Make your changes, commit to git, and then create a PR by running
usethis::pr_push(), and following the prompts in your browser. The title of your PR should briefly describe the change. The body of your PR should containFixes #issue-number.
+Make your changes, commit to git, and then create a PR by running
usethis::pr_push(), and following the prompts in your browser. The title of your PR should briefly describe the change. The body of your PR should containFixes #issue-number.For user-facing changes, add a bullet to the top of
NEWS.md(i.e. just below the first header). Follow the style described in https://style.tidyverse.org/news.html.
What happens after submitting a PR?
diff --git a/dev/index.html b/dev/index.html index c4066700a..a99c72314 100644 --- a/dev/index.html +++ b/dev/index.html @@ -201,6 +201,9 @@
stan = stan_opts(cores = 4, control = list(adapt_delta = 0.99)),
verbose = interactive()
)
+#> WARN [2023-09-27 14:28:15] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
+#> Running the chains for more iterations may help. See
+#> https://mc-stan.org/misc/warnings.html#bulk-ess -
names(estimates)
#> [1] "estimates" "estimated_reported_cases"
#> [3] "summary" "plots"
@@ -216,7 +219,7 @@
New confirmed cases by infection date
-2313 (1159 – 4345)
+2279 (1118 – 4301)
Expected change in daily cases
@@ -228,11 +231,11 @@
Rate of growth
--0.026 (-0.1 – 0.038)
+-0.027 (-0.11 – 0.038)
Doubling/halving time (days)
--26 (18 – -6.7)
+-25 (18 – -6.6)
@@ -240,36 +243,36 @@
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
-#> 1: 2020-02-22 R NA estimate 2.140044 2.142893 0.13818099 1.937615
-#> 2: 2020-02-23 R NA estimate 2.105628 2.106892 0.11415164 1.936612
-#> 3: 2020-02-24 R NA estimate 2.068985 2.069442 0.09420757 1.921287
-#> 4: 2020-02-25 R NA estimate 2.031434 2.030725 0.07830576 1.907767
-#> 5: 2020-02-26 R NA estimate 1.991226 1.990969 0.06634858 1.884688
-#> 6: 2020-02-27 R NA estimate 1.950962 1.950427 0.05807390 1.856440
+#> 1: 2020-02-22 R NA estimate 2.132829 2.137525 0.13485496 1.922173
+#> 2: 2020-02-23 R NA estimate 2.098537 2.101913 0.11121883 1.924287
+#> 3: 2020-02-24 R NA estimate 2.064197 2.064871 0.09229141 1.917506
+#> 4: 2020-02-25 R NA estimate 2.025665 2.026552 0.07781861 1.900920
+#> 5: 2020-02-26 R NA estimate 1.985770 1.987160 0.06736735 1.878103
+#> 6: 2020-02-27 R NA estimate 1.946217 1.946932 0.06027737 1.849663
#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: 2.046299 2.104219 2.174057 2.232616 2.370781
-#> 2: 2.025782 2.075403 2.132810 2.182697 2.294095
-#> 3: 2.003747 2.044222 2.090967 2.131610 2.225019
-#> 4: 1.977390 2.010528 2.048636 2.082264 2.163819
-#> 5: 1.944677 1.974170 2.008207 2.035011 2.103163
-#> 6: 1.910567 1.935790 1.965265 1.988672 2.046538
+#> 1: 2.046920 2.098708 2.166627 2.223948 2.366795
+#> 2: 2.027190 2.071652 2.128400 2.174681 2.290964
+#> 3: 2.002659 2.040515 2.087058 2.127006 2.220110
+#> 4: 1.974456 2.007299 2.044107 2.077997 2.158944
+#> 5: 1.942225 1.969654 2.002432 2.030157 2.099359
+#> 6: 1.907058 1.930582 1.961116 1.985619 2.045692
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
-#> 1: 2020-02-22 R NA estimate 2.140044 2.142893 0.13818099 1.937615
-#> 2: 2020-02-23 R NA estimate 2.105628 2.106892 0.11415164 1.936612
-#> 3: 2020-02-24 R NA estimate 2.068985 2.069442 0.09420757 1.921287
-#> 4: 2020-02-25 R NA estimate 2.031434 2.030725 0.07830576 1.907767
-#> 5: 2020-02-26 R NA estimate 1.991226 1.990969 0.06634858 1.884688
-#> 6: 2020-02-27 R NA estimate 1.950962 1.950427 0.05807390 1.856440
+#> 1: 2020-02-22 R NA estimate 2.132829 2.137525 0.13485496 1.922173
+#> 2: 2020-02-23 R NA estimate 2.098537 2.101913 0.11121883 1.924287
+#> 3: 2020-02-24 R NA estimate 2.064197 2.064871 0.09229141 1.917506
+#> 4: 2020-02-25 R NA estimate 2.025665 2.026552 0.07781861 1.900920
+#> 5: 2020-02-26 R NA estimate 1.985770 1.987160 0.06736735 1.878103
+#> 6: 2020-02-27 R NA estimate 1.946217 1.946932 0.06027737 1.849663
#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: 2.046299 2.104219 2.174057 2.232616 2.370781
-#> 2: 2.025782 2.075403 2.132810 2.182697 2.294095
-#> 3: 2.003747 2.044222 2.090967 2.131610 2.225019
-#> 4: 1.977390 2.010528 2.048636 2.082264 2.163819
-#> 5: 1.944677 1.974170 2.008207 2.035011 2.103163
-#> 6: 1.910567 1.935790 1.965265 1.988672 2.046538
+#> 1: 2.046920 2.098708 2.166627 2.223948 2.366795
+#> 2: 2.027190 2.071652 2.128400 2.174681 2.290964
+#> 3: 2.002659 2.040515 2.087058 2.127006 2.220110
+#> 4: 1.974456 2.007299 2.044107 2.077997 2.158944
+#> 5: 1.942225 1.969654 2.002432 2.030157 2.099359
+#> 6: 1.907058 1.930582 1.961116 1.985619 2.045692
head(summary(estimates, type = "parameters", params = "R"))
#> date variable strat type median mean sd lower_90
-#> 1: 2020-02-22 R NA estimate 2.140044 2.142893 0.13818099 1.937615
-#> 2: 2020-02-23 R NA estimate 2.105628 2.106892 0.11415164 1.936612
-#> 3: 2020-02-24 R NA estimate 2.068985 2.069442 0.09420757 1.921287
-#> 4: 2020-02-25 R NA estimate 2.031434 2.030725 0.07830576 1.907767
-#> 5: 2020-02-26 R NA estimate 1.991226 1.990969 0.06634858 1.884688
-#> 6: 2020-02-27 R NA estimate 1.950962 1.950427 0.05807390 1.856440
+#> 1: 2020-02-22 R NA estimate 2.132829 2.137525 0.13485496 1.922173
+#> 2: 2020-02-23 R NA estimate 2.098537 2.101913 0.11121883 1.924287
+#> 3: 2020-02-24 R NA estimate 2.064197 2.064871 0.09229141 1.917506
+#> 4: 2020-02-25 R NA estimate 2.025665 2.026552 0.07781861 1.900920
+#> 5: 2020-02-26 R NA estimate 1.985770 1.987160 0.06736735 1.878103
+#> 6: 2020-02-27 R NA estimate 1.946217 1.946932 0.06027737 1.849663
#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: 2.046299 2.104219 2.174057 2.232616 2.370781
-#> 2: 2.025782 2.075403 2.132810 2.182697 2.294095
-#> 3: 2.003747 2.044222 2.090967 2.131610 2.225019
-#> 4: 1.977390 2.010528 2.048636 2.082264 2.163819
-#> 5: 1.944677 1.974170 2.008207 2.035011 2.103163
-#> 6: 1.910567 1.935790 1.965265 1.988672 2.046538Reported cases are returned in a separate data frame in order to streamline the reporting of forecasts and for model evaluation.
head(summary(estimates, output = "estimated_reported_cases"))
#> date type median mean sd lower_90 lower_50 lower_20
-#> 1: 2020-02-22 gp_rt 65.5 67.2870 18.83096 40 54 61
-#> 2: 2020-02-23 gp_rt 78.0 78.8395 21.73755 47 63 72
-#> 3: 2020-02-24 gp_rt 77.0 78.8920 21.59142 47 64 72
-#> 4: 2020-02-25 gp_rt 73.0 75.0705 20.82804 45 61 68
-#> 5: 2020-02-26 gp_rt 78.0 79.8325 22.03166 47 65 73
-#> 6: 2020-02-27 gp_rt 110.0 112.9160 28.92359 71 92 103
+#> 1: 2020-02-22 gp_rt 61 62.9225 16.90344 39.00 51 57.0
+#> 2: 2020-02-23 gp_rt 76 77.0560 20.88159 46.00 62 70.6
+#> 3: 2020-02-24 gp_rt 77 79.0875 21.69463 48.95 64 72.0
+#> 4: 2020-02-25 gp_rt 75 76.1825 20.03599 46.00 62 70.0
+#> 5: 2020-02-26 gp_rt 79 81.1295 22.38634 49.00 65 74.0
+#> 6: 2020-02-27 gp_rt 113 115.4200 28.91020 71.00 95 106.0
#> upper_20 upper_50 upper_90
-#> 1: 70 79.00 101
-#> 2: 83 92.00 117
-#> 3: 82 92.00 116
-#> 4: 78 87.00 115
-#> 5: 83 91.25 120
-#> 6: 118 130.00 165A range of plots are returned (with the single summary plot shown below). These plots can also be generated using the following plot method.
