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Automatic Vignette update (#848)
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86 changes: 43 additions & 43 deletions vignettes/EpiNow2.Rmd
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Expand Up @@ -152,11 +152,11 @@ knitr::kable(summary(estimates))

|measure |estimate |
|:--------------------------------|:----------------------|
|New infections per day |2235 (1258 -- 4079) |
|New infections per day |2275 (1102 -- 4554) |
|Expected change in daily reports |Likely decreasing |
|Effective reproduction no. |0.89 (0.69 -- 1.1) |
|Rate of growth |-0.029 (-0.1 -- 0.049) |
|Doubling/halving time (days) |-24 (14 -- -6.9) |
|Effective reproduction no. |0.89 (0.63 -- 1.2) |
|Rate of growth |-0.029 (-0.13 -- 0.07) |
|Doubling/halving time (days) |-24 (10 -- -5.4) |



Expand All @@ -165,22 +165,22 @@ Summarised parameter estimates can also easily be returned, either filtered for

``` r
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
#> <Date> <char> <char> <char> <num> <num> <num> <num>
#> 1: 2020-02-22 R <NA> estimate 2.295630 2.300011 0.14226384 2.072498
#> 2: 2020-02-23 R <NA> estimate 2.254414 2.260309 0.12775375 2.058350
#> 3: 2020-02-24 R <NA> estimate 2.213859 2.218421 0.11582310 2.039173
#> 4: 2020-02-25 R <NA> estimate 2.167609 2.174463 0.10645004 2.011138
#> 5: 2020-02-26 R <NA> estimate 2.119388 2.128587 0.09939624 1.977938
#> 6: 2020-02-27 R <NA> estimate 2.072939 2.080978 0.09424020 1.942162
#> date variable strat type median mean sd lower_90
#> <Date> <char> <char> <char> <num> <num> <num> <num>
#> 1: 2020-02-22 R <NA> estimate 2.306013 2.310790 0.1540239 2.056853
#> 2: 2020-02-23 R <NA> estimate 2.266565 2.271512 0.1360063 2.049787
#> 3: 2020-02-24 R <NA> estimate 2.226070 2.230026 0.1222073 2.028592
#> 4: 2020-02-25 R <NA> estimate 2.183114 2.186413 0.1121909 2.004851
#> 5: 2020-02-26 R <NA> estimate 2.137137 2.140814 0.1052495 1.974622
#> 6: 2020-02-27 R <NA> estimate 2.090623 2.093424 0.1005494 1.940482
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> <num> <num> <num> <num> <num>
#> 1: 2.199171 2.257930 2.331979 2.393862 2.536167
#> 2: 2.170840 2.220883 2.287825 2.346032 2.475402
#> 3: 2.137291 2.182338 2.242451 2.297515 2.410503
#> 4: 2.099378 2.138842 2.196990 2.246167 2.354895
#> 5: 2.058774 2.094525 2.147410 2.192443 2.298576
#> 6: 2.014358 2.047728 2.098028 2.142222 2.244473
#> 1: 2.205403 2.268372 2.343532 2.415484 2.562332
#> 2: 2.179397 2.233646 2.305061 2.365446 2.495602
#> 3: 2.143963 2.197360 2.260493 2.313351 2.438623
#> 4: 2.109778 2.155150 2.213032 2.262105 2.377589
#> 5: 2.068317 2.112241 2.163534 2.210975 2.317693
#> 6: 2.024142 2.064680 2.115606 2.160040 2.261932
```

