Loads examples of posterior and forecast summaries produced
using example scripts. Used to streamline examples,
in package tests and to enable users to explore package functionality
without needing to install cmdstanr.
Arguments
- strains
Integer number of strains. Defaults to 2. Current maximum is 2.
- type
A character string indicating the example to load. Supported options are "posterior", "forecast", "observations", and "script" which are the output of
fv_tidy_posterior(),fv_extract_forecast(),filter_by_availability(with the date argument set to "2021-08-26" applied to the germany_covid19_delta_obs package dataset), and the script used to generate these examples respectively.
See also
Package data sets
germany_covid19_delta_obs
Examples
# Load the summarised posterior from an example fit of the one strain model
fv_example(strains = 1, type = "posterior")
#> Available value types: model, cases, growth, rt, raw
#> value_type variable clean_name date type obs
#> 1: model beta Beta <NA> NA
#> 2: model init_cases[1] Initial cases <NA> NA
#> 3: model phi[1] Notification overdispersion <NA> NA
#> 4: model r_init Initial growth <NA> NA
#> 5: model r_scale Growth (sd) <NA> NA
#> ---
#> 187: raw log_lik[11] <NA> NA
#> 188: raw log_lik[12] <NA> NA
#> 189: raw log_lik[13] <NA> NA
#> 190: raw log_lik[14] <NA> NA
#> 191: raw log_lik[15] <NA> NA
#> observed forecast_start exponentiated mean median sd
#> 1: NA NA FALSE 0.146 0.141 3.99e-01
#> 2: NA NA FALSE 89700.000 89400.000 6.15e+03
#> 3: NA NA FALSE 108.000 88.400 8.50e+01
#> 4: NA NA FALSE 0.141 0.139 7.16e-02
#> 5: NA NA FALSE 0.117 0.112 5.20e-02
#> ---
#> 187: NA NA NA -9.540 -9.470 5.69e-01
#> 188: NA NA NA -8.940 -8.900 4.33e-01
#> 189: NA NA NA -8.920 -8.800 6.72e-01
#> 190: NA NA NA -8.150 -8.050 5.99e-01
#> 191: NA NA NA -7.600 -7.500 6.33e-01
#> mad q5 q20 q80 q95 rhat ess_bulk ess_tail
#> 1: 4.40e-01 -5.09e-01 -2.10e-01 0.522 7.99e-01 1 1050 781
#> 2: 6.02e+03 7.97e+04 8.46e+04 94800.000 1.00e+05 1 1930 1340
#> 3: 5.50e+01 2.66e+01 4.91e+01 149.000 2.65e+02 1 728 920
#> 4: 6.78e-02 2.25e-02 8.41e-02 0.198 2.61e-01 1 1310 1190
#> 5: 5.04e-02 4.42e-02 7.16e-02 0.157 2.11e-01 1 930 1060
#> ---
#> 187: 4.97e-01 -1.06e+01 -9.92e+00 -9.080 -8.76e+00 1 1010 853
#> 188: 3.95e-01 -9.72e+00 -9.27e+00 -8.590 -8.31e+00 1 834 1140
#> 189: 5.21e-01 -1.02e+01 -9.34e+00 -8.420 -8.07e+00 1 1300 1110
#> 190: 5.16e-01 -9.26e+00 -8.57e+00 -7.670 -7.38e+00 1 1320 1320
#> 191: 4.71e-01 -8.76e+00 -7.96e+00 -7.140 -6.85e+00 1 833 993
# Load the summarised forecast from this posterior
fv_example(strains = 1, type = "forecast")
#> Available value types: cases, growth, rt
#> value_type type date horizon forecast_start mean
#> 1: cases Overall 2021-07-03 1 FALSE 3010.0000000
#> 2: cases Overall 2021-07-10 2 FALSE 1910.0000000
#> 3: cases Overall 2021-07-17 3 FALSE 1320.0000000
#> 4: cases Overall 2021-07-24 4 FALSE 1070.0000000
#> 5: growth Overall 2021-06-26 1 FALSE -0.4140714
#> 6: growth Overall 2021-07-03 2 FALSE -0.4085714
#> 7: growth Overall 2021-07-10 3 FALSE -0.4109286
#> 8: growth Overall 2021-07-17 4 FALSE -0.4085714
#> 9: rt Overall 2021-06-26 1 FALSE 0.6609537
#> 10: rt Overall 2021-07-03 2 FALSE 0.6645990
#> 11: rt Overall 2021-07-10 3 FALSE 0.6630343
#> 12: rt Overall 2021-07-17 4 FALSE 0.6645990
#> median sd mad q5 q20 q80
#> 1: 2910.0000000 797.000 661.000 1900.0000000 2400.0000000 3570.0000000
#> 2: 1720.0000000 942.000 660.000 843.0000000 1250.0000000 2450.0000000
#> 3: 1030.0000000 1440.000 592.000 342.0000000 628.0000000 1700.0000000
#> 4: 616.0000000 3900.000 477.000 133.0000000 297.0000000 1230.0000000
#> 5: -0.4140714 0.163 0.145 -0.6277857 -0.5107143 -0.3166429
#> 6: -0.4109286 0.227 0.198 -0.6945714 -0.5390000 -0.2757857
#> 7: -0.4148571 0.272 0.236 -0.7472143 -0.5696429 -0.2514286
#> 8: -0.4156429 0.317 0.276 -0.7857143 -0.5971429 -0.2231429
#> 9: 0.6609537 0.163 0.145 0.5337724 0.6000668 0.7285909
#> 10: 0.6630343 0.227 0.198 0.4992884 0.5833313 0.7589756
#> 11: 0.6604346 0.272 0.236 0.4736843 0.5657274 0.7776890
#> 12: 0.6599159 0.317 0.276 0.4557940 0.5503819 0.8000006
#> q95
#> 1: 4.470000e+03
#> 2: 3.420000e+03
#> 3: 3.010000e+03
#> 4: 2.780000e+03
#> 5: -2.145000e-01
#> 6: -1.265000e-01
#> 7: -6.655000e-02
#> 8: 4.714286e-03
#> 9: 8.069448e-01
#> 10: 8.811741e-01
#> 11: 9.356161e-01
#> 12: 1.004725e+00
# Load the script used to generate these examples
# Optionally source this script to regenerate the example
readLines(fv_example(strains = 1, type = "script"))
#> [1] "options(mc.cores = 2)"
#> [2] "obs <- filter_by_availability("
#> [3] " germany_covid19_delta_obs,"
#> [4] " date = \"2021-06-26\""
#> [5] ")"
#> [6] ""
#> [7] "current_obs <- filter_by_availability("
#> [8] " germany_covid19_delta_obs,"
#> [9] " date = \"2021-08-26\""
#> [10] ")"
#> [11] ""
#> [12] "dt <- fv_as_data_list("
#> [13] " obs,"
#> [14] " overdispersion = TRUE,"
#> [15] " voc_scale = c(0.4, 0.2)"
#> [16] ")"
#> [17] ""
#> [18] "inits <- fv_inits(dt, strains = 1)"
#> [19] ""
#> [20] "model <- suppressMessages(fv_model(strains = 1))"
#> [21] ""
#> [22] "fit <- fv_sample("
#> [23] " data = dt, model = model, init = inits,"
#> [24] " adapt_delta = 0.99, max_treedepth = 15, chains = 2"
#> [25] ")"
#> [26] ""
#> [27] "# summarise posterior assuming a mean generation time of 5.5 days."
#> [28] "posterior <- fv_tidy_posterior(fit, scale_r = 5.5 / 7)"
#> [29] "forecast <- fv_extract_forecast(posterior)"