docs: add performance metric example, table of contents

README.Rmd

@@ -1,6 +1,9 @@
 ---
 title: "README"
-output: github_document
+output: 
+  github_document:
+    toc: true
+    toc_depth: 2
 ---
 
 <!-- README.md is generated from README.Rmd. Please edit README.Rmd -->
@@ -27,11 +30,12 @@ master:
 
 **************
 
-## This is a repository for the Stock Assessment Tool: SSMSE
+**This is a repository for the Stock Assessment Tool: SSMSE**
+
 - Supported by the NOAA Fisheries Integrated Toolbox
 
 
-## Disclaimer
+**Disclaimer**
 
 “The United States Department of Commerce (DOC) GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.”
 
@@ -69,7 +73,7 @@ Still having trouble installing SSMSE? Please don't hesitate to open an [issue](
 
 ## An SSMSE example
 
-Suppose we want to look at 2 scenarios, one where Steepness (H) is specified correctly and one  where it is specified incorrectly in an estimation model (EM):
+Suppose we want to look at how well we are able to achieve a specified management procedure under uncertainty in the operating model. We will look 2 scenarios, one where Steepness (H) is specified correctly and one  where it is specified incorrectly in an estimation model (EM):
 
 1. **H-ctl**: Cod operating model (H = 0.65) with correctly specified cod model EM (fixed H = 0.65)
 2. **H-1**: Cod operating model (OM; H = 1) with misspecified cod model EM (fixed H = 0.65)
@@ -200,6 +204,10 @@ sample_struct_list <- list("H-ctl" = sample_struct, "H-1" = sample_struct)
 
 Process error can be added through the recruitment deviations. In this case, `rec_dev_pattern = "rand"` in the call to `run_SSMSE` is used to use random recruitment deviations with the same standard deviation as the historical recruitment deviation pattern. Set `scope = 2` so that the same recruitment deviation patterns are used across scenarios, but different patterns are use across iterations in the same scenario.
 
+### Performance metrics
+
+Quantitative performance metrics should be specified before conducting an MSE. Typically, a suite of performance metrics will be examined; however, for simplicity in this example, we will only look at what the achieved relative biomass was for the last 3 years of projection in the MSE to determine how it compares to the intended management target of 40% of unfished Spawning Stock Biomass. Note that we are only running our MSE projectsion for 6 years, but longer projections are typical in MSE analyses.
+
 ### Run SSMSE
 
 Now, use `run_SSMSE` to run the MSE analysis loop (note this will take some time to run, ~ 20 min):
@@ -226,7 +234,7 @@ run_SSMSE(scen_name_vec = c("H-ctl", "H-1"),# name of the scenario
 ```
 Here, only 5 iterations are run per scenario; However, in a real MSE analysis, running 100+ iterations would be preferred, so that the full range of uncertainty (given observation and process errors) is visible in the results.
 
-### Summarize results and view output
+### Summarize results
 
 The function `SSMSE_summary_all` can be used to summarize the model results in a list of dataframes.
 
@@ -268,6 +276,43 @@ ggplot2::ggplot(data = subset(summary$ts, model_run %in% c("cod_OM", "cod-1_OM",
                 ggplot2::theme_classic()
 ```
 