plot(estimates)gp = NULL, stan = stan_opts(cores = 4, warmup = 250, samples = 1000) ) -#> INFO [2023-06-09 13:52:11] Producing following optional outputs: regions, summary, samples, plots, latest -#> INFO [2023-06-09 13:52:11] Reporting estimates using data up to: 2020-04-21 -#> INFO [2023-06-09 13:52:11] No target directory specified so returning output -#> INFO [2023-06-09 13:52:11] Producing estimates for: testland, realland -#> INFO [2023-06-09 13:52:11] Regions excluded: none -#> INFO [2023-06-09 13:52:40] Completed estimates for: testland -#> INFO [2023-06-09 13:53:07] Completed estimates for: realland -#> INFO [2023-06-09 13:53:07] Completed regional estimates -#> INFO [2023-06-09 13:53:07] Regions with estimates: 2 -#> INFO [2023-06-09 13:53:07] Regions with runtime errors: 0 -#> INFO [2023-06-09 13:53:07] Producing summary -#> INFO [2023-06-09 13:53:07] No summary directory specified so returning summary output -#> INFO [2023-06-09 13:53:08] No target directory specified so returning timings +#> INFO [2023-09-27 14:28:21] Producing following optional outputs: regions, summary, samples, plots, latest +#> INFO [2023-09-27 14:28:21] Reporting estimates using data up to: 2020-04-21 +#> INFO [2023-09-27 14:28:21] No target directory specified so returning output +#> INFO [2023-09-27 14:28:21] Producing estimates for: testland, realland +#> INFO [2023-09-27 14:28:21] Regions excluded: none +#> INFO [2023-09-27 14:28:54] Completed estimates for: testland +#> INFO [2023-09-27 14:29:24] Completed estimates for: realland +#> INFO [2023-09-27 14:29:24] Completed regional estimates +#> INFO [2023-09-27 14:29:24] Regions with estimates: 2 +#> INFO [2023-09-27 14:29:24] Regions with runtime errors: 0 +#> INFO [2023-09-27 14:29:24] Producing summary +#> INFO [2023-09-27 14:29:24] No summary directory specified so returning summary output +#> INFO [2023-09-27 14:29:25] 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.
Summary measures that are returned include a table formatted for reporting (along with raw results for further processing). Futures updated will extend the S3 methods used above to smooth access to this output.
@@ -341,19 +344,19 @@
realland -2176 (1192 – 4065) +2150 (1109 – 4062) Likely decreasing -0.87 (0.65 – 1.1) --0.032 (-0.096 – 0.03) --22 (23 – -7.2) +0.86 (0.64 – 1.1) +-0.033 (-0.098 – 0.029) +-21 (24 – -7.1) diff --git a/dev/news/index.html b/dev/news/index.html index 050435393..08435028b 100644 --- a/dev/news/index.html +++ b/dev/news/index.html @@ -61,6 +61,10 @@ testland -2217 (1150 – 4155) +2149 (1182 – 4040) Likely decreasing -0.87 (0.64 – 1.2) --0.031 (-0.099 – 0.036) --23 (19 – -7) +0.87 (0.66 – 1.2) +-0.031 (-0.093 – 0.035) +-22 (20 – -7.4) EpiNow2 1.4.0
CRAN release: 2023-09-26
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml index 0259910cc..85a1028b9 100644 --- a/dev/pkgdown.yml +++ b/dev/pkgdown.yml @@ -7,7 +7,7 @@ articles: estimate_secondary: estimate_secondary.html estimate_truncation: estimate_truncation.html gaussian_process_implementation_details: gaussian_process_implementation_details.html -last_built: 2023-09-27T17:02Z +last_built: 2023-10-03T09:53Z urls: reference: epiforecasts.io/EpiNow2/reference article: epiforecasts.io/EpiNow2/articles diff --git a/dev/reference/Rplot002.png b/dev/reference/Rplot002.png index 6f2409f5e..733407b51 100644 Binary files a/dev/reference/Rplot002.png and b/dev/reference/Rplot002.png differ diff --git a/dev/reference/Rplot003.png b/dev/reference/Rplot003.png index 099f44a16..70c392df7 100644 Binary files a/dev/reference/Rplot003.png and b/dev/reference/Rplot003.png differ diff --git a/dev/reference/Rplot004.png b/dev/reference/Rplot004.png index 6a586cb68..8adce7296 100644 Binary files a/dev/reference/Rplot004.png and b/dev/reference/Rplot004.png differ diff --git a/dev/reference/Rplot005.png b/dev/reference/Rplot005.png index 4d67da014..afc56dc5e 100644 Binary files a/dev/reference/Rplot005.png and b/dev/reference/Rplot005.png differ diff --git a/dev/reference/Rplot006.png b/dev/reference/Rplot006.png index 96b7a9b2e..75f016df9 100644 Binary files a/dev/reference/Rplot006.png and b/dev/reference/Rplot006.png differ diff --git a/dev/reference/Rplot007.png b/dev/reference/Rplot007.png index 25fb0a44b..747a543b4 100644 Binary files a/dev/reference/Rplot007.png and b/dev/reference/Rplot007.png differ diff --git a/dev/reference/Rplot008.png b/dev/reference/Rplot008.png index 1793e835a..82740d2cf 100644 Binary files a/dev/reference/Rplot008.png and b/dev/reference/Rplot008.png differ diff --git a/dev/reference/Rplot009.png b/dev/reference/Rplot009.png index 854c70588..edd408050 100644 Binary files a/dev/reference/Rplot009.png and b/dev/reference/Rplot009.png differ diff --git a/dev/reference/adjust_infection_to_report.html b/dev/reference/adjust_infection_to_report.html index d0aa172aa..39a39e35a 100644 --- a/dev/reference/adjust_infection_to_report.html +++ b/dev/reference/adjust_infection_to_report.html @@ -158,17 +158,17 @@Examples) print(report) #> date cases -#> 1: 2020-02-24 1 -#> 2: 2020-02-25 3 -#> 3: 2020-02-26 3 -#> 4: 2020-02-27 7 -#> 5: 2020-02-28 19 +#> 1: 2020-02-24 2 +#> 2: 2020-02-25 6 +#> 3: 2020-02-26 12 +#> 4: 2020-02-27 23 +#> 5: 2020-02-28 28 #> --- -#> 124: 2020-06-26 298 -#> 125: 2020-06-27 238 -#> 126: 2020-06-28 222 -#> 127: 2020-06-29 241 -#> 128: 2020-06-30 279 +#> 124: 2020-06-26 216 +#> 125: 2020-06-27 230 +#> 126: 2020-06-28 231 +#> 127: 2020-06-29 243 +#> 128: 2020-06-30 238 # mapping with a weekly reporting effect report_weekly <- adjust_infection_to_report( @@ -178,17 +178,17 @@
Examples) print(report_weekly) #> date cases -#> 1: 2020-02-25 2 -#> 2: 2020-02-26 3 -#> 3: 2020-02-27 8 -#> 4: 2020-02-28 12 -#> 5: 2020-02-29 22 +#> 1: 2020-02-25 5 +#> 2: 2020-02-26 8 +#> 3: 2020-02-27 24 +#> 4: 2020-02-28 23 +#> 5: 2020-02-29 39 #> --- -#> 123: 2020-06-26 250 -#> 124: 2020-06-27 229 -#> 125: 2020-06-28 219 -#> 126: 2020-06-29 259 -#> 127: 2020-06-30 265 +#> 123: 2020-06-26 219 +#> 124: 2020-06-27 181 +#> 125: 2020-06-28 251 +#> 126: 2020-06-29 277 +#> 127: 2020-06-30 242 # map using a deterministic median shift for both delays report_median <- adjust_infection_to_report(cases, @@ -197,17 +197,17 @@
Examples) print(report_median) #> date confirm cases -#> 1: 2020-03-02 14 14 -#> 2: 2020-03-03 62 62 -#> 3: 2020-03-04 53 53 -#> 4: 2020-03-05 97 97 -#> 5: 2020-03-06 93 93 +#> 1: 2020-02-29 14 14 +#> 2: 2020-03-01 62 62 +#> 3: 2020-03-02 53 53 +#> 4: 2020-03-03 97 97 +#> 5: 2020-03-04 93 93 #> --- -#> 126: 2020-07-05 296 296 -#> 127: 2020-07-06 255 255 -#> 128: 2020-07-07 175 175 -#> 129: 2020-07-08 174 174 -#> 130: 2020-07-09 126 126 +#> 126: 2020-07-03 296 296 +#> 127: 2020-07-04 255 255 +#> 128: 2020-07-05 175 175 +#> 129: 2020-07-06 174 174 +#> 130: 2020-07-07 126 126 # map with a weekly reporting effect and stochastic reporting model report_stochastic <- adjust_infection_to_report( @@ -221,17 +221,17 @@
Examples) print(report_stochastic) #> date cases -#> 1: 2020-02-25 3 -#> 2: 2020-02-26 0 -#> 3: 2020-02-27 4 -#> 4: 2020-02-28 26 -#> 5: 2020-02-29 41 +#> 1: 2020-02-24 1 +#> 2: 2020-02-25 3 +#> 3: 2020-02-26 22 +#> 4: 2020-02-27 10 +#> 5: 2020-02-28 42 #> --- -#> 123: 2020-06-26 273 -#> 124: 2020-06-27 201 -#> 125: 2020-06-28 232 -#> 126: 2020-06-29 263 -#> 127: 2020-06-30 259 +#> 124: 2020-06-26 190 +#> 125: 2020-06-27 187 +#> 126: 2020-06-28 230 +#> 127: 2020-06-29 279 +#> 128: 2020-06-30 264 # }
Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
out
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.14) and logSD 1.2 (SD 0.078)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.5 (SD 0.11) and logSD 1.1 (SD 0.079)
#>
# }
diff --git a/dev/reference/create_stan_args.html b/dev/reference/create_stan_args.html
index ed02fa718..89e76e391 100644
--- a/dev/reference/create_stan_args.html
+++ b/dev/reference/create_stan_args.html
@@ -964,7 +964,7 @@ Examples#> [1] FALSE
#>
#> $seed
-#> [1] 44752644
+#> [1] 24591502
#>
#> $future
#> [1] FALSE
@@ -1827,7 +1827,7 @@ Examples#> [1] FALSE
#>
#> $seed
-#> [1] 51002639
+#> [1] 521764
#>
#> $future
#> [1] FALSE
diff --git a/dev/reference/dist_fit.html b/dev/reference/dist_fit.html
index e24219c5e..944c2aa22 100644
--- a/dev/reference/dist_fit.html
+++ b/dev/reference/dist_fit.html
@@ -135,15 +135,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
-#> Chain 1: Rejecting initial value:
-#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1: Stan can't start sampling from this initial value.
