Update examples

README.Rmd

@@ -206,10 +206,10 @@ discretized versions of popular continuous probability distributions.
 
 ```{r}
 ww_assumptions <- sewer_assumptions(
-  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset", # shedding load distribution is relative to symptom onset
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 ```
 
@@ -224,7 +224,7 @@ Now that we have the data and necessary assumptions, we can use `EpiSewer` to es
 Stan regularly provides updates about the progress of the sampler. The overall 
 runtime will depend on your hardware resources, the size of the data, 
 the complexity of the model used, and how well the model actually fits the data.
-On a MacBook Pro (2 GHz Quad-Core Intel Core i5) the example below takes about 4 minutes to run.
+On a MacBook Pro (2 GHz Quad-Core Intel Core i5) the example below takes about 5 minutes to run.
 ```{r, eval = FALSE}
 options(mc.cores = 4) # allow stan to use 4 cores, i.e. one for each chain
 ww_result <- EpiSewer(

README.md

@@ -274,10 +274,10 @@ probability distributions.
 
 ``` r
 ww_assumptions <- sewer_assumptions(
-  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset", # shedding load distribution is relative to symptom onset
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 ```
 
@@ -299,7 +299,7 @@ Stan regularly provides updates about the progress of the sampler. The
 overall runtime will depend on your hardware resources, the size of the
 data, the complexity of the model used, and how well the model actually
 fits the data. On a MacBook Pro (2 GHz Quad-Core Intel Core i5) the
-example below takes about 4 minutes to run.
+example below takes about 5 minutes to run.
 
 ``` r
 options(mc.cores = 4) # allow stan to use 4 cores, i.e. one for each chain
@@ -547,11 +547,11 @@ number.
 ``` r
 head(ww_result$summary$R, 5)
 #>          date     mean   median lower_0.95 lower_0.5 upper_0.5 upper_0.95
-#> 1: 2021-12-06 1.049711 1.044085  0.7324020 0.9372597  1.152850   1.395459
-#> 2: 2021-12-07 1.050463 1.042825  0.7470177 0.9427265  1.150043   1.380040
-#> 3: 2021-12-08 1.051356 1.041470  0.7572534 0.9485155  1.150755   1.377987
-#> 4: 2021-12-09 1.052399 1.044530  0.7605042 0.9530230  1.149892   1.369603
-#> 5: 2021-12-10 1.053602 1.047620  0.7645480 0.9585617  1.151843   1.366598
+#> 1: 2021-12-03 1.042238 1.044795  0.7012397 0.9294330  1.157058   1.365061
+#> 2: 2021-12-04 1.043177 1.044975  0.7155260 0.9338175  1.155830   1.355124
+#> 3: 2021-12-05 1.044297 1.045450  0.7303710 0.9367903  1.154685   1.346828
+#> 4: 2021-12-06 1.045626 1.045795  0.7430955 0.9419335  1.152473   1.342926
+#> 5: 2021-12-07 1.047189 1.046505  0.7521539 0.9480593  1.147990   1.336999
 #>        type seeding
 #> 1: estimate    TRUE
 #> 2: estimate    TRUE
@@ -575,7 +575,7 @@ ww_result$fitted$diagnostic_summary()
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 1.0296476 0.9815957 1.0001229 0.8657642
+#> [1] 0.9014503 0.9680378 0.9334176 0.9486877
 ```
 
