r4ss-intro-vignette.Rmd

---
title: "Introduction to r4ss"
author: "Ian G. Taylor and Kathryn L. Doering"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Introduction to r4ss}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
library(r4ss)
```

**r4ss** is an R package containing functions related to the 
[Stock Synthesis fisheries stock assessment modeling framework](https://vlab.noaa.gov/web/stock-synthesis). 
This vignette covers installing the package and an overview of functions.

## Installing the r4ss R package

### Basic installation
The package can be run on OS X, Windows, or Linux.
The CRAN version of r4ss is not as regularly updated and therefore may be out of
date. Instead, it is recommended to install from GitHub:

```{r, install-and-load, eval=FALSE}
# install.packages("pak") # if needed
pak::pkg_install("r4ss/r4ss")
```

### Loading the package and reading help pages

You can then load the package with:

```{r, load-package, eval=FALSE}
library("r4ss")
```

And read the help files with:

```{r, help, eval=FALSE}
?r4ss
help(package = "r4ss")
```

### Alternative versions

Although we've made an effort to maintain backward compatibility to at
least Stock Synthesis version 3.24S (from July 2013), there may be cases
where it's necessary to install either an older version of r4ss, such as
when a recent change to the package causes something to fail, or a
development version of the package that isn't in the `main` branch yet,
such as to test upcoming features. 

To install alternative versions of r4ss, provide a reference to the
`install_github`, such as  
```{r, install-older-version, eval=FALSE}
pak::pkg_install("r4ss/r4ss@1.46.1") # install r4ss version 1.46.1
```
where the `ref` input can be a release number, the name of a branch on
GitHub, or a git SHA-1 code, which are [listed with all code changes
committed](https://github.com/r4ss/r4ss/commits/). 

## Reading model output and making default plots {#plot}

The most important two functions are `SS_output()` and `SS_plots()`, the
first for reading the output from a Stock Synthesis model and the second
for making a large set of plots illustrating that output. 

```{r, eval=FALSE, echo=TRUE, message=FALSE}
# it's useful to create a variable for the directory with the model output
mydir <- file.path(
  path.package("r4ss"),
  file.path("extdata", "simple_small")
)

# read the model output and print diagnostic messages
replist <- SS_output(
  dir = mydir,
  verbose = TRUE,
  printstats = TRUE
)

# plots the results
SS_plots(replist)
```

By default `SS_plots()` creates PNG and HTML files in a new `plots`
sub-directory in the same location as the model files. The HTML files
(example excerpt below) facilitate exploration of the png figures. The
home tab should open in a browser automatically after `SS_plots()`
creates all PNG and HTML files. 

![Illustration of the HTML view of the plots created by the `SS_plots()`
function.](r4ss_html_capture.png){ width=75% } 

### Creating select plots

`SS_plots()` runs slowly due to the large number of plots created. If
only a few plots are of interest, it is more efficient to plot only the
necessary ones. Groups of plots to generate in the call to `SS_plots()`
can be specified through the `plot` argument. For example, if only the
plots of Catch were desired, call: 

```{r, eval=FALSE}
SS_plots(replist = replist, plot = 7)
```

If only plots of catch and discards were desired, the user could call:
```{r, eval=FALSE}
SS_plots(replist = replist, plot = c(7, 9))
```
The documentation for the `plot` argument in the help file for
`SS_plots()` lists the corresponding numbers for each group of plots. 

It is not uncommon to run into bugs with the plotting functions because
of the vast number of model configurations available in SS3 and plots
created from them. A strategy with for dealing with a bug is to exclude
the set of plots where the bug is occurring as a temporary fix. In the
long term, bugs typically get attention fairly quickly from maintainers
when reported to the [r4ss issue
tracker](https://github.com/r4ss/r4ss/issues). For example, if there was
a bug in the conditional age-at-length fits (plot set 18), exclude the
plot: 

```{r, eval=FALSE}
SS_plots(replist, plot = c(1:17, 19:26))
```

## Scripting Stock Synthesis workflows with `r4ss`

Using functions in `r4ss`, a fully scripted workflow for modifying Stock
Synthesis files and running Stock Synthesis models is possible. 

We'll demonstrate this by creating a new model from a model in the
`r4ss` package.

```{r}
# initial model to modify
mod_path <- system.file(file.path("extdata", "simple_small"), package = "r4ss")
# create a new directory to put a new, modified version of the model
new_mod_path <- "simple_new"
```
Use the r4ss utility function to copy over the model files from
`mod_path` to `new_mod_path`:

```{r}
copy_SS_inputs(dir.old = mod_path, dir.new = new_mod_path)
```

Note that the function `populate_multiple_folders()` can be used to copy
several folders of Stock Synthesis model inputs.

### Read in Stock Synthesis files

Stock Synthesis files can be read in as list objects in R using the
`SS_read()` function.