-#> Chain 1: Rejecting initial value:
-#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
-#> Chain 1: Gradient evaluation took 6.7e-05 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.67 seconds.
+#> Chain 1: Gradient evaluation took 6.8e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -190,15 +184,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.231 seconds (Warm-up)
-#> Chain 1: 0.264 seconds (Sampling)
-#> Chain 1: 0.495 seconds (Total)
+#> Chain 1: Elapsed Time: 0.229 seconds (Warm-up)
+#> Chain 1: 0.202 seconds (Sampling)
+#> Chain 1: 0.431 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 6.3e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
+#> Chain 2: Gradient evaluation took 6.6e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -245,19 +239,19 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.228 seconds (Warm-up)
-#> Chain 2: 0.249 seconds (Sampling)
-#> Chain 2: 0.477 seconds (Total)
+#> Chain 2: Elapsed Time: 0.246 seconds (Warm-up)
+#> Chain 2: 0.278 seconds (Sampling)
+#> Chain 2: 0.524 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> lambda[1] 2.56 0.02 0.40 1.88 2.27 2.53 2.82 3.41 694 1.01
-#> lp__ -15.86 0.03 0.67 -17.73 -16.04 -15.61 -15.43 -15.38 687 1.00
+#> lambda[1] 2.57 0.01 0.41 1.85 2.27 2.54 2.84 3.45 781 1
+#> lp__ -14.92 0.03 0.70 -16.95 -15.06 -14.66 -14.48 -14.42 781 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:20 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:34 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -271,8 +265,8 @@ Examples#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.00029 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.9 seconds.
+#> Chain 1: Gradient evaluation took 0.000288 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.88 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -319,15 +313,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 2.473 seconds (Warm-up)
-#> Chain 1: 2.033 seconds (Sampling)
-#> Chain 1: 4.506 seconds (Total)
+#> Chain 1: Elapsed Time: 2.124 seconds (Warm-up)
+#> Chain 1: 2.868 seconds (Sampling)
+#> Chain 1: 4.992 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 0.000276 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.76 seconds.
+#> Chain 2: Gradient evaluation took 0.000246 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -374,28 +368,22 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 2.206 seconds (Warm-up)
-#> Chain 2: 2.817 seconds (Sampling)
-#> Chain 2: 5.023 seconds (Total)
+#> Chain 2: Elapsed Time: 2.147 seconds (Warm-up)
+#> Chain 2: 1.719 seconds (Sampling)
+#> Chain 2: 3.866 seconds (Total)
#> Chain 2:
-#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#bulk-ess
-#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> alpha_raw[1] 0.85 0.04 0.56 0.04 0.42 0.77 1.21 2.10 249 1
-#> beta_raw[1] 0.94 0.03 0.54 0.09 0.52 0.88 1.29 2.14 417 1
-#> alpha[1] 6.98 0.04 0.56 6.16 6.54 6.89 7.33 8.22 249 1
-#> beta[1] 6.99 0.03 0.54 6.14 6.57 6.93 7.34 8.19 417 1
-#> lp__ -11.63 0.11 1.44 -15.35 -12.27 -11.19 -10.60 -10.19 177 1
+#> alpha_raw[1] 0.84 0.03 0.55 0.05 0.41 0.76 1.20 2.10 268 1.01
+#> beta_raw[1] 1.07 0.04 0.58 0.11 0.66 1.03 1.45 2.26 262 1.01
+#> alpha[1] 6.17 0.03 0.55 5.38 5.74 6.09 6.53 7.43 268 1.01
+#> beta[1] 6.50 0.04 0.58 5.54 6.09 6.46 6.88 7.69 262 1.01
+#> lp__ -11.74 0.11 1.43 -15.48 -12.41 -11.27 -10.67 -10.24 173 1.02
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:30 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:43 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -407,6 +395,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
@@ -456,15 +447,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
Binary files a/dev/reference/estimate_secondary-1.png and b/dev/reference/estimate_secondary-1.png differ
diff --git a/dev/reference/estimate_secondary-2.png b/dev/reference/estimate_secondary-2.png
index c77fb0906..f04d2ffc8 100644
Binary files a/dev/reference/estimate_secondary-2.png and b/dev/reference/estimate_secondary-2.png differ
diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
index 50aa45b6e..deccbbb0f 100644
Binary files a/dev/reference/estimate_secondary-3.png and b/dev/reference/estimate_secondary-3.png differ
diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
Binary files a/dev/reference/estimate_secondary-4.png and b/dev/reference/estimate_secondary-4.png differ
diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
Binary files a/dev/reference/figures/unnamed-chunk-15-1.png and b/dev/reference/figures/unnamed-chunk-15-1.png differ
diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
Binary files a/dev/reference/figures/unnamed-chunk-19-1.png and b/dev/reference/figures/unnamed-chunk-19-1.png differ
diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
Binary files a/dev/reference/plot_estimates-1.png and b/dev/reference/plot_estimates-1.png differ
diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
index 6b747148e..66cee9970 100644
Binary files a/dev/reference/plot_estimates-2.png and b/dev/reference/plot_estimates-2.png differ
diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
Binary files a/dev/reference/plot_estimates-3.png and b/dev/reference/plot_estimates-3.png differ
diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
Binary files a/dev/reference/plot_estimates-4.png and b/dev/reference/plot_estimates-4.png differ
diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples#> [1] FALSE
#>
#> $seed
-#> [1] 51002639
+#> [1] 521764
#>
#> $future
#> [1] FALSE
diff --git a/dev/reference/dist_fit.html b/dev/reference/dist_fit.html
index e24219c5e..944c2aa22 100644
--- a/dev/reference/dist_fit.html
+++ b/dev/reference/dist_fit.html
@@ -135,15 +135,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
-#> Chain 1: Rejecting initial value:
-#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1: Stan can't start sampling from this initial value.
-#> Chain 1: Rejecting initial value:
-#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
-#> Chain 1: Gradient evaluation took 6.7e-05 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.67 seconds.
+#> Chain 1: Gradient evaluation took 6.8e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -190,15 +184,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.231 seconds (Warm-up)
-#> Chain 1: 0.264 seconds (Sampling)
-#> Chain 1: 0.495 seconds (Total)
+#> Chain 1: Elapsed Time: 0.229 seconds (Warm-up)
+#> Chain 1: 0.202 seconds (Sampling)
+#> Chain 1: 0.431 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 6.3e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
+#> Chain 2: Gradient evaluation took 6.6e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -245,19 +239,19 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.228 seconds (Warm-up)
-#> Chain 2: 0.249 seconds (Sampling)
-#> Chain 2: 0.477 seconds (Total)
+#> Chain 2: Elapsed Time: 0.246 seconds (Warm-up)
+#> Chain 2: 0.278 seconds (Sampling)
+#> Chain 2: 0.524 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> lambda[1] 2.56 0.02 0.40 1.88 2.27 2.53 2.82 3.41 694 1.01
-#> lp__ -15.86 0.03 0.67 -17.73 -16.04 -15.61 -15.43 -15.38 687 1.00
+#> lambda[1] 2.57 0.01 0.41 1.85 2.27 2.54 2.84 3.45 781 1
+#> lp__ -14.92 0.03 0.70 -16.95 -15.06 -14.66 -14.48 -14.42 781 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:20 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:34 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -271,8 +265,8 @@ Examples#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.00029 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.9 seconds.
+#> Chain 1: Gradient evaluation took 0.000288 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.88 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -319,15 +313,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 2.473 seconds (Warm-up)
-#> Chain 1: 2.033 seconds (Sampling)
-#> Chain 1: 4.506 seconds (Total)
+#> Chain 1: Elapsed Time: 2.124 seconds (Warm-up)
+#> Chain 1: 2.868 seconds (Sampling)
+#> Chain 1: 4.992 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 0.000276 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.76 seconds.
