rt non-VOC 2021-07-10 5 FALSE 0.57740331
#> 60: rt non-VOC 2021-07-17 6 FALSE 0.57423631
#> value_type type date horizon forecast_start mean
#> median sd mad q5 q20 q80
#> 1: 3320.00000000 9.90e+02 8.30e+02 2160.0000000 2710.0000000 4.130000e+03
#> 2: 2360.00000000 1.31e+03 1.00e+03 1130.0000000 1630.0000000 3.380000e+03
#> 3: 1790.00000000 1.89e+03 1.10e+03 529.0000000 1050.0000000 3.090000e+03
#> 4: 1450.00000000 2.93e+03 1.19e+03 259.0000000 673.0000000 3.150000e+03
#> 5: 2050.00000000 5.04e+02 4.50e+02 1380.0000000 1700.0000000 2.450000e+03
#> 6: 1880.00000000 5.05e+02 4.29e+02 1260.0000000 1560.0000000 2.290000e+03
#> 7: 1720.00000000 6.18e+02 5.24e+02 980.0000000 1320.0000000 2.240000e+03
#> 8: 1550.00000000 9.07e+02 6.96e+02 687.0000000 1030.0000000 2.290000e+03
#> 9: 1380.00000000 1.50e+03 8.72e+02 395.0000000 789.0000000 2.400000e+03
#> 10: 1240.00000000 2.52e+03 1.02e+03 220.0000000 572.0000000 2.740000e+03
#> 11: 6090.00000000 1.06e+03 9.41e+02 4660.0000000 5350.0000000 6.930000e+03
#> 12: 3120.00000000 6.60e+02 5.49e+02 2260.0000000 2670.0000000 3.630000e+03
#> 13: 1590.00000000 5.37e+02 4.40e+02 938.0000000 1240.0000000 2.000000e+03
#> 14: 790.00000000 4.99e+02 3.55e+02 352.0000000 531.0000000 1.170000e+03
#> 15: 388.00000000 4.53e+02 2.59e+02 117.0000000 218.0000000 7.020000e+02
#> 16: 194.00000000 4.56e+02 1.70e+02 33.0000000 85.0000000 4.380000e+02
#> 17: 0.25000000 4.17e-02 3.63e-02 0.1910000 0.2190000 2.830000e-01
#> 18: 0.37600000 5.78e-02 5.14e-02 0.2900000 0.3300000 4.210000e-01
#> 19: 0.52000000 6.78e-02 6.08e-02 0.4120000 0.4650000 5.730000e-01
#> 20: 0.66200000 6.75e-02 6.08e-02 0.5480000 0.6050000 7.120000e-01
#> 21: 0.77800000 5.81e-02 5.16e-02 0.6760000 0.7290000 8.200000e-01
#> 22: 0.86300000 4.48e-02 3.84e-02 0.7810000 0.8250000 8.930000e-01
#> 23: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 24: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 25: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 26: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 27: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 28: 1.58849291 3.08e-02 2.97e-02 1.5296997 1.5575948 1.618732e+00
#> 29: -0.29542857 1.71e-01 1.60e-01 -0.5177857 -0.4015000 -1.901429e-01
#> 30: -0.24200000 2.35e-01 2.20e-01 -0.5405714 -0.3802857 -9.192857e-02
#> 31: -0.18935714 2.84e-01 2.66e-01 -0.5531429 -0.3637857 -4.392143e-03
#> 32: -0.15085714 3.38e-01 3.07e-01 -0.5916429 -0.3590714 4.698571e-02
#> 33: -0.04957857 8.02e-02 7.29e-02 -0.1626429 -0.0990000 -3.370714e-03
#> 34: -0.06317143 1.02e-01 9.55e-02 -0.1893571 -0.1249286 -1.815000e-04
#> 35: -0.07637143 1.70e-01 1.60e-01 -0.2938571 -0.1807143 3.166429e-02
#> 36: -0.08485714 2.34e-01 2.19e-01 -0.3881429 -0.2231429 6.505714e-02
#> 37: -0.08642857 2.84e-01 2.68e-01 -0.4549286 -0.2608571 9.507143e-02