 Finally, the `checksums` attribute gives us several checksums that
@@ -590,7 +590,7 @@ ww_result$checksums
 #> [1] "6346549bd7c2ac9a9d200503d7356a29"
 #> 
 #> $input
-#> [1] "fd1bc664ef6320ad9f8427d3a0f3d18c"
+#> [1] "c6dad2f8ed2b6fdaf952926f3eb3e97d"
 #> 
 #> $fit_opts
 #> [1] "5309bbbc3cd1cc109eac60d2fc82de45"
@@ -599,5 +599,5 @@ ww_result$checksums
 #> [1] "e92f83d0ca5d22b3bb5849d62c5412ee"
 #> 
 #> $init
-#> [1] "30505348d747504aa36fe8fb6bdfaaf3"
+#> [1] "82126b86311c9716bda5d21c04c52142"
 ```

data-raw/Influenza Zurich.R

@@ -38,8 +38,8 @@ measurements <- measurements[date <= as.Date("2022-12-19")]
 ww_data_influenza_Zurich <- sewer_data(measurements = measurements, flows = Influenza_A_Zurich$flows)
 usethis::use_data(ww_data_influenza_Zurich, overwrite = TRUE)
 
-generation_dist <- get_discrete_gamma_shifted(gamma_mean = 2.6, gamma_sd = 1.5, maxX = 8)
-shedding_dist <- get_discrete_gamma(gamma_mean = 2.491217, gamma_sd = 1.004283, maxX = 6)
+generation_dist <- get_discrete_gamma_shifted(gamma_mean = 2.6, gamma_sd = 1.5)
+shedding_dist <- get_discrete_gamma(gamma_mean = 2.491217, gamma_sd = 1.004283)
 ww_assumptions_influenza_Zurich <- sewer_assumptions(
   generation_dist = generation_dist,
   shedding_dist = shedding_dist,

data-raw/SARS-CoV-2 Zurich.R

@@ -57,9 +57,9 @@ measurements_sparse <- data_zurich$measurements[,weekday := weekdays(data_zurich
 ww_data_SARS_CoV_2_Zurich <- sewer_data(measurements = measurements_sparse, flows = data_zurich$flows, cases = data_zurich$cases)
 usethis::use_data(ww_data_SARS_CoV_2_Zurich, overwrite = TRUE)
 
-generation_dist <- get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)
-incubation_dist <- get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10)
-shedding_dist <- get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30)
+generation_dist <- get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)
+incubation_dist <- get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4)
+shedding_dist <- get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397)
 ww_assumptions_SARS_CoV_2_Zurich <- sewer_assumptions(
   generation_dist = generation_dist,
   incubation_dist = incubation_dist,

vignettes/detailed-example.Rmd

@@ -153,8 +153,8 @@ The `shedding` module describes how infected individuals shed pathogen particles
 ```{r}
 ww_shedding <- model_shedding(
   shedding_dist = shedding_dist_assume(
-    get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30), shedding_reference = "symptom_onset"),
-  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10)),
+    get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397), shedding_reference = "symptom_onset"),
+  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4)),
   load_per_case = load_per_case_calibrate(cases = data_zurich$cases),
   load_variation = load_variation_estimate()
 )
@@ -173,7 +173,7 @@ The `infections` module describes the underlying process leading to infected ind
 
 ```{r}
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)),
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)),
   R = R_estimate_splines(),
   seeding = seeding_estimate_rw(),
   infection_noise = infection_noise_estimate()
@@ -212,7 +212,7 @@ We have now specified all six `EpiSewer` modules. While this seems like a lot of
 
 ```{r}
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12))
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4))
 )
 ```
 
@@ -324,14 +324,14 @@ ww_sewage <- model_sewage(
 )
 
 ww_shedding <- model_shedding(
-  shedding_dist = shedding_dist_assume(get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30), shedding_reference = "symptom_onset"),
-  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10)),
+  shedding_dist = shedding_dist_assume(get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397), shedding_reference = "symptom_onset"),
+  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4)),
   load_per_case = load_per_case_calibrate(cases = data_zurich$cases),
   load_variation = load_variation_estimate()
 )
 
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)),
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)),
   R = R_estimate_splines(),
   seeding = seeding_estimate_rw(),
   infection_noise = infection_noise_estimate()

vignettes/detailed-example.md

@@ -11,7 +11,7 @@ to first read the `vignette("model-specification")` vignette.
 ### Loading the package
 
 We assume that `EpiSewer` is already successfully installed (see
-[README](../index.html#Installing%20the%20package)).
+[README](../index.html): installing the package).
 
 ``` r
 library(EpiSewer)
@@ -121,8 +121,7 @@ function for each option. To see all available options, you can use
 
 ``` r
 component_functions("LOD")
-#> [1] "LOD_none()"          "LOD_assume()"       
-#> [3] "LOD_estimate_dPCR()"
+#> [1] "LOD_none()"          "LOD_assume()"        "LOD_estimate_dPCR()"
 ```
 
 For example, we could assume a limit of detection of `2.56 gc/mL` (based
@@ -200,8 +199,8 @@ particles into the wastewater:
 ``` r
 ww_shedding <- model_shedding(
   shedding_dist = shedding_dist_assume(
-    get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30), shedding_reference = "symptom_onset"),
-  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10)),
+    get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397), shedding_reference = "symptom_onset"),
+  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4)),
   load_per_case = load_per_case_calibrate(cases = data_zurich$cases),
   load_variation = load_variation_estimate()
 )
@@ -228,7 +227,7 @@ infected individuals:
 
 ``` r
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)),
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)),
   R = R_estimate_splines(),
   seeding = seeding_estimate_rw(),
   infection_noise = infection_noise_estimate()
@@ -291,7 +290,7 @@ described above could simply be written as
 
 ``` r
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12))
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4))
 )
 ```
 
@@ -437,14 +436,14 @@ ww_sewage <- model_sewage(
 )
 
 ww_shedding <- model_shedding(
-  shedding_dist = shedding_dist_assume(get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30), shedding_reference = "symptom_onset"),
-  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10)),
+  shedding_dist = shedding_dist_assume(get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397), shedding_reference = "symptom_onset"),
+  incubation_dist = incubation_dist_assume(get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4)),
   load_per_case = load_per_case_calibrate(cases = data_zurich$cases),
   load_variation = load_variation_estimate()
 )
 
 ww_infections <- model_infections(
-  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)),
+  generation_dist = generation_dist_assume(get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)),
   R = R_estimate_splines(),
   seeding = seeding_estimate_rw(),
   infection_noise = infection_noise_estimate()

vignettes/model-specification.Rmd

@@ -139,10 +139,10 @@ ww_data <- sewer_data(
   )
 
 ww_assumptions <- sewer_assumptions(
-  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset",
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 
 EpiSewer(
@@ -165,10 +165,10 @@ ww_data <- sewer_data(
 