+#> Chain 2: Gradient evaluation took 0.000246 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -374,28 +368,22 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 2.206 seconds (Warm-up)
-#> Chain 2: 2.817 seconds (Sampling)
-#> Chain 2: 5.023 seconds (Total)
+#> Chain 2: Elapsed Time: 2.147 seconds (Warm-up)
+#> Chain 2: 1.719 seconds (Sampling)
+#> Chain 2: 3.866 seconds (Total)
#> Chain 2:
-#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#bulk-ess
-#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> alpha_raw[1] 0.85 0.04 0.56 0.04 0.42 0.77 1.21 2.10 249 1
-#> beta_raw[1] 0.94 0.03 0.54 0.09 0.52 0.88 1.29 2.14 417 1
-#> alpha[1] 6.98 0.04 0.56 6.16 6.54 6.89 7.33 8.22 249 1
-#> beta[1] 6.99 0.03 0.54 6.14 6.57 6.93 7.34 8.19 417 1
-#> lp__ -11.63 0.11 1.44 -15.35 -12.27 -11.19 -10.60 -10.19 177 1
+#> alpha_raw[1] 0.84 0.03 0.55 0.05 0.41 0.76 1.20 2.10 268 1.01
+#> beta_raw[1] 1.07 0.04 0.58 0.11 0.66 1.03 1.45 2.26 262 1.01
+#> alpha[1] 6.17 0.03 0.55 5.38 5.74 6.09 6.53 7.43 268 1.01
+#> beta[1] 6.50 0.04 0.58 5.54 6.09 6.46 6.88 7.69 262 1.01
+#> lp__ -11.74 0.11 1.43 -15.48 -12.41 -11.27 -10.67 -10.24 173 1.02
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:30 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:43 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -407,6 +395,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
@@ -456,15 +447,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
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diff --git a/dev/reference/estimate_secondary-2.png b/dev/reference/estimate_secondary-2.png
index c77fb0906..f04d2ffc8 100644
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diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
index 50aa45b6e..deccbbb0f 100644
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diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
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diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
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diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
Binary files a/dev/reference/plot_estimates-1.png and b/dev/reference/plot_estimates-1.png differ
diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
index 6b747148e..66cee9970 100644
Binary files a/dev/reference/plot_estimates-2.png and b/dev/reference/plot_estimates-2.png differ
diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
Binary files a/dev/reference/plot_estimates-3.png and b/dev/reference/plot_estimates-3.png differ
diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.231 seconds (Warm-up)
-#> Chain 1: 0.264 seconds (Sampling)
-#> Chain 1: 0.495 seconds (Total)
+#> Chain 1: Elapsed Time: 0.229 seconds (Warm-up)
+#> Chain 1: 0.202 seconds (Sampling)
+#> Chain 1: 0.431 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 6.3e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
+#> Chain 2: Gradient evaluation took 6.6e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -245,19 +239,19 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.228 seconds (Warm-up)
-#> Chain 2: 0.249 seconds (Sampling)
-#> Chain 2: 0.477 seconds (Total)
+#> Chain 2: Elapsed Time: 0.246 seconds (Warm-up)
+#> Chain 2: 0.278 seconds (Sampling)
+#> Chain 2: 0.524 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> lambda[1] 2.56 0.02 0.40 1.88 2.27 2.53 2.82 3.41 694 1.01
-#> lp__ -15.86 0.03 0.67 -17.73 -16.04 -15.61 -15.43 -15.38 687 1.00
+#> lambda[1] 2.57 0.01 0.41 1.85 2.27 2.54 2.84 3.45 781 1
+#> lp__ -14.92 0.03 0.70 -16.95 -15.06 -14.66 -14.48 -14.42 781 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:20 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:34 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -271,8 +265,8 @@ Examples#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.00029 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.9 seconds.
+#> Chain 1: Gradient evaluation took 0.000288 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.88 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -319,15 +313,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 2.473 seconds (Warm-up)
-#> Chain 1: 2.033 seconds (Sampling)
-#> Chain 1: 4.506 seconds (Total)
+#> Chain 1: Elapsed Time: 2.124 seconds (Warm-up)
+#> Chain 1: 2.868 seconds (Sampling)
+#> Chain 1: 4.992 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 0.000276 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.76 seconds.
+#> Chain 2: Gradient evaluation took 0.000246 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -374,28 +368,22 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 2.206 seconds (Warm-up)
-#> Chain 2: 2.817 seconds (Sampling)
-#> Chain 2: 5.023 seconds (Total)
+#> Chain 2: Elapsed Time: 2.147 seconds (Warm-up)
+#> Chain 2: 1.719 seconds (Sampling)
+#> Chain 2: 3.866 seconds (Total)
#> Chain 2:
-#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#bulk-ess
-#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> alpha_raw[1] 0.85 0.04 0.56 0.04 0.42 0.77 1.21 2.10 249 1
-#> beta_raw[1] 0.94 0.03 0.54 0.09 0.52 0.88 1.29 2.14 417 1
-#> alpha[1] 6.98 0.04 0.56 6.16 6.54 6.89 7.33 8.22 249 1
-#> beta[1] 6.99 0.03 0.54 6.14 6.57 6.93 7.34 8.19 417 1
-#> lp__ -11.63 0.11 1.44 -15.35 -12.27 -11.19 -10.60 -10.19 177 1
+#> alpha_raw[1] 0.84 0.03 0.55 0.05 0.41 0.76 1.20 2.10 268 1.01
+#> beta_raw[1] 1.07 0.04 0.58 0.11 0.66 1.03 1.45 2.26 262 1.01
+#> alpha[1] 6.17 0.03 0.55 5.38 5.74 6.09 6.53 7.43 268 1.01
+#> beta[1] 6.50 0.04 0.58 5.54 6.09 6.46 6.88 7.69 262 1.01
+#> lp__ -11.74 0.11 1.43 -15.48 -12.41 -11.27 -10.67 -10.24 173 1.02
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:30 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:43 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -407,6 +395,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
@@ -456,15 +447,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
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index a29d2677f..d74092b7b 100644
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diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
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diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
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index 6b747148e..66cee9970 100644
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diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
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diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.00029 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.9 seconds.
+#> Chain 1: Gradient evaluation took 0.000288 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.88 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -319,15 +313,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 2.473 seconds (Warm-up)
-#> Chain 1: 2.033 seconds (Sampling)
-#> Chain 1: 4.506 seconds (Total)
+#> Chain 1: Elapsed Time: 2.124 seconds (Warm-up)
+#> Chain 1: 2.868 seconds (Sampling)
+#> Chain 1: 4.992 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 0.000276 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.76 seconds.
+#> Chain 2: Gradient evaluation took 0.000246 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -374,28 +368,22 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 2.206 seconds (Warm-up)
-#> Chain 2: 2.817 seconds (Sampling)
-#> Chain 2: 5.023 seconds (Total)
+#> Chain 2: Elapsed Time: 2.147 seconds (Warm-up)
+#> Chain 2: 1.719 seconds (Sampling)
+#> Chain 2: 3.866 seconds (Total)
#> Chain 2:
-#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#bulk-ess
-#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> alpha_raw[1] 0.85 0.04 0.56 0.04 0.42 0.77 1.21 2.10 249 1
-#> beta_raw[1] 0.94 0.03 0.54 0.09 0.52 0.88 1.29 2.14 417 1
-#> alpha[1] 6.98 0.04 0.56 6.16 6.54 6.89 7.33 8.22 249 1
-#> beta[1] 6.99 0.03 0.54 6.14 6.57 6.93 7.34 8.19 417 1
-#> lp__ -11.63 0.11 1.44 -15.35 -12.27 -11.19 -10.60 -10.19 177 1
+#> alpha_raw[1] 0.84 0.03 0.55 0.05 0.41 0.76 1.20 2.10 268 1.01
+#> beta_raw[1] 1.07 0.04 0.58 0.11 0.66 1.03 1.45 2.26 262 1.01
+#> alpha[1] 6.17 0.03 0.55 5.38 5.74 6.09 6.53 7.43 268 1.01
+#> beta[1] 6.50 0.04 0.58 5.54 6.09 6.46 6.88 7.69 262 1.01
+#> lp__ -11.74 0.11 1.43 -15.48 -12.41 -11.27 -10.67 -10.24 173 1.02
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:30 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:43 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -407,6 +395,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
@@ -456,15 +447,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
Binary files a/dev/reference/estimate_secondary-1.png and b/dev/reference/estimate_secondary-1.png differ
diff --git a/dev/reference/estimate_secondary-2.png b/dev/reference/estimate_secondary-2.png
index c77fb0906..f04d2ffc8 100644
Binary files a/dev/reference/estimate_secondary-2.png and b/dev/reference/estimate_secondary-2.png differ
diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
index 50aa45b6e..deccbbb0f 100644
Binary files a/dev/reference/estimate_secondary-3.png and b/dev/reference/estimate_secondary-3.png differ
diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
Binary files a/dev/reference/estimate_secondary-4.png and b/dev/reference/estimate_secondary-4.png differ
diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
Binary files a/dev/reference/figures/unnamed-chunk-15-1.png and b/dev/reference/figures/unnamed-chunk-15-1.png differ
diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
Binary files a/dev/reference/figures/unnamed-chunk-19-1.png and b/dev/reference/figures/unnamed-chunk-19-1.png differ
diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
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diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
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diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
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diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 2.206 seconds (Warm-up)
-#> Chain 2: 2.817 seconds (Sampling)
-#> Chain 2: 5.023 seconds (Total)
+#> Chain 2: Elapsed Time: 2.147 seconds (Warm-up)
+#> Chain 2: 1.719 seconds (Sampling)
+#> Chain 2: 3.866 seconds (Total)
#> Chain 2:
-#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#bulk-ess
-#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> alpha_raw[1] 0.85 0.04 0.56 0.04 0.42 0.77 1.21 2.10 249 1
-#> beta_raw[1] 0.94 0.03 0.54 0.09 0.52 0.88 1.29 2.14 417 1
-#> alpha[1] 6.98 0.04 0.56 6.16 6.54 6.89 7.33 8.22 249 1
-#> beta[1] 6.99 0.03 0.54 6.14 6.57 6.93 7.34 8.19 417 1
-#> lp__ -11.63 0.11 1.44 -15.35 -12.27 -11.19 -10.60 -10.19 177 1
+#> alpha_raw[1] 0.84 0.03 0.55 0.05 0.41 0.76 1.20 2.10 268 1.01
+#> beta_raw[1] 1.07 0.04 0.58 0.11 0.66 1.03 1.45 2.26 262 1.01
+#> alpha[1] 6.17 0.03 0.55 5.38 5.74 6.09 6.53 7.43 268 1.01
+#> beta[1] 6.50 0.04 0.58 5.54 6.09 6.46 6.88 7.69 262 1.01
+#> lp__ -11.74 0.11 1.43 -15.48 -12.41 -11.27 -10.67 -10.24 173 1.02
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:30 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:43 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -407,6 +395,9 @@ Examples)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
@@ -456,15 +447,15 @@ Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
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diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
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diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
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diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
Binary files a/dev/reference/plot_estimates-1.png and b/dev/reference/plot_estimates-1.png differ
diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
index 6b747148e..66cee9970 100644
Binary files a/dev/reference/plot_estimates-2.png and b/dev/reference/plot_estimates-2.png differ
diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
Binary files a/dev/reference/plot_estimates-3.png and b/dev/reference/plot_estimates-3.png differ
diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples#> Chain 1: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.48 seconds (Warm-up)
-#> Chain 1: 0.417 seconds (Sampling)
-#> Chain 1: 0.897 seconds (Total)
+#> Chain 1: Elapsed Time: 0.5 seconds (Warm-up)
+#> Chain 1: 0.454 seconds (Sampling)
+#> Chain 1: 0.954 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
-#> Chain 2: Gradient evaluation took 9e-05 seconds
-#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
+#> Chain 2: Gradient evaluation took 9.1e-05 seconds
+#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
@@ -511,20 +502,20 @@ Examples#> Chain 2: Iteration: 1950 / 2000 [ 97%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
-#> Chain 2: Elapsed Time: 0.481 seconds (Warm-up)
-#> Chain 2: 0.503 seconds (Sampling)
-#> Chain 2: 0.984 seconds (Total)
+#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
+#> Chain 2: 0.465 seconds (Sampling)
+#> Chain 2: 0.948 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
-#> mu[1] 1.60 0.00 0.02 1.56 1.59 1.60 1.62 1.64 1583 1
-#> sigma[1] 0.17 0.00 0.02 0.14 0.16 0.17 0.18 0.21 1400 1
-#> lp__ -75.35 0.04 1.03 -78.17 -75.73 -75.01 -74.64 -74.39 777 1
+#> mu[1] 1.60 0.00 0.02 1.56 1.58 1.60 1.61 1.64 1227 1
+#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 1218 1
+#> lp__ -68.35 0.04 1.05 -71.22 -68.75 -68.01 -67.61 -67.34 843 1
#>
-#> Samples were drawn using NUTS(diag_e) at Wed Sep 27 17:03:32 2023.