+Now, calculate and plot the performance metric.
+```{r plot_rel_SSB}
+get_rel_SSB_avg <- function(summary, min_yr, max_yr) {
+  OM_vals <- unique(summary$ts$model_run)
+  OM_vals <- grep("_OM$", OM_vals, value = TRUE)
+  B_unfished <- summary$scalar %>% 
+                 filter(model_run %in% OM_vals) %>% 
+                 select(iteration, scenario,SSB_Unfished)
+  SSB_yr <- summary$ts %>% 
+              filter(year >= min_yr & year <= max_yr) %>% 
+              select(iteration, scenario, year, SpawnBio)
+  SSB_yr <- left_join(SSB_yr, B_unfished) %>% 
+              mutate(Rel_SSB = SpawnBio/SSB_Unfished) %>% 
+              group_by(iteration, scenario) %>% 
+              summarize(avg_SSB = mean(Rel_SSB), .groups = "keep") %>% 
+              ungroup()
+  SSB_yr
+}
+rel_SSB <- get_rel_SSB_avg(summary, min_yr = 104, max_yr = 106)
+
+# function to summarize data in plot
+data_summary <- function(x) {
+  m <- mean(x)
+  ymin <- m-sd(x)
+  ymax <- m+sd(x)
+  return(c(y=m,ymin=ymin,ymax=ymax))
+}
+ggplot(data = rel_SSB, aes(x = scenario, y = avg_SSB)) +
+  geom_hline(yintercept = 0.4, color = "gray") +
+  stat_summary(fun.data = data_summary, 
+               position = position_dodge(width = 0.9), color = "blue") +
+  scale_y_continuous(limits = c(0, 0.8)) +
+  labs(title = "Long-term average relative SSB\n(years 101-106)", 
+       x = "Scenario", y = "SSB/SSB_unfished") +
+  theme_classic()
+```
+
 If you wish to delete the files created from this example, you can use:
 ```{r, eval = FALSE}
 unlink(run_SSMSE_dir, recursive = TRUE)

README.md

@@ -1,6 +1,16 @@
 README
 ================
 
+  - [(SSMSE) Management Strategy Evaluation for Stock Synthesis
+    (SS)](#ssmse-management-strategy-evaluation-for-stock-synthesis-ss)
+      - [Installing the SSMSE R
+        package](#installing-the-ssmse-r-package)
+      - [Troubleshooting Installation](#troubleshooting-installation)
+      - [An SSMSE example](#an-ssmse-example)
+      - [How can I contribute to SSMSE?](#how-can-i-contribute-to-ssmse)
+      - [Roadmap: Where is SSMSE headed
+        next?](#roadmap-where-is-ssmse-headed-next)
+
 <!-- README.md is generated from README.Rmd. Please edit README.Rmd -->
 
 # (SSMSE) Management Strategy Evaluation for Stock Synthesis (SS)
@@ -17,11 +27,11 @@ status](https://ci.appveyor.com/api/projects/status/github/nmfs-fish-tools/SSMSE
 
 -----
 
-## This is a repository for the Stock Assessment Tool: SSMSE
+**This is a repository for the Stock Assessment Tool: SSMSE**
 
   - Supported by the NOAA Fisheries Integrated Toolbox
 
-## Disclaimer
+**Disclaimer**
 
 “The United States Department of Commerce (DOC) GitHub project code is
 provided on an ‘as is’ basis and the user assumes responsibility for its
@@ -90,9 +100,10 @@ Still having trouble installing SSMSE? Please don’t hesitate to open an
 
 ## An SSMSE example
 
-Suppose we want to look at 2 scenarios, one where Steepness (H) is
-specified correctly and one where it is specified incorrectly in an
-estimation model (EM):
+Suppose we want to look at how well we are able to achieve a specified
+management procedure under uncertainty in the operating model. We will
+look 2 scenarios, one where Steepness (H) is specified correctly and one
+where it is specified incorrectly in an estimation model (EM):
 
 1.  **H-ctl**: Cod operating model (H = 0.65) with correctly specified
     cod model EM (fixed H = 0.65)
@@ -338,6 +349,17 @@ the historical recruitment deviation pattern. Set `scope = 2` so that
 the same recruitment deviation patterns are used across scenarios, but
 different patterns are use across iterations in the same scenario.
 
+### Performance metrics
+
+Quantitative performance metrics should be specified before conducting
+an MSE. Typically, a suite of performance metrics will be examined;
+however, for simplicity in this example, we will only look at what the
+achieved relative biomass was for the last 3 years of projection in the
+MSE to determine how it compares to the intended management target of
+40% of unfished Spawning Stock Biomass. Note that we are only running
+our MSE projectsion for 6 years, but longer projections are typical in
+MSE analyses.
+
 ### Run SSMSE
 
 Now, use `run_SSMSE` to run the MSE analysis loop (note this will take
@@ -369,7 +391,7 @@ analysis, running 100+ iterations would be preferred, so that the full
 range of uncertainty (given observation and process errors) is visible
 in the results.
 
-### Summarize results and view output
+### Summarize results
 
 The function `SSMSE_summary_all` can be used to summarize the model
 results in a list of dataframes.
@@ -446,6 +468,47 @@ ggplot2::ggplot(data = subset(summary$ts, model_run %in% c("cod_OM", "cod-1_OM",
 
 ![](man/figures/README-plot_SSB-1.png)<!-- -->
 
+Now, calculate and plot the performance metric.
+
+``` r
+get_rel_SSB_avg <- function(summary, min_yr, max_yr) {
+  OM_vals <- unique(summary$ts$model_run)
+  OM_vals <- grep("_OM$", OM_vals, value = TRUE)
+  B_unfished <- summary$scalar %>% 
+                 filter(model_run %in% OM_vals) %>% 
+                 select(iteration, scenario,SSB_Unfished)
+  SSB_yr <- summary$ts %>% 
+              filter(year >= min_yr & year <= max_yr) %>% 
+              select(iteration, scenario, year, SpawnBio)
+  SSB_yr <- left_join(SSB_yr, B_unfished) %>% 
+              mutate(Rel_SSB = SpawnBio/SSB_Unfished) %>% 
+              group_by(iteration, scenario) %>% 
+              summarize(avg_SSB = mean(Rel_SSB), .groups = "keep") %>% 
+              ungroup()
+  SSB_yr
+}
+rel_SSB <- get_rel_SSB_avg(summary, min_yr = 104, max_yr = 106)
+## Joining, by = c("iteration", "scenario")
+
+# function to summarize data in plot
+data_summary <- function(x) {
+  m <- mean(x)
+  ymin <- m-sd(x)
+  ymax <- m+sd(x)
+  return(c(y=m,ymin=ymin,ymax=ymax))
+}
+ggplot(data = rel_SSB, aes(x = scenario, y = avg_SSB)) +
+  geom_hline(yintercept = 0.4, color = "gray") +
+  stat_summary(fun.data = data_summary, 
+               position = position_dodge(width = 0.9), color = "blue") +
+  scale_y_continuous(limits = c(0, 0.8)) +
+  labs(title = "Long-term average relative SSB\n(years 101-106)", 
+       x = "Scenario", y = "SSB/SSB_unfished") +
+  theme_classic()
+```
+
+![](man/figures/README-plot_rel_SSB-1.png)<!-- -->
+
 If you wish to delete the files created from this example, you can use:
 
 ``` r