 # Leave out the generation time distribution
 ww_assumptions <- sewer_assumptions(
-  #generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  #generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset",
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 
 # Provide flows directly to sewage module
@@ -179,7 +179,7 @@ ww_sewage <- model_sewage(
 # Provide generation time distribution directly to infections module
 ww_infections <- model_infections(
   generation_dist = generation_dist_assume(
-    get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)
+    get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)
   )
 )
 

vignettes/model-specification.md

@@ -2,9 +2,9 @@ Modeling in EpiSewer
 ================
 
 This is a conceptual overview over the modeling functionality in
-`EpiSewer`. See [here](model-definition.md) for a mathematical
+`EpiSewer`. See `vignette("model-definition")` for a mathematical
 definition of the underlying generative model, and
-[here](detailed-example.md) for an example vignette.
+`vignette("detailed-example")` for an example vignette.
 
 ## Modules
 
@@ -14,7 +14,7 @@ process behind the wastewater measurements: `infections`, `shedding`,
 specify `forecast` functionality. Each of these modules consists of a
 number of module components, as shown below.
 
-<img src="figures/specification-model-1.png" width="100%" />
+<img src="figures/specification-model-1.png" alt="The 6 modules of the EpiSewer model" width="100%" />
 
 The modules are defined using their corresponding module function,
 i.e. by calling `model_infections()`, `model_shedding()`,
@@ -51,28 +51,27 @@ component, you can consult the documentation or use the helper
 
 ``` r
 EpiSewer::component_functions("infection_noise")
-#> [1] "infection_noise_none()"    
-#> [2] "infection_noise_estimate()"
+#> [1] "infection_noise_none()"     "infection_noise_estimate()"
 ```
 
 #### 💡 Multiple modeling options
 
 Some components have multiple versions of the same modeling function
 type. For example, there are currently three approaches to estimate the
-reproduction number, namely `R_estimate_splines` (smoothing splines),
-`R_estimate_rw` (random walk), `R_estimate_ets` (exponential smoothing),
-and `R_estimate_approx` (approximation of renewal model).
+reproduction number, namely `R_estimate_splines()` (smoothing splines),
+`R_estimate_rw()` (random walk), `R_estimate_ets()` (exponential
+smoothing), and `R_estimate_approx()` (approximation of renewal model).
 
 ``` r
 EpiSewer::component_functions("R")
-#> [1] "R_estimate_splines()" "R_estimate_ets()"    
-#> [3] "R_estimate_approx()"  "R_estimate_rw()"
+#> [1] "R_estimate_splines()" "R_estimate_ets()"     "R_estimate_approx()" 
+#> [4] "R_estimate_rw()"
 ```
 
 #### ❗ Modeling restrictions
 
 Not all components support all modeling types. For example, `EpiSewer`
-currently only offers `generation_dist_assume`, but not
+currently only offers `generation_dist_assume()`, but not
 `generation_dist_estimate`, `generation_dist_calibrate`, or
 `generation_dist_observe`.
 
@@ -100,10 +99,10 @@ ww_data <- sewer_data(
   )
 
 ww_assumptions <- sewer_assumptions(
-  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset",
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 
 EpiSewer(
@@ -114,7 +113,7 @@ EpiSewer(
 
 What happens under the hood is that when individual model components are
 not provided with the data or assumptions they need, they search the
-data and assumption arguments passed to the `EpiSewer` function.
+data and assumption arguments passed to the `EpiSewer()` function.
 
 💡 It is always possible to mix both approaches and specify data or
 assumptions explicitly in the model component:
@@ -129,10 +128,10 @@ ww_data <- sewer_data(
 
 # Leave out the generation time distribution
 ww_assumptions <- sewer_assumptions(
-  #generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12),
-  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397, maxX = 30),
+  #generation_dist = get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4),
+  shedding_dist = get_discrete_gamma(gamma_shape = 0.929639, gamma_scale = 7.241397),
   shedding_reference = "symptom_onset",
-  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4, maxX = 10),
+  incubation_dist = get_discrete_gamma(gamma_shape = 8.5, gamma_scale = 0.4),
 )
 
 # Provide flows directly to sewage module
@@ -143,7 +142,7 @@ ww_sewage <- model_sewage(
 # Provide generation time distribution directly to infections module
 ww_infections <- model_infections(
   generation_dist = generation_dist_assume(
-    get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4, maxX = 12)
+    get_discrete_gamma_shifted(gamma_mean = 3, gamma_sd = 2.4)
   )
 )
 
@@ -158,5 +157,5 @@ result <- EpiSewer(
 
 Note that if the same data or assumptions are supplied via the
 `sewer_data()`/`sewer_assumptions()` *and* the individual component,
-`EpiSewer` will compare both arguments and throw an error if they
+`EpiSewer()` will compare both arguments and throw an error if they
 differ.