#> 38: -0.08485714 3.38e-01 3.11e-01 -0.5287857 -0.2954286 1.170714e-01
#> 39: -0.51307143 8.31e-02 7.58e-02 -0.6277857 -0.5657143 -4.635714e-01
#> 40: -0.52642857 1.07e-01 1.01e-01 -0.6592143 -0.5900714 -4.604286e-01
#> 41: -0.53900000 1.73e-01 1.65e-01 -0.7621429 -0.6458571 -4.305714e-01
#> 42: -0.54607143 2.37e-01 2.23e-01 -0.8564286 -0.6890714 -3.944286e-01
#> 43: -0.55157143 2.86e-01 2.73e-01 -0.9192857 -0.7252143 -3.669286e-01
#> 44: -0.55000000 3.40e-01 3.15e-01 -1.0057143 -0.7597857 -3.441429e-01
#> 45: 0.74421257 1.71e-01 1.60e-01 0.5958384 0.6693153 8.268410e-01
#> 46: 0.78505618 2.35e-01 2.20e-01 0.5824153 0.6836660 9.121703e-01
#> 47: 0.82749092 2.84e-01 2.66e-01 0.5751394 0.6950401 9.956175e-01
#> 48: 0.85997054 3.38e-01 3.07e-01 0.5534174 0.6983245 1.048107e+00
#> 49: 0.95163038 8.02e-02 7.29e-02 0.8498947 0.9057427 9.966350e-01
#> 50: 0.93878253 1.02e-01 9.55e-02 0.8274909 0.8825599 9.998185e-01
#> 51: 0.92647202 1.70e-01 1.60e-01 0.7453830 0.8346738 1.032171e+00
#> 52: 0.91864351 2.34e-01 2.19e-01 0.6783154 0.8000006 1.067220e+00
#> 53: 0.91720106 2.84e-01 2.68e-01 0.6344933 0.7703910 1.099737e+00
#> 54: 0.91864351 3.38e-01 3.11e-01 0.5893201 0.7442126 1.124200e+00
#> 55: 0.59865403 8.31e-02 7.58e-02 0.5337724 0.5679543 6.290331e-01
#> 56: 0.59071089 1.07e-01 1.01e-01 0.5172576 0.5542877 6.310132e-01
#> 57: 0.58333129 1.73e-01 1.65e-01 0.4666654 0.5242130 6.501375e-01
#> 58: 0.57922086 2.37e-01 2.23e-01 0.4246761 0.5020420 6.740651e-01
#> 59: 0.57604389 2.86e-01 2.73e-01 0.3988038 0.4842208 6.928591e-01
#> 60: 0.57694981 3.40e-01 3.15e-01 0.3657833 0.4677667 7.088277e-01
#> median sd mad q5 q20 q80
#> q95
#> 1: 5280.00000000
#> 2: 5150.00000000
#> 3: 5790.00000000
#> 4: 7380.00000000
#> 5: 2970.00000000
#> 6: 2800.00000000
#> 7: 2970.00000000
#> 8: 3400.00000000
#> 9: 4580.00000000
#> 10: 6400.00000000
#> 11: 7930.00000000
#> 12: 4330.00000000
#> 13: 2640.00000000
#> 14: 1790.00000000
#> 15: 1310.00000000
#> 16: 1010.00000000
#> 17: 0.32500000
#> 18: 0.47500000
#> 19: 0.63100000
#> 20: 0.76300000
#> 21: 0.86000000
#> 22: 0.92100000
#> 23: 1.65214002
#> 24: 1.65214002
#> 25: 1.65214002
#> 26: 1.65214002
#> 27: 1.65214002
#> 28: 1.65214002
#> 29: -0.07542857
#> 30: 0.04635714
#> 31: 0.16971429
#> 32: 0.26635714
#> 33: 0.04447143
#> 34: 0.06859286
#> 35: 0.14535714
#> 36: 0.20664286
#> 37: 0.27185714
#> 38: 0.33707143
#> 39: -0.41564286
#> 40: -0.38185714
#> 41: -0.31821429
#> 42: -0.25378571
#> 43: -0.18857143
#> 44: -0.13042857
#> 45: 0.92734597
#> 46: 1.04744843
#> 47: 1.18496624
#> 48: 1.30520112
#> 49: 1.04547511
#> 50: 1.07100007
#> 51: 1.15645252
#> 52: 1.22954337
#> 53: 1.31239950
#> 54: 1.40083912
#> 55: 0.65991591
#> 56: 0.68259256
#> 57: 0.72744689
#> 58: 0.77585804
#> 59: 0.82814135
#> 60: 0.87771918
#> q95