+#> Samples were drawn using NUTS(diag_e) at Tue Oct 3 09:54:45 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
diff --git a/dev/reference/dist_skel.html b/dev/reference/dist_skel.html
index 37d0fc607..78d67797b 100644
--- a/dev/reference/dist_skel.html
+++ b/dev/reference/dist_skel.html
@@ -147,7 +147,7 @@ Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
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index bd17ce2a9..be97c1b84 100644
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
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diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
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diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples## Exponential model
# sample
dist_skel(10, model = "exp", params = list(rate = 1))
-#> [1] 0 0 0 0 0 3 1 0 0 0
+#> [1] 0 1 1 0 1 1 1 1 1 0
# cumulative prob density
dist_skel(1:10, model = "exp", dist = TRUE, params = list(rate = 1))
@@ -165,7 +165,7 @@ Examples## Gamma model
# sample
dist_skel(10, model = "gamma", params = list(shape = 1, scale = 2))
-#> [1] 0 0 0 0 0 1 0 0 0 0
+#> [1] 0 1 0 0 0 0 0 0 1 0
# cumulative prob density
dist_skel(0:10,
@@ -187,7 +187,7 @@ Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
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diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
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diff --git a/dev/reference/estimate_secondary-2.png b/dev/reference/estimate_secondary-2.png
index c77fb0906..f04d2ffc8 100644
Binary files a/dev/reference/estimate_secondary-2.png and b/dev/reference/estimate_secondary-2.png differ
diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
index 50aa45b6e..deccbbb0f 100644
Binary files a/dev/reference/estimate_secondary-3.png and b/dev/reference/estimate_secondary-3.png differ
diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
Binary files a/dev/reference/estimate_secondary-4.png and b/dev/reference/estimate_secondary-4.png differ
diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
Binary files a/dev/reference/figures/unnamed-chunk-15-1.png and b/dev/reference/figures/unnamed-chunk-15-1.png differ
diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
Binary files a/dev/reference/figures/unnamed-chunk-19-1.png and b/dev/reference/figures/unnamed-chunk-19-1.png differ
diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
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diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
index 6b747148e..66cee9970 100644
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diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
Binary files a/dev/reference/plot_estimates-3.png and b/dev/reference/plot_estimates-3.png differ
diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
Binary files a/dev/reference/plot_estimates-4.png and b/dev/reference/plot_estimates-4.png differ
diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples## Log normal model
# sample
dist_skel(10, model = "lognormal", params = list(mean = log(5), sd = log(2)))
-#> [1] 3 2 1 5 2 5 5 9 1 2
+#> [1] 3 2 7 3 2 15 4 4 11 8
# cumulative prob density
dist_skel(0:10,
diff --git a/dev/reference/epinow-1.png b/dev/reference/epinow-1.png
index 1af19789e..56d30f8f0 100644
Binary files a/dev/reference/epinow-1.png and b/dev/reference/epinow-1.png differ
diff --git a/dev/reference/epinow.html b/dev/reference/epinow.html
index c9ae129c8..834252d62 100644
--- a/dev/reference/epinow.html
+++ b/dev/reference/epinow.html
@@ -287,25 +287,22 @@ Examples delays = delay_opts(incubation_period + reporting_delay)
)
#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-04-01.log
+#> Writing EpiNow2 logs to the console and: /tmp/RtmpwVkgrx/regional-epinow/2020-04-01.log
#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpYkZBel/epinow/2020-04-01.log
-#> WARN [2023-09-27 17:05:13] epinow: There were 4 divergent transitions after warmup. See
+#> Writing EpiNow2.epinow logs to the console and: /tmp/RtmpwVkgrx/epinow/2020-04-01.log
+#> WARN [2023-10-03 09:56:19] epinow: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. -
-#> WARN [2023-09-27 17:05:13] epinow: Examine the pairs() plot to diagnose sampling problems
+#> WARN [2023-10-03 09:56:19] epinow: Examine the pairs() plot to diagnose sampling problems
#> -
-#> WARN [2023-09-27 17:05:15] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#tail-ess -
# summary of the latest estimates
summary(out)
-#> measure estimate
-#> 1: New confirmed cases by infection date 3339 (1383 -- 7683)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.78 (0.5 -- 1.1)
-#> 4: Rate of growth -0.05 (-0.13 -- 0.03)
-#> 5: Doubling/halving time (days) -14 (23 -- -5.2)
+#> measure estimate
+#> 1: New confirmed cases by infection date 3267 (1418 -- 7613)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.77 (0.51 -- 1.1)
+#> 4: Rate of growth -0.053 (-0.13 -- 0.024)
+#> 5: Doubling/halving time (days) -13 (29 -- -5.3)
# plot estimates
plot(out)
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
Binary files a/dev/reference/estimate_secondary-1.png and b/dev/reference/estimate_secondary-1.png differ
diff --git a/dev/reference/estimate_secondary-2.png b/dev/reference/estimate_secondary-2.png
index c77fb0906..f04d2ffc8 100644
Binary files a/dev/reference/estimate_secondary-2.png and b/dev/reference/estimate_secondary-2.png differ
diff --git a/dev/reference/estimate_secondary-3.png b/dev/reference/estimate_secondary-3.png
index 50aa45b6e..deccbbb0f 100644
Binary files a/dev/reference/estimate_secondary-3.png and b/dev/reference/estimate_secondary-3.png differ
diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
Binary files a/dev/reference/estimate_secondary-4.png and b/dev/reference/estimate_secondary-4.png differ
diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
Binary files a/dev/reference/figures/unnamed-chunk-15-1.png and b/dev/reference/figures/unnamed-chunk-15-1.png differ
diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
Binary files a/dev/reference/figures/unnamed-chunk-19-1.png and b/dev/reference/figures/unnamed-chunk-19-1.png differ
diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
Binary files a/dev/reference/plot_estimates-1.png and b/dev/reference/plot_estimates-1.png differ
diff --git a/dev/reference/plot_estimates-2.png b/dev/reference/plot_estimates-2.png
index 6b747148e..66cee9970 100644
Binary files a/dev/reference/plot_estimates-2.png and b/dev/reference/plot_estimates-2.png differ
diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
Binary files a/dev/reference/plot_estimates-3.png and b/dev/reference/plot_estimates-3.png differ
diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
Binary files a/dev/reference/plot_estimates-4.png and b/dev/reference/plot_estimates-4.png differ
diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
@@ -313,151 +310,151 @@ Examples# summary of R estimates
summary(out, type = "parameters", params = "R")
#> date variable strat type median
-#> 1: 2020-02-22 R <NA> estimate 2.0630556
-#> 2: 2020-02-23 R <NA> estimate 2.0561503
-#> 3: 2020-02-24 R <NA> estimate 2.0449901
-#> 4: 2020-02-25 R <NA> estimate 2.0278170
-#> 5: 2020-02-26 R <NA> estimate 2.0082019
-#> 6: 2020-02-27 R <NA> estimate 1.9850389
-#> 7: 2020-02-28 R <NA> estimate 1.9604793
-#> 8: 2020-02-29 R <NA> estimate 1.9327947
-#> 9: 2020-03-01 R <NA> estimate 1.9018071
-#> 10: 2020-03-02 R <NA> estimate 1.8653780
-#> 11: 2020-03-03 R <NA> estimate 1.8274850
-#> 12: 2020-03-04 R <NA> estimate 1.7873618
-#> 13: 2020-03-05 R <NA> estimate 1.7461182
-#> 14: 2020-03-06 R <NA> estimate 1.7024330
-#> 15: 2020-03-07 R <NA> estimate 1.6578493
-#> 16: 2020-03-08 R <NA> estimate 1.6126959
-#> 17: 2020-03-09 R <NA> estimate 1.5671570
-#> 18: 2020-03-10 R <NA> estimate 1.5211080
-#> 19: 2020-03-11 R <NA> estimate 1.4722861
-#> 20: 2020-03-12 R <NA> estimate 1.4243094
-#> 21: 2020-03-13 R <NA> estimate 1.3773781
-#> 22: 2020-03-14 R <NA> estimate 1.3308711
-#> 23: 2020-03-15 R <NA> estimate 1.2863432
-#> 24: 2020-03-16 R <NA> estimate 1.2426223
-#> 25: 2020-03-17 R <NA> estimate 1.1993434
-#> 26: 2020-03-18 R <NA> estimate 1.1565843
-#> 27: 2020-03-19 R <NA> estimate 1.1160840
-#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766523
-#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0395418
-#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0055248
-#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9738876
-#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9449986
-#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9175016
-#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8922611
-#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8694524
-#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8478990
-#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8279840
-#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8108192
-#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7963001
-#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7827052
-#> 41: 2020-04-02 R <NA> forecast 0.7827052
-#> 42: 2020-04-03 R <NA> forecast 0.7827052
-#> 43: 2020-04-04 R <NA> forecast 0.7827052
-#> 44: 2020-04-05 R <NA> forecast 0.7827052
-#> 45: 2020-04-06 R <NA> forecast 0.7827052
-#> 46: 2020-04-07 R <NA> forecast 0.7827052
-#> 47: 2020-04-08 R <NA> forecast 0.7827052
+#> 1: 2020-02-22 R <NA> estimate 2.0665663
+#> 2: 2020-02-23 R <NA> estimate 2.0572481
+#> 3: 2020-02-24 R <NA> estimate 2.0453332
+#> 4: 2020-02-25 R <NA> estimate 2.0291015
+#> 5: 2020-02-26 R <NA> estimate 2.0099345
+#> 6: 2020-02-27 R <NA> estimate 1.9861627
+#> 7: 2020-02-28 R <NA> estimate 1.9593040
+#> 8: 2020-02-29 R <NA> estimate 1.9297156
+#> 9: 2020-03-01 R <NA> estimate 1.8968719
+#> 10: 2020-03-02 R <NA> estimate 1.8620086
+#> 11: 2020-03-03 R <NA> estimate 1.8245663
+#> 12: 2020-03-04 R <NA> estimate 1.7856377
+#> 13: 2020-03-05 R <NA> estimate 1.7450311
+#> 14: 2020-03-06 R <NA> estimate 1.7016611
+#> 15: 2020-03-07 R <NA> estimate 1.6563836
+#> 16: 2020-03-08 R <NA> estimate 1.6122470
+#> 17: 2020-03-09 R <NA> estimate 1.5660727
+#> 18: 2020-03-10 R <NA> estimate 1.5187449
+#> 19: 2020-03-11 R <NA> estimate 1.4725610
+#> 20: 2020-03-12 R <NA> estimate 1.4268102
+#> 21: 2020-03-13 R <NA> estimate 1.3806179
+#> 22: 2020-03-14 R <NA> estimate 1.3345526
+#> 23: 2020-03-15 R <NA> estimate 1.2893035
+#> 24: 2020-03-16 R <NA> estimate 1.2437824
+#> 25: 2020-03-17 R <NA> estimate 1.2002469
+#> 26: 2020-03-18 R <NA> estimate 1.1573501
+#> 27: 2020-03-19 R <NA> estimate 1.1161739
+#> 28: 2020-03-20 R <NA> estimate based on partial data 1.0766134
+#> 29: 2020-03-21 R <NA> estimate based on partial data 1.0397432
+#> 30: 2020-03-22 R <NA> estimate based on partial data 1.0046027
+#> 31: 2020-03-23 R <NA> estimate based on partial data 0.9720808
+#> 32: 2020-03-24 R <NA> estimate based on partial data 0.9421734
+#> 33: 2020-03-25 R <NA> estimate based on partial data 0.9146241
+#> 34: 2020-03-26 R <NA> estimate based on partial data 0.8882882
+#> 35: 2020-03-27 R <NA> estimate based on partial data 0.8637211
+#> 36: 2020-03-28 R <NA> estimate based on partial data 0.8432459
+#> 37: 2020-03-29 R <NA> estimate based on partial data 0.8233689
+#> 38: 2020-03-30 R <NA> estimate based on partial data 0.8040429
+#> 39: 2020-03-31 R <NA> estimate based on partial data 0.7875137
+#> 40: 2020-04-01 R <NA> estimate based on partial data 0.7737082
+#> 41: 2020-04-02 R <NA> forecast 0.7737082
+#> 42: 2020-04-03 R <NA> forecast 0.7737082
+#> 43: 2020-04-04 R <NA> forecast 0.7737082
+#> 44: 2020-04-05 R <NA> forecast 0.7737082
+#> 45: 2020-04-06 R <NA> forecast 0.7737082
+#> 46: 2020-04-07 R <NA> forecast 0.7737082
+#> 47: 2020-04-08 R <NA> forecast 0.7737082
#> date variable strat type median
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
-#> 1: 2.0654700 0.09140100 1.9155783 2.0020193 2.0396755 2.0871779 2.1268969
-#> 2: 2.0563523 0.08022335 1.9215210 2.0012674 2.0358472 2.0755238 2.1108048
-#> 3: 2.0444288 0.07473759 1.9201075 1.9933436 2.0267166 2.0618980 2.0933144
-#> 4: 2.0293066 0.07313843 1.9094931 1.9812958 2.0112020 2.0455288 2.0750877
-#> 5: 2.0107181 0.07317587 1.8945218 1.9637873 1.9914265 2.0253861 2.0562972
-#> 6: 1.9885398 0.07317225 1.8735226 1.9414698 1.9694782 2.0034635 2.0319879
-#> 7: 1.9627930 0.07233090 1.8496825 1.9150012 1.9439377 1.9777927 2.0034959
-#> 8: 1.9336289 0.07056372 1.8209677 1.8875588 1.9165284 1.9477680 1.9748104
-#> 9: 1.9012993 0.06818768 1.7909055 1.8554740 1.8848149 1.9159897 1.9408901
-#> 10: 1.8661220 0.06563729 1.7591815 1.8222831 1.8498433 1.8811835 1.9060901
-#> 11: 1.8284459 0.06325700 1.7275961 1.7849515 1.8125328 1.8434810 1.8682203
-#> 12: 1.7886225 0.06121413 1.6906501 1.7469806 1.7725290 1.8034032 1.8289286
-#> 13: 1.7469866 0.05952738 1.6537891 1.7063251 1.7304593 1.7607230 1.7860075
-#> 14: 1.7038447 0.05815048 1.6127375 1.6656774 1.6871892 1.7168131 1.7420630
-#> 15: 1.6594723 0.05703632 1.5680799 1.6232867 1.6438681 1.6734787 1.6967755
-#> 16: 1.6141157 0.05614712 1.5208269 1.5786742 1.5999863 1.6271643 1.6507582
-#> 17: 1.5679991 0.05542859 1.4794937 1.5339373 1.5545437 1.5809058 1.6025780
-#> 18: 1.5213347 0.05479077 1.4364502 1.4858618 1.5075202 1.5336994 1.5544011
-#> 19: 1.4743341 0.05412324 1.3893539 1.4407256 1.4600211 1.4849195 1.5068804
-#> 20: 1.4272184 0.05333719 1.3459530 1.3943973 1.4119901 1.4375661 1.4596837
-#> 21: 1.3802253 0.05240441 1.2981623 1.3476243 1.3663559 1.3903809 1.4128795
-#> 22: 1.3336098 0.05137065 1.2547889 1.3002948 1.3204158 1.3438768 1.3654469
-#> 23: 1.2876408 0.05034666 1.2079334 1.2547660 1.2755033 1.2979544 1.3178008
-#> 24: 1.2425929 0.04949943 1.1610801 1.2112646 1.2303971 1.2538519 1.2717270
-#> 25: 1.1987378 0.04906204 1.1192653 1.1683065 1.1869327 1.2106080 1.2276416
-#> 26: 1.1563358 0.04935304 1.0768096 1.1252999 1.1441786 1.1678975 1.1873108
-#> 27: 1.1156284 0.05076837 1.0337800 1.0821770 1.1045952 1.1274072 1.1483944
-#> 28: 1.0768329 0.05371196 0.9891035 1.0414082 1.0637567 1.0901997 1.1110108
-#> 29: 1.0401383 0.05848225 0.9437150 1.0008865 1.0258714 1.0541659 1.0781587
-#> 30: 1.0057028 0.06518837 0.8988046 0.9613202 0.9898081 1.0225159 1.0463345
-#> 31: 0.9736528 0.07375161 0.8530719 0.9246384 0.9556944 0.9920036 1.0191419
-#> 32: 0.9440836 0.08397308 0.8083194 0.8901982 0.9220925 0.9636271 0.9959606
-#> 33: 0.9170620 0.09561328 0.7658533 0.8551038 0.8915989 0.9383704 0.9772777
-#> 34: 0.8926284 0.10845147 0.7191085 0.8221853 0.8639950 0.9141947 0.9598976
-#> 35: 0.8707991 0.12231924 0.6752212 0.7914982 0.8393913 0.8950385 0.9462878
-#> 36: 0.8515667 0.13711236 0.6368603 0.7631138 0.8172923 0.8799523 0.9334947
-#> 37: 0.8348991 0.15278700 0.5977289 0.7355687 0.7954542 0.8634365 0.9228019
-#> 38: 0.8207374 0.16934660 0.5625805 0.7100129 0.7752600 0.8504781 0.9155790
-#> 39: 0.8089934 0.18682358 0.5307585 0.6881798 0.7569616 0.8375029 0.9105444
-#> 40: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 41: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 42: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 43: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 44: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 45: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 46: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
-#> 47: 0.7995472 0.20525588 0.4991515 0.6693275 0.7375850 0.8260378 0.9065211
+#> 1: 2.0675992 0.09321586 1.9200772 2.0020907 2.0418754 2.0913262 2.1279903
+#> 2: 2.0580873 0.08265899 1.9256702 2.0003472 2.0368055 2.0765106 2.1114986
+#> 3: 2.0457177 0.07776500 1.9232081 1.9917157 2.0255956 2.0642544 2.0937309
+#> 4: 2.0301204 0.07655432 1.9092095 1.9785126 2.0098879 2.0480723 2.0783659
+#> 5: 2.0110487 0.07671004 1.8943839 1.9594731 1.9906786 2.0268263 2.0580830
+#> 6: 1.9883960 0.07657572 1.8693509 1.9358609 1.9679766 2.0041800 2.0355304
+#> 7: 1.9621961 0.07538180 1.8439932 1.9112285 1.9420285 1.9773355 2.0076980
+#> 8: 1.9326079 0.07304591 1.8180598 1.8836862 1.9116599 1.9475577 1.9779922
+#> 9: 1.8998901 0.06990064 1.7915296 1.8543763 1.8799528 1.9146614 1.9434036
+#> 10: 1.8643683 0.06645585 1.7624156 1.8200211 1.8457151 1.8781120 1.9062110
+#> 11: 1.8264026 0.06320783 1.7283402 1.7838776 1.8091214 1.8398913 1.8665988
+#> 12: 1.7863597 0.06050377 1.6893963 1.7463969 1.7700015 1.8000902 1.8242854
+#> 13: 1.7445913 0.05848122 1.6505095 1.7047233 1.7296612 1.7583197 1.7816244
+#> 14: 1.7014209 0.05708947 1.6087531 1.6628035 1.6881031 1.7147731 1.7375611
+#> 15: 1.6571377 0.05616611 1.5656264 1.6196018 1.6433582 1.6713940 1.6926185
+#> 16: 1.6119963 0.05552094 1.5212405 1.5745969 1.5983431 1.6257454 1.6471978
+#> 17: 1.5662210 0.05499117 1.4781976 1.5303195 1.5522478 1.5798505 1.6017974
+#> 18: 1.5200143 0.05446108 1.4320667 1.4847695 1.5063324 1.5336986 1.5542528
+#> 19: 1.4735659 0.05385879 1.3852717 1.4380756 1.4601230 1.4868331 1.5076272
+#> 20: 1.4270628 0.05314735 1.3421077 1.3923514 1.4142647 1.4394029 1.4600824
+#> 21: 1.3806978 0.05232109 1.2967022 1.3475073 1.3683833 1.3926720 1.4130233
+#> 22: 1.3346746 0.05140993 1.2517303 1.3024069 1.3228197 1.3467060 1.3661292
+#> 23: 1.2892110 0.05049159 1.2078625 1.2572549 1.2776300 1.3013117 1.3202908
+#> 24: 1.2445379 0.04971247 1.1650458 1.2120100 1.2316036 1.2559181 1.2751219
+#> 25: 1.2008969 0.04931451 1.1220326 1.1692435 1.1885228 1.2115402 1.2322867
+#> 26: 1.1585347 0.04964892 1.0787812 1.1264758 1.1463528 1.1686740 1.1908429
+#> 27: 1.1176967 0.05114163 1.0349831 1.0841377 1.1045945 1.1286412 1.1507694
+#> 28: 1.0786178 0.05419002 0.9900636 1.0438489 1.0646946 1.0906172 1.1136690
+#> 29: 1.0415139 0.05903373 0.9463847 1.0037804 1.0263286 1.0544926 1.0787054
+#> 30: 1.0065729 0.06569194 0.8996783 0.9645353 0.9893831 1.0205071 1.0498277
+#> 31: 0.9739497 0.07400918 0.8537320 0.9241620 0.9558316 0.9888470 1.0220276
+#> 32: 0.9437633 0.08375962 0.8093050 0.8880214 0.9214014 0.9605303 0.9979074
+#> 33: 0.9160978 0.09473853 0.7648559 0.8526487 0.8910609 0.9361275 0.9771412
+#> 34: 0.8910053 0.10680803 0.7207233 0.8210039 0.8604409 0.9145747 0.9600097
+#> 35: 0.8685081 0.11989808 0.6809183 0.7890024 0.8344334 0.8936418 0.9477999
+#> 36: 0.8485996 0.13397913 0.6421538 0.7606687 0.8109790 0.8736524 0.9341676
+#> 37: 0.8312435 0.14902846 0.6028938 0.7340772 0.7875024 0.8574421 0.9230531
+#> 38: 0.8163713 0.16500692 0.5709322 0.7090663 0.7684541 0.8454247 0.9140968
+#> 39: 0.8038812 0.18184986 0.5389194 0.6874220 0.7502059 0.8337178 0.9094884
+#> 40: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 41: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 42: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 43: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 44: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 45: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 46: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
+#> 47: 0.7936384 0.19946247 0.5067533 0.6684721 0.7315682 0.8188544 0.9065502
#> mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
-#> 1: 2.218045
-#> 2: 2.188334
-#> 3: 2.166292
-#> 4: 2.148114
-#> 5: 2.127795
-#> 6: 2.105892
-#> 7: 2.083002
-#> 8: 2.052544
-#> 9: 2.018622
-#> 10: 1.975189
-#> 11: 1.933968
-#> 12: 1.889693
-#> 13: 1.844601
-#> 14: 1.797986
-#> 15: 1.751755
-#> 16: 1.706830
-#> 17: 1.659899
-#> 18: 1.614138
-#> 19: 1.567617
-#> 20: 1.518484
-#> 21: 1.467544
-#> 22: 1.416691
-#> 23: 1.371550
-#> 24: 1.323983
-#> 25: 1.280000
-#> 26: 1.236005
-#> 27: 1.196761
-#> 28: 1.164351
-#> 29: 1.136624
-#> 30: 1.112678
-#> 31: 1.093556
-#> 32: 1.082855
-#> 33: 1.077849
-#> 34: 1.075605
-#> 35: 1.082215
-#> 36: 1.087623
-#> 37: 1.098749
-#> 38: 1.112807
-#> 39: 1.128388
-#> 40: 1.147318
-#> 41: 1.147318
-#> 42: 1.147318
-#> 43: 1.147318
-#> 44: 1.147318
-#> 45: 1.147318
-#> 46: 1.147318
-#> 47: 1.147318
+#> 1: 2.223460
+#> 2: 2.198021
+#> 3: 2.175876
+#> 4: 2.159349
+#> 5: 2.135009
+#> 6: 2.113177
+#> 7: 2.086532
+#> 8: 2.054193
+#> 9: 2.017649
+#> 10: 1.976230
+#> 11: 1.931684
+#> 12: 1.890184
+#> 13: 1.845283
+#> 14: 1.796508
+#> 15: 1.751461
+#> 16: 1.703839
+#> 17: 1.658727
+#> 18: 1.609549
+#> 19: 1.559474
+#> 20: 1.510314
+#> 21: 1.463889
+#> 22: 1.416947
+#> 23: 1.372417
+#> 24: 1.326898
+#> 25: 1.281746
+#> 26: 1.240077
+#> 27: 1.202808
+#> 28: 1.169020
+#> 29: 1.139084
+#> 30: 1.116139
+#> 31: 1.099959
+#> 32: 1.089888
+#> 33: 1.075114
+#> 34: 1.067066
+#> 35: 1.067619
+#> 36: 1.075636
+#> 37: 1.086348
+#> 38: 1.096073
+#> 39: 1.105078
+#> 40: 1.117807
+#> 41: 1.117807
+#> 42: 1.117807
+#> 43: 1.117807
+#> 44: 1.117807
+#> 45: 1.117807
+#> 46: 1.117807
+#> 47: 1.117807
#> upper_90
options(old_opts)
diff --git a/dev/reference/estimate_delay.html b/dev/reference/estimate_delay.html
index c51775503..8fd92f86f 100644
--- a/dev/reference/estimate_delay.html
+++ b/dev/reference/estimate_delay.html
@@ -116,7 +116,7 @@ Examples#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#>
-#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.092) and logSD 1.1 (SD 0.08)
+#> Uncertain lognormal distribution with (untruncated) logmean 1.4 (SD 0.11) and logSD 1.1 (SD 0.067)
#>
# }
diff --git a/dev/reference/estimate_infections-1.png b/dev/reference/estimate_infections-1.png
index 7bceb5b96..d25d6c92f 100644
Binary files a/dev/reference/estimate_infections-1.png and b/dev/reference/estimate_infections-1.png differ
diff --git a/dev/reference/estimate_infections.html b/dev/reference/estimate_infections.html
index 4b8b37543..19484090a 100644
--- a/dev/reference/estimate_infections.html
+++ b/dev/reference/estimate_infections.html
@@ -270,226 +270,28 @@ Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
diff --git a/dev/reference/estimate_secondary-1.png b/dev/reference/estimate_secondary-1.png
index 79cec7922..9ce3258cc 100644
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index 50aa45b6e..deccbbb0f 100644
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diff --git a/dev/reference/estimate_secondary-4.png b/dev/reference/estimate_secondary-4.png
index a29d2677f..d74092b7b 100644
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diff --git a/dev/reference/figures/unnamed-chunk-15-1.png b/dev/reference/figures/unnamed-chunk-15-1.png
index bd17ce2a9..be97c1b84 100644
Binary files a/dev/reference/figures/unnamed-chunk-15-1.png and b/dev/reference/figures/unnamed-chunk-15-1.png differ
diff --git a/dev/reference/figures/unnamed-chunk-19-1.png b/dev/reference/figures/unnamed-chunk-19-1.png
index 28b59741d..7f188369e 100644
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
diff --git a/dev/reference/plot_estimates-1.png b/dev/reference/plot_estimates-1.png
index e060d7df6..d2bc233da 100644
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index 6b747148e..66cee9970 100644
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diff --git a/dev/reference/plot_estimates-3.png b/dev/reference/plot_estimates-3.png
index 2d044fc78..c828818f2 100644
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diff --git a/dev/reference/plot_estimates-4.png b/dev/reference/plot_estimates-4.png
index 59f0adf53..9220e9caf 100644
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples sd = convert_to_logsd(2, 1), sd_sd = 0, max = 10
)
-# default settings but assuming that delays are fixed rather than uncertain
+# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
generation_time = generation_time_opts(generation_time),
delays = delay_opts(incubation_period + reporting_delay),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
stan = stan_opts(control = list(adapt_delta = 0.95))
)
-#> Warning: There were 16 divergent transitions after warmup. See
+#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# real time estimates
summary(def)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2288 (1090 -- 4452)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.88 (0.61 -- 1.2)
-#> 4: Rate of growth -0.026 (-0.099 -- 0.04)
-#> 5: Doubling/halving time (days) -26 (18 -- -7)
+#> measure estimate
+#> 1: New confirmed cases by infection date 2243 (1105 -- 4457)
+#> 2: Expected change in daily cases Likely decreasing
+#> 3: Effective reproduction no. 0.87 (0.61 -- 1.2)
+#> 4: Rate of growth -0.028 (-0.097 -- 0.038)
+#> 5: Doubling/halving time (days) -25 (18 -- -7.2)
# summary plot
plot(def)
-
-# decreasing the accuracy of the approximate Gaussian to speed up
-#computation.
-# These settings are an area of active research. See ?gp_opts for details.
-agp <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 7 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-summary(agp)
-#> measure estimate
-#> 1: New confirmed cases by infection date 2317 (1222 -- 4466)
-#> 2: Expected change in daily cases Likely decreasing
-#> 3: Effective reproduction no. 0.89 (0.65 -- 1.2)
-#> 4: Rate of growth -0.025 (-0.086 -- 0.039)
-#> 5: Doubling/halving time (days) -28 (18 -- -8.1)
-plot(agp)
-
-
-# Adjusting for future susceptible depletion
-dep <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- pop = 1000000, future = "latest"
- ),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1), horizon = 21,
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(dep)
-
-
-# Adjusting for truncation of the most recent data
-# See estimate_truncation for an approach to estimating this from data
-trunc_dist <- dist_spec(
- mean = convert_to_logmean(0.5, 0.5), mean_sd = 0.1,
- sd = convert_to_logsd(0.5, 0.5), sd_sd = 0.1,
- max = 3
-)
-trunc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- truncation = trunc_opts(trunc_dist),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = gp_opts(ls_min = 10, basis_prop = 0.1),
- stan = stan_opts(control = list(adapt_delta = 0.95))
-)
-#> Warning: There were 12 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(trunc)
-
-
-# using back calculation (combined here with under reporting)
-# this model is in the order of 10 ~ 100 faster than the gaussian process
-# method
-# it is likely robust for retrospective Rt but less reliable for real time
-# estimates
-# the width of the prior window controls the reliance on observed data and
-# can be optionally switched off using backcalc_opts(prior = "none"),
-# see ?backcalc_opts for other options
-backcalc <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = NULL, backcalc = backcalc_opts(),
- obs = obs_opts(scale = list(mean = 0.4, sd = 0.05)),
- horizon = 0
-)
-plot(backcalc)
-
-
-# Rt projected into the future using the Gaussian process
-project_rt <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(
- prior = list(mean = 2, sd = 0.1),
- future = "project"
- )
-)
-#> Warning: There were 2 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(project_rt)
-
-
-# default settings on a later snapshot of data
-snapshot_cases <- example_confirmed[80:130]
-snapshot <- estimate_infections(snapshot_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(prior = list(mean = 1, sd = 0.1))
-)
-#> Warning: There were 9 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(snapshot)
-
-
-# stationary Rt assumption (likely to provide biased real-time estimates)
-# with uncertain reporting delays
-stat <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), gp_on = "R0")
-)
-plot(stat)
-
-
-# no gaussian process (i.e fixed Rt assuming no breakpoints)
-# with uncertain reporting delays
-fixed <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- gp = NULL
-)
-plot(fixed)
-
-
-# no delays
-no_delay <- estimate_infections(
- reported_cases,
- generation_time = generation_time_opts(generation_time)
-)
-#> Warning: There were 6 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
-plot(no_delay)
-
-
-# break point but otherwise static Rt
-# with uncertain reporting delays
-bp_cases <- data.table::copy(reported_cases)
-bp_cases <- bp_cases[,
- breakpoint := ifelse(date == as.Date("2020-03-16"), 1, 0)
-]
-bkp <- estimate_infections(bp_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
- gp = NULL
-)
-# break point effect
-summary(bkp, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.6706079 -0.6707402 0.03079727 -0.7205391
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.6920782 -0.677934 -0.663612 -0.6496607 -0.6199435
-plot(bkp)
-
-
-# weekly random walk
-# with uncertain reporting delays
-rw <- estimate_infections(reported_cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period_uncertain + reporting_delay),
- rt = rt_opts(prior = list(mean = 2, sd = 0.1), rw = 7),
- gp = NULL
-)
-
-# random walk effects
-summary(rw, type = "parameters", params = "breakpoints")
-#> date variable strat type median mean sd lower_90
-#> 1: <NA> breakpoints 1 <NA> -0.13147171 -0.13165406 0.06754508 -0.2436721
-#> 2: <NA> breakpoints 2 <NA> -0.16519498 -0.16579733 0.08090044 -0.2985345
-#> 3: <NA> breakpoints 3 <NA> -0.22426607 -0.22009731 0.09409975 -0.3700840
-#> 4: <NA> breakpoints 4 <NA> -0.27288529 -0.27893416 0.10131415 -0.4519362
-#> 5: <NA> breakpoints 5 <NA> -0.06125400 -0.06214609 0.10181322 -0.2285185
-#> 6: <NA> breakpoints 6 <NA> 0.03729778 0.03622026 0.10364271 -0.1322581
-#> 7: <NA> breakpoints 7 <NA> -0.05214400 -0.05366692 0.11556742 -0.2430230
-#> 8: <NA> breakpoints 8 <NA> -0.01826475 -0.02225903 0.17383430 -0.3114837
-#> lower_50 lower_20 upper_20 upper_50 upper_90
-#> 1: -0.17719182 -0.14870718 -0.11245775 -0.084840789 -0.02463561
-#> 2: -0.21731991 -0.18472591 -0.14526880 -0.112817291 -0.03270595
-#> 3: -0.28347439 -0.24447000 -0.19886899 -0.156702712 -0.06781363
-#> 4: -0.34821974 -0.30158352 -0.24941575 -0.205805213 -0.11860007
-#> 5: -0.13155188 -0.08593423 -0.03672112 0.003281974 0.10832854
-#> 6: -0.03459092 0.01008262 0.06048472 0.102449845 0.20412552
-#> 7: -0.12453964 -0.07880822 -0.02389124 0.024575211 0.13557993
-#> 8: -0.13592836 -0.06196672 0.02553465 0.091020111 0.25225923
-plot(rw)
-
-
options(old_opts)
# }
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diff --git a/dev/reference/get_regional_results.html b/dev/reference/get_regional_results.html
index 481a3b140..eed3ee350 100644
--- a/dev/reference/get_regional_results.html
+++ b/dev/reference/get_regional_results.html
@@ -121,70 +121,13 @@ Author<
Examples
- # \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
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diff --git a/dev/reference/plot_estimates.html b/dev/reference/plot_estimates.html
index de94a5d10..65314210d 100644
--- a/dev/reference/plot_estimates.html
+++ b/dev/reference/plot_estimates.html
@@ -135,42 +135,15 @@ Value
Examples
- # \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }
Examples
-# \donttest{
-# construct example distributions
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# example case vector
-cases <- example_confirmed[1:30]
-cases <- data.table::rbindlist(list(
- data.table::copy(cases)[, region := "testland"],
- cases[, region := "realland"]
+ # get example multiregion estimates
+regional_out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_regional_epinow.rds"
))
-# save results to tmp folder
-dir <- file.path(tempdir(check = TRUE), "results")
-# run multiregion estimates
-regional_out <- regional_epinow(
- reported_cases = cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay),
- rt = rt_opts(rw = 7), gp = NULL,
- output = c("regions", "latest"),
- target_folder = dir,
- return_output = TRUE
-)
-#> INFO [2023-09-27 17:31:36] Producing following optional outputs: regions, latest
-#> Logging threshold set at INFO for the EpiNow2 logger
-#> Writing EpiNow2 logs to the console and: /tmp/RtmpYkZBel/regional-epinow/2020-03-22.log
-#> Logging threshold set at INFO for the EpiNow2.epinow logger
-#> Writing EpiNow2.epinow logs to: /tmp/RtmpYkZBel/epinow/2020-03-22.log
-#> INFO [2023-09-27 17:31:36] Reporting estimates using data up to: 2020-03-22
-#> INFO [2023-09-27 17:31:36] Saving estimates to : /tmp/RtmpYkZBel/results
-#> INFO [2023-09-27 17:31:36] Producing estimates for: testland, realland
-#> INFO [2023-09-27 17:31:36] Regions excluded: none
-#> INFO [2023-09-27 17:32:05] Completed estimates for: testland
-#> INFO [2023-09-27 17:32:30] Completed estimates for: realland
-#> INFO [2023-09-27 17:32:30] Completed regional estimates
-#> INFO [2023-09-27 17:32:30] Regions with estimates: 2
-#> INFO [2023-09-27 17:32:30] Regions with runtime errors: 0
-#> INFO [2023-09-27 17:32:30] Saving timings information to : /tmp/RtmpYkZBel/results
# from output
results <- get_regional_results(regional_out$regional, samples = FALSE)
-names(results)
-#> [1] "estimates"
-
-# from a folder
-folder_results <- get_regional_results(results_dir = dir, samples = FALSE)
-names(folder_results)
-#> [1] "estimates"
-# }
Value
Examples
-# \donttest{
-# define example cases
-cases <- example_confirmed[1:40]
+ # get example model results
+out <- readRDS(system.file(
+ package = "EpiNow2", "extdata", "example_estimate_infections.rds"
+))
-# set up example delays
-generation_time <- get_generation_time(
- disease = "SARS-CoV-2", source = "ganyani"
-)
-incubation_period <- get_incubation_period(
- disease = "SARS-CoV-2", source = "lauer"
-)
-reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
-
-# run model
-out <- estimate_infections(cases,
- generation_time = generation_time_opts(generation_time),
- delays = delay_opts(incubation_period + reporting_delay)
-)
-#> Warning: There were 10 divergent transitions after warmup. See
-#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
-#> to find out why this is a problem and how to eliminate them.
-#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
estimate = out$summarised[variable == "infections"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")
@@ -178,7 +151,7 @@ Examples# plot reported cases estimated via Rt
plot_estimates(
estimate = out$summarised[variable == "reported_cases"],
- reported = cases,
+ reported = out$observations,
ylab = "Cases"
)
@@ -198,7 +171,6 @@ Examples hline = 1, estimate_type = "Estimate"
)
-# }