Reported cases are returned in a separate data frame in order to streamline the reporting of forecasts and for model evaluation.
Expand All @@ -190,20 +190,20 @@ Reported cases are returned in a separate data frame in order to streamline the
head(summary(estimates, output = "estimated_reported_cases"))
#> date type median mean sd lower_90 lower_50 lower_20
#> <Date> <char> <num> <num> <num> <num> <num> <num>
#> 1: 2020-02-22 gp_rt 75 77.7545 22.45421 46 62.00 70
#> 2: 2020-02-23 gp_rt 87 89.3255 24.15807 53 73.00 82
#> 3: 2020-02-24 gp_rt 86 88.5850 23.75963 54 72.00 81
#> 4: 2020-02-25 gp_rt 80 82.0745 22.36889 49 66.00 75
#> 5: 2020-02-26 gp_rt 81 82.8155 22.64243 50 67.75 76
#> 6: 2020-02-27 gp_rt 107 110.9325 29.73030 66 90.00 101
#> 1: 2020-02-22 gp_rt 74 76.8805 21.41765 46 62 69
#> 2: 2020-02-23 gp_rt 85 88.0035 24.36979 53 71 80
#> 3: 2020-02-24 gp_rt 87 89.3710 24.82584 53 72 81
#> 4: 2020-02-25 gp_rt 79 81.6050 23.06389 48 66 74
#> 5: 2020-02-26 gp_rt 80 82.3575 22.85603 50 66 74
#> 6: 2020-02-27 gp_rt 107 110.2845 30.84153 66 88 100
#> upper_20 upper_50 upper_90
#> <num> <num> <num>
#> 1: 81 91 118
#> 2: 93 104 132
#> 3: 92 103 132
#> 4: 86 96 120
#> 5: 86 96 124
#> 6: 115 129 163
#> 1: 80 89 116
#> 2: 92 102 132
#> 3: 93 104 135
#> 4: 85 95 124
#> 5: 86 95 124
#> 6: 115 129 166
```

A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following `plot` method.
Expand Down Expand Up @@ -252,19 +252,19 @@ estimates <- regional_epinow(
gp = NULL,
stan = stan_opts(cores = 4, warmup = 250, samples = 1000)
)
#> INFO [2024-10-15 17:53:24] Producing following optional outputs: regions, summary, samples, plots, latest
#> INFO [2024-10-15 17:53:24] Reporting estimates using data up to: 2020-04-21
#> INFO [2024-10-15 17:53:24] No target directory specified so returning output
#> INFO [2024-10-15 17:53:24] Producing estimates for: testland, realland
#> INFO [2024-10-15 17:53:24] Regions excluded: none
#> INFO [2024-10-15 17:53:46] Completed estimates for: testland
#> INFO [2024-10-15 17:54:09] Completed estimates for: realland
#> INFO [2024-10-15 17:54:09] Completed regional estimates
#> INFO [2024-10-15 17:54:09] Regions with estimates: 2
#> INFO [2024-10-15 17:54:09] Regions with runtime errors: 0
#> INFO [2024-10-15 17:54:09] Producing summary
#> INFO [2024-10-15 17:54:09] No summary directory specified so returning summary output
#> INFO [2024-10-15 17:54:10] No target directory specified so returning timings
#> INFO [2024-11-01 00:25:14] Producing following optional outputs: regions, summary, samples, plots, latest
#> INFO [2024-11-01 00:25:14] Reporting estimates using data up to: 2020-04-21
#> INFO [2024-11-01 00:25:14] No target directory specified so returning output
#> INFO [2024-11-01 00:25:14] Producing estimates for: testland, realland
#> INFO [2024-11-01 00:25:14] Regions excluded: none
#> INFO [2024-11-01 00:25:36] Completed estimates for: testland
#> INFO [2024-11-01 00:26:00] Completed estimates for: realland
#> INFO [2024-11-01 00:26:00] Completed regional estimates
#> INFO [2024-11-01 00:26:00] Regions with estimates: 2
#> INFO [2024-11-01 00:26:00] Regions with runtime errors: 0
#> INFO [2024-11-01 00:26:00] Producing summary
#> INFO [2024-11-01 00:26:00] No summary directory specified so returning summary output
#> INFO [2024-11-01 00:26:00] No target directory specified so returning timings
```

Results from each region are stored in a `regional` list with across region summary measures and plots stored in a `summary` list. All results can be set to be internally saved by setting the `target_folder` and `summary_dir` arguments. Each region can be estimated in parallel using the `{future}` package (when in most scenarios `cores` should be set to 1). For routine use each MCMC chain can also be run in parallel (with `future` = TRUE) with a time out (`max_execution_time`) allowing for partial results to be returned if a subset of chains is running longer than expected. See the documentation for the `{future}` package for details on nested futures.
Expand Down

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