```{r, eval=TRUE}
inputs <- r4ss::SS_read(dir = new_mod_path)
# can also separately run the functions called by SS_read():
# SS_readstarter(), SS_readdat(), SS_readctl(), SS_readforecast(),
# and SS_readwtatage()
```

### Investigate the model

Each of the input files is read into R as a list which are then grouped
as a larger list. The components of the
list should be in the same order as they appear in the text file. Use
`names()` to see all the list components:

```{r}
names(inputs) # see the elements of the big list
names(inputs$start) # see names of the list components of starter file
```
Or reference a specific element to see the components. For example, we
can look at the mortality and growth parameter section (MG_parms):

```{r}
inputs$ctl$MG_parms
```

### Modify the model
You could make basic or large structural changes to your model in R. For
example, the initial value of M can be changed:

```{r}
# view the initial value
inputs$ctl$MG_parms["NatM_p_1_Fem_GP_1", "INIT"]
# change it to 0.2
inputs$ctl$MG_parms["NatM_p_1_Fem_GP_1", "INIT"] <- 0.2
```

When making large structural changes, additional elements may need to be
added that were NULL before. To find out the names in the r4ss list
object, it may be necessary to make changes directly to the input files
and then read it in again to R, or to look at the source code for the
names of the list elements. For example, the source code for
`SS_readctl()` when using a SS3.30 file is located at
[https://github.com/r4ss/r4ss/blob/main/R/SS_readctl_3.30.R](https://github.com/r4ss/r4ss/blob/main/R/SS_readctl_3.30.R).

Settings in other files can also be modified. For example, the biomass
target can be modified in the forecast file
```{r}
inputs$fore$Btarget
inputs$fore$Btarget <- 0.45
inputs$fore$Btarget
```

### Write out the modified models

The `SS_write()` function can be used to write out the modified stock
synthesis input R objects into input files:

```{r}
r4ss::SS_write(inputs, dir = new_mod_path, overwrite = TRUE)
```

If you make changes to the input model files that render the file
unparsable by Stock Synthesis, the `SS_write()` function may throw an
error (and hopefully provide an informative message about why). However,
it is possible that an invalid Stock Synthesis model file could be
written, so the true test is whether or not it is possible to run Stock
Synthesis with the modified model files.

If you need help troubleshooting `SS_read()` or `SS_write()` and the
associated functions for each model file, or would like to report a bug,
please [post an issue in the r4ss repository](https://github.com/r4ss/r4ss/issues).

### Download the Stock Synthesis executable from GitHub
The [latest release of the Stock Synthesis executable](https://github.com/nmfs-ost/ss3-source-code/releases/latest) 
or other releases found by entering a character string of a version tag (list of 
tags is available [here](https://github.com/nmfs-ost/ss3-source-code/tags)) 
can be downloaded from the Stock Synthesis GitHub page using the function:
```{r, eval = FALSE}
# Default with no version downloads the latest release
r4ss::get_ss3_exe()

# Download the latest release to a specific directory
r4ss::get_ss3_exe(dir = new_mod_path)

# Adding a character string for a specific version using the GitHub tag
r4ss::get_ss3_exe(dir = new_mod_path, version = "v3.30.18")
```

You can also use the function without a specified directory which will download 
the executable to your working directory. This function downloads the correct 
executable according to information it gets about your operating system.

### Run the modified model

The model can now be run with Stock Synthesis. The call to do this
depends on where the Stock Synthesis executable is on your computer. If
the Stock Synthesis executable is in the same folder as the model that
will be run, `run()` can be used. Assuming the stock synthesis executable
is called ss.exe:

```{r exe-in-same-dir, eval=FALSE}
r4ss::run(dir = new_mod_path, skipfinished = FALSE)
```

Note this is similar to resetting the working directory and running the
model with `system()` or `shell()`, but deals with differences among
operating systems automatically. Another advantage of `run()` is that 
there is no need to change the working directory.

If the executable in a different folder than the model, specify either
the absolute or relative path to the executable. Note that executables for 
v3.30.22.1 and after are named ss3.exe, ss3_linux, and ss3_osx *unless* you 
download the executables using `get_ss3_exe()` which gives them the names 
ss3.exe (for windows) and ss3 (for linux/osx) regardless of the version. 
```{r exe-in-diff-dir, eval=FALSE}
# use the absolute exe path in the call on a Windows computer.
run(dir = new_mod_path, exe = "c:/SS/SSv3.30.19.01_Apr15/ss.exe")
# use the absolute exe path in the call on linux.
run(dir = new_mod_path, exe = "~/SS/SSv3.30.19.01_Apr15/ss_linux")
```

Finally, if the stock synthesis executable is in your PATH, then
`run()` should find it automatically.

### Investigate the model run

As [previously](#plot), `SS_output()` and `SS_plots()` can be used to
investigate the model results.

### Should I script my whole Stock Synthesis workflow?

Scripting using r4ss functions is one way of developing a reproducible
and coherent Stock Synthesis development workflow. However, there are
many ways that Stock Synthesis models could be run and modified. What is
most important is that you find a workflow that works for you and that
you are able to document changes being made to a model. Version control
(such as [git](https://git-scm.com/)) is another tool that may help
document changes to models.

## Functions for common stock assessment tasks

While stock assessment processes differ among regions, some modeling
workflows and diagnostics are commonly used. Within r4ss, there are
functions to perform a retrospective (`retro()`), jitter the
starting values and reoptimize the stock assessment model a number of
times to check for local minima (`jitter()`) and tuning
composition data (`tune_comps()`).

Additional model diagnostics for Stock Synthesis models are available as
part of the [ss3diags](https://github.com/jabbamodel/ss3diags) package.

### Running retrospectives

A retrospective analysis removes a certain number of years of the model
data and recalculates the fit. This is typically done several times and
the results are used to look for retrospective patterns (i.e.,
non-random deviations in estimated parameters or derived quantities as
years of data are removed). If the model results change drastically and
non-randomly as data is removed, this is less support for the model. For
more on the theory and details behind retrospective analyses, see
[Hurtado-Ferro et al. 2015](https://doi.org/10.1093/icesjms/fsu198) and
[Legault 2020](https://doi.org/10.1093/icesjms/fsaa184).

The function `retro()` can be used to run retrospective analyses
starting from an existing Stock Synthesis model. Note that it is safest
to create a copy of your original Stock Synthesis model that the
retrospective is run on, just in case there are problems with the run.
For example, a five year retrospective could be done:

```{r, echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE, results=FALSE}
# create a temporary path for the retrospective analyses to run and download the
# ss3 exe
old_mod_path <- system.file(file.path("extdata", "simple_small"), package = "r4ss")
new_mod_path <- tempdir()
all_files <- list.files(old_mod_path, full.names = TRUE)
file.copy(from = all_files, to = new_mod_path)
get_ss3_exe(dir = new_mod_path)
```


```{r, eval=TRUE, message=FALSE, warning=FALSE}
# run the retrospective analyses
retro(
  dir = new_mod_path, # wherever the model files are
  oldsubdir = "", # subfolder within dir
  newsubdir = "retrospectives", # new place to store retro runs within dir
  years = 0:-5, # years relative to ending year of model
  exe = "ss3"
)
```
After running this retrospective, six new folders would be created
within a new "retrospectives" directory, where each folder would contain 
a different run of the retrospective (removing 0 to 5 years of data).

After the retrospective models have run, the results can be used as a
diagnostic:

```{r, eval=TRUE, warning=FALSE, message=FALSE, out.width="75%"}
# load the 6 models
retroModels <- SSgetoutput(dirvec = file.path(
  new_mod_path, "retrospectives",
  paste("retro", 0:-5, sep = "")
))
# summarize the model results
retroSummary <- SSsummarize(retroModels)
# create a vector of the ending year of the retrospectives
endyrvec <- retroSummary[["endyrs"]] + 0:-5
# make plots comparing the 6 models
# showing 2 out of the 17 plots done by SSplotComparisons
SSplotComparisons(retroSummary,
  endyrvec = endyrvec,
  legendlabels = paste("Data", 0:-5, "years"),
  subplot = 2, # only show one plot in vignette
  print = TRUE, # send plots to PNG file
  plot = FALSE, # don't plot to default graphics device
  plotdir = new_mod_path
)
```

```{r, eval = FALSE, echo = TRUE}
knitr::include_graphics(file.path(new_mod_path, "compare2_spawnbio_uncertainty.png"))
```

```{r, echo = FALSE, eval = FALSE}
fls <- list.files(new_mod_path, pattern = "*.png", full.name = TRUE)
to <- here::here("vignettes")
file.copy(fls, to = to)
```

![Illustration of the second comparison plot created by the `SSplotComparisons()`
function.](compare2_spawnbio_uncertainty.png){ width=75% } 

```{r, