chore(rmarkdown): re-run all rmarkdown 🎉 all results are the same 🥳 (only change is in logs - printing dist / dist_config etc., and as all the destination codes 1 2 3 esd etc. are now consistent - a happy benefit of the changes!) (#11)

Author: amyheather

outputs/log_example.log

@@ -1,10 +1,10 @@
 Parameters:
-asu_arrivals=list(stroke = 1.2, tia = 9.3, neuro = 3.6, other = 3.2);
- rehab_arrivals=list(stroke = 3, neuro = 31.7, other = 28.6);
- asu_los=list(stroke_no_esd = list(mean = 7.4, sd = 8.61), stroke_esd = list(mean = 4.6, sd = 4.8), stroke_mortality = list(mean = 7, sd = 8.7), tia = list(mean = 1.8, sd = 2.3), neuro = list(mean = 4, sd = 5), other = list(mean = 3.8, sd = 5.2));
- rehab_los=list(stroke_no_esd = list(mean = 28.4, sd = 27.2), stroke_esd = list(mean = 30.3, sd = 23.1), tia = list(mean = 18.7, sd = 23.5), neuro = list(mean = 27.6, sd = 28.4), other = list(mean = 16.1, sd = 14.1));
- asu_routing=list(stroke = list(rehab = 0.24, esd = 0.13, other = 0.63), tia = list(rehab = 0.01, esd = 0.01, other = 0.98), neuro = list(rehab = 0.11, esd = 0.05, other = 0.84), other = list(rehab = 0.05, esd = 0.1, other = 0.85));
- rehab_routing=list(stroke = list(esd = 0.4, other = 0.6), tia = list(esd = 0, other = 1), neuro = list(esd = 0.09, other = 0.91), other = list(esd = 0.13, other = 0.88));
+dist_config=list(asu_arrival_stroke = list(class_name = "exponential", params = list(mean = 1.2)), asu_arrival_tia = list(class_name = "exponential", params = list(mean = 9.3)), asu_arrival_neuro = list(class_name = "exponential", params = list(mean = 3.6)), asu_arrival_other = list(class_name = "exponential", params = list(mean = 3.2)), rehab_arrival_stroke = list(class_name = "exponential", params = list(mean = 3)), rehab_arrival_neuro = list(class_name = "exponential", params = list(mean = 31.7)), rehab_arrival_other = list(
+    class_name = "exponential", params = list(mean = 28.6)), asu_los_stroke_noesd = list(class_name = "lognormal", params = list(mean = 7.4, sd = 8.61)), asu_los_stroke_esd = list(class_name = "lognormal", params = list(mean = 4.6, sd = 4.8)), asu_los_stroke_mortality = list(class_name = "lognormal", params = list(mean = 7, sd = 8.7)), asu_los_tia = list(class_name = "lognormal", params = list(mean = 1.8, sd = 2.3)), asu_los_neuro = list(class_name = "lognormal", params = list(mean = 4, sd = 5)), 
+    asu_los_other = list(class_name = "lognormal", params = list(mean = 3.8, sd = 5.2)), rehab_los_stroke_noesd = list(class_name = "lognormal", params = list(mean = 28.4, sd = 27.2)), rehab_los_stroke_esd = list(class_name = "lognormal", params = list(mean = 30.3, sd = 23.1)), rehab_los_tia = list(class_name = "lognormal", params = list(mean = 18.7, sd = 23.5)), rehab_los_neuro = list(class_name = "lognormal", params = list(mean = 27.6, sd = 28.4)), rehab_los_other = list(class_name = "lognormal", 
+        params = list(mean = 16.1, sd = 14.1)), asu_routing_stroke = list(class_name = "discrete", params = list(values = list("rehab", "esd", "other"), prob = list(0.24, 0.13, 0.63))), asu_routing_tia = list(class_name = "discrete", params = list(values = list("rehab", "esd", "other"), prob = list(0.01, 0.01, 0.98))), asu_routing_neuro = list(class_name = "discrete", params = list(values = list("rehab", "esd", "other"), prob = list(0.11, 0.05, 0.84))), asu_routing_other = list(class_name = "discrete", 
+        params = list(values = list("rehab", "esd", "other"), prob = list(0.05, 0.1, 0.85))), rehab_routing_stroke = list(class_name = "discrete", params = list(values = list("esd", "other"), prob = list(0.4, 0.6))), rehab_routing_tia = list(class_name = "discrete", params = list(values = list("esd", "other"), prob = list(0, 1))), rehab_routing_neuro = list(class_name = "discrete", params = list(values = list("esd", "other"), prob = list(0.09, 0.91))), rehab_routing_other = list(class_name = "discrete", 
+        params = list(values = list("esd", "other"), prob = list(0.13, 0.88))));
  warm_up_period=3;
  data_collection_period=5;
  number_of_runs=1;
@@ -14,8 +14,40 @@ asu_arrivals=list(stroke = 1.2, tia = 9.3, neuro = 3.6, other = 3.2);
  log_to_file=TRUE;
  file_path=../outputs/log_example.log;
  verbose=TRUE;
- asu_los_lnorm=list(stroke_no_esd = list(meanlog = 1.57347219029677, sdlog = 0.925211121759084), stroke_esd = list(meanlog = 1.15775021244827, sdlog = 0.858261138636461), stroke_mortality = list(meanlog = 1.47890496960638, sdlog = 0.966442113578391), tia = list(meanlog = 0.103778649052444, sdlog = 0.983878057331979), neuro = list(meanlog = 0.915802688887627, sdlog = 0.970042960112864), other = list(meanlog = 0.807396441784017, sdlog = 1.02723378541433));
- rehab_los_lnorm=list(stroke_no_esd = list(meanlog = 3.02093582638726, sdlog = 0.806787851643665), stroke_esd = list(meanlog = 3.18205014379696, sdlog = 0.676901128257839), tia = list(meanlog = 2.45477304821951, sdlog = 0.973396605337238), neuro = list(meanlog = 2.95675141458562, sdlog = 0.849781569743057), other = list(meanlog = 2.49418211833932, sdlog = 0.754502688730926))
+ dist=list(arrival = list(asu = list(stroke = function (size = 1) 
+rexp(size, rate = 1/mean), tia = function (size = 1) 
+rexp(size, rate = 1/mean), neuro = function (size = 1) 
+rexp(size, rate = 1/mean), other = function (size = 1) 
+rexp(size, rate = 1/mean)), rehab = list(stroke = function (size = 1) 
+rexp(size, rate = 1/mean), neuro = function (size = 1) 
+rexp(size, rate = 1/mean), other = function (size = 1) 
+rexp(size, rate = 1/mean))), los = list(asu = list(stroke_noesd = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), stroke_esd = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), stroke_mortality = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), tia = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), neuro = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), other = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog)), rehab = list(stroke_noesd = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), stroke_esd = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), tia = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), neuro = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog), other = function (size = 1) 
+rlnorm(size, meanlog = meanlog, sdlog = sdlog))), routing = list(asu = list(stroke = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), tia = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), neuro = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), other = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob)), rehab = list(stroke = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), tia = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), neuro = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob), other = function (size = 1) 
+sample(values, size = size, replace = TRUE, prob = prob))));
+ map_val2num=1:3;
+ map_num2val=c(`1` = "rehab", `2` = "esd", `3` = "other");
+ dist_config_num=list(asu_arrival_stroke = list(class_name = "exponential", params = list(mean = 1.2)), asu_arrival_tia = list(class_name = "exponential", params = list(mean = 9.3)), asu_arrival_neuro = list(class_name = "exponential", params = list(mean = 3.6)), asu_arrival_other = list(class_name = "exponential", params = list(mean = 3.2)), rehab_arrival_stroke = list(class_name = "exponential", params = list(mean = 3)), rehab_arrival_neuro = list(class_name = "exponential", params = list(mean = 31.7)), rehab_arrival_other = list(
+    class_name = "exponential", params = list(mean = 28.6)), asu_los_stroke_noesd = list(class_name = "lognormal", params = list(mean = 7.4, sd = 8.61)), asu_los_stroke_esd = list(class_name = "lognormal", params = list(mean = 4.6, sd = 4.8)), asu_los_stroke_mortality = list(class_name = "lognormal", params = list(mean = 7, sd = 8.7)), asu_los_tia = list(class_name = "lognormal", params = list(mean = 1.8, sd = 2.3)), asu_los_neuro = list(class_name = "lognormal", params = list(mean = 4, sd = 5)), 
+    asu_los_other = list(class_name = "lognormal", params = list(mean = 3.8, sd = 5.2)), rehab_los_stroke_noesd = list(class_name = "lognormal", params = list(mean = 28.4, sd = 27.2)), rehab_los_stroke_esd = list(class_name = "lognormal", params = list(mean = 30.3, sd = 23.1)), rehab_los_tia = list(class_name = "lognormal", params = list(mean = 18.7, sd = 23.5)), rehab_los_neuro = list(class_name = "lognormal", params = list(mean = 27.6, sd = 28.4)), rehab_los_other = list(class_name = "lognormal", 
+        params = list(mean = 16.1, sd = 14.1)), asu_routing_stroke = list(class_name = "discrete", params = list(values = 1:3, prob = list(0.24, 0.13, 0.63))), asu_routing_tia = list(class_name = "discrete", params = list(values = 1:3, prob = list(0.01, 0.01, 0.98))), asu_routing_neuro = list(class_name = "discrete", params = list(values = 1:3, prob = list(0.11, 0.05, 0.84))), asu_routing_other = list(class_name = "discrete", params = list(values = 1:3, prob = list(0.05, 0.1, 0.85))), rehab_routing_stroke = list(
+        class_name = "discrete", params = list(values = 2:3, prob = list(0.4, 0.6))), rehab_routing_tia = list(class_name = "discrete", params = list(values = 2:3, prob = list(0, 1))), rehab_routing_neuro = list(class_name = "discrete", params = list(values = 2:3, prob = list(0.09, 0.91))), rehab_routing_other = list(class_name = "discrete", params = list(values = 2:3, prob = list(0.13, 0.88))))
 Log:
 0.865903: asu_other0: 🚶 Arrived at ASU
 0.865903: asu_other0: 🎯 Planned ASU -> 3 (other)
@@ -35,7 +67,7 @@ Log:
 4.16715: asu_tia1: ⏳ ASU length of stay: 0.2
 4.18468: asu_other1: 🏁 ASU stay completed
 4.19181: rehab_stroke0: 🚶 Arrived at rehab
-4.19181: rehab_stroke0: 🎯 Planned rehab -> 1 (esd)
+4.19181: rehab_stroke0: 🎯 Planned rehab -> 2 (esd)
 4.19181: rehab_stroke0: ⏳ Rehab length of stay: 10.714
 4.36704: asu_tia1: 🏁 ASU stay completed
 4.70094: asu_stroke1: 🚶 Arrived at ASU
@@ -59,7 +91,7 @@ Log:
 7.12946: asu_stroke4: 🎯 Planned ASU -> 3 (other)
 7.12946: asu_stroke4: ⏳ ASU length of stay: 12.365
 7.29875: rehab_stroke1: 🚶 Arrived at rehab
-7.29875: rehab_stroke1: 🎯 Planned rehab -> 2 (other)
+7.29875: rehab_stroke1: 🎯 Planned rehab -> 3 (other)
 7.29875: rehab_stroke1: ⏳ Rehab length of stay: 34.847
 7.77172: asu_other3: 🚶 Arrived at ASU
 7.77172: asu_other3: 🎯 Planned ASU -> 3 (other)

rmarkdown/analysis.Rmd

@@ -67,7 +67,7 @@ cores <- 9L
 ### Run the model
 
 ```{r}
-param <- create_parameters(cores = cores)
+param <- parameters(cores = cores)
 base_results <- runner(param = param)
 ```
 
@@ -249,13 +249,19 @@ plot_delay_prob(
 **5% more admissions.** A 5% increase in admissions across all patient subgroups.
 
 ```{r}
-s1_param <- create_parameters(
-  asu_arrivals = lapply(create_asu_arrivals(), FUN = function(x) x * 0.95),
-  rehab_arrivals = lapply(create_rehab_arrivals(), FUN = function(x) x * 0.95),
-  cores = cores
-)
-print(s1_param$asu_arrivals)
-print(s1_param$rehab_arrivals)
+s1_param <- parameters(cores = cores)
+for (key in names(s1_param$dist_config)) {
+  if (grepl("arrival", key)) {
+    s1_param$dist_config[[key]]$params$mean <- (
+      s1_param$dist_config[[key]]$params$mean * 0.95
+    )
+  }
+}
+# View an example
+print("Before:")
+print(unlist(param$dist_config$asu_arrival_stroke))
+print("After:")
+print(unlist(s1_param$dist_config$asu_arrival_stroke))
 ```
 
 ```{r}
@@ -371,11 +377,15 @@ write.csv(adj_full_tab_2, file.path(output_dir, "table2.csv"))
 
 ```{r}
 # Set IAT very high, essentially meaning that we have no neuro arrivals
-s4_param <- create_parameters(
-  asu_arrivals = create_asu_arrivals(neuro = 10000000000),
-  rehab_arrivals = create_rehab_arrivals(neuro = 10000000000),
-  cores = 9L
-)
+s4_param <- parameters(cores = 9L)
+s4_param$dist_config$asu_arrival_neuro$params$mean = 10000000000
+s4_param$dist_config$rehab_arrival_neuro$params$mean = 10000000000
+
+# View an example
+print("Before:")
+print(unlist(param$dist_config$asu_arrival_neuro))
+print("After:")
+print(unlist(s4_param$dist_config$asu_arrival_neuro))
 ```
 
 ```{r}

rmarkdown/analysis.md

@@ -1,7 +1,7 @@
 Analysis
 ================
 Amy Heather
-2025-07-11
+2025-08-11
 
 - [Set-up](#set-up)
 - [Base case](#base-case)
@@ -58,7 +58,8 @@ last in the plotting function).
 
 Install the latest version of the local simulation package. If running
 sequentially, `devtools::load_all()` is sufficient. If running in
-parallel, you must use `devtools::install()` and then `library(simulation)`.
+parallel, you must use `devtools::install()` and then
+`library(simulation)`.
 
 ``` r
 devtools::install(upgrade = "never")
@@ -75,11 +76,12 @@ devtools::install(upgrade = "never")
     ##   ─  building ‘simulation_0.1.0.tar.gz’
     ##      
     ## Running /opt/R/4.4.1/lib/R/bin/R CMD INSTALL \
-    ##   /tmp/Rtmpz9aafi/simulation_0.1.0.tar.gz --install-tests 
+    ##   /tmp/RtmpLULkF1/simulation_0.1.0.tar.gz --install-tests 
     ## * installing to library ‘/home/amy/.cache/R/renv/library/rdesrap_stroke-34041c45/linux-ubuntu-noble/R-4.4/x86_64-pc-linux-gnu’
     ## * installing *source* package ‘simulation’ ...
     ## ** using staged installation
     ## ** R
+    ## ** inst
     ## ** tests
     ## ** byte-compile and prepare package for lazy loading
     ## ** help
@@ -128,7 +130,7 @@ cores <- 9L
 ### Run the model
 
 ``` r
-param <- create_parameters(cores = cores)
+param <- parameters(cores = cores)
 base_results <- runner(param = param)
 ```
 
@@ -337,38 +339,39 @@ plot_delay_prob(
 subgroups.
 
 ``` r
-s1_param <- create_parameters(
-  asu_arrivals = lapply(create_asu_arrivals(), FUN = function(x) x * 0.95),
-  rehab_arrivals = lapply(create_rehab_arrivals(), FUN = function(x) x * 0.95),
-  cores = cores
-)
-print(s1_param$asu_arrivals)
+s1_param <- parameters(cores = cores)
+for (key in names(s1_param$dist_config)) {
+  if (grepl("arrival", key)) {
+    s1_param$dist_config[[key]]$params$mean <- (
+      s1_param$dist_config[[key]]$params$mean * 0.95
+    )
+  }
+}
+# View an example
+print("Before:")
 ```
 
-    ## $stroke
-    ## [1] 1.14
-    ## 
-    ## $tia
-    ## [1] 8.835
-    ## 
-    ## $neuro
-    ## [1] 3.42
-    ## 
-    ## $other
-    ## [1] 3.04
+    ## [1] "Before:"
 
 ``` r
-print(s1_param$rehab_arrivals)
+print(unlist(param$dist_config$asu_arrival_stroke))
 ```
 
-    ## $stroke
-    ## [1] 20.71
-    ## 
-    ## $neuro
-    ## [1] 30.115
-    ## 
-    ## $other
-    ## [1] 27.17
+    ##    class_name   params.mean 
+    ## "exponential"         "1.2"
+
+``` r
+print("After:")
+```
+
+    ## [1] "After:"
+
+``` r
+print(unlist(s1_param$dist_config$asu_arrival_stroke))
+```
+
+    ##    class_name   params.mean 
+    ## "exponential"        "1.14"
 
 ``` r
 s1_results <- runner(param = s1_param)
@@ -556,13 +559,36 @@ requirements.
 
 ``` r
 # Set IAT very high, essentially meaning that we have no neuro arrivals
-s4_param <- create_parameters(
-  asu_arrivals = create_asu_arrivals(neuro = 10000000000),
-  rehab_arrivals = create_rehab_arrivals(neuro = 10000000000),
-  cores = 9L
-)
+s4_param <- parameters(cores = 9L)
+s4_param$dist_config$asu_arrival_neuro$params$mean = 10000000000
+s4_param$dist_config$rehab_arrival_neuro$params$mean = 10000000000
+
+# View an example
+print("Before:")
+```
+
+    ## [1] "Before:"
+
+``` r
+print(unlist(param$dist_config$asu_arrival_neuro))
+```
+
+    ##    class_name   params.mean 
+    ## "exponential"         "3.6"
+
+``` r
+print("After:")
+```
+
+    ## [1] "After:"
+
+``` r
+print(unlist(s4_param$dist_config$asu_arrival_neuro))
 ```
 
+    ##    class_name   params.mean 
+    ## "exponential"       "1e+10"
+
 ``` r
 s4_results <- runner(param = s4_param)
 ```
@@ -1049,4 +1075,4 @@ seconds <- as.integer(runtime %% 60L)
 cat(sprintf("Notebook run time: %dm %ds", minutes, seconds))
 ```
 
-    ## Notebook run time: 2m 33s
+    ## Notebook run time: 1m 33s

rmarkdown/generate_exp_results.Rmd

@@ -45,7 +45,7 @@ testdata_dir <- file.path("..", "tests", "testthat", "testdata")
 ## Base case
 
 ```{r}
-param <- create_parameters(cores = 1L, number_of_runs = 2L)
+param <- parameters(cores = 1L, number_of_runs = 2L)
 print(param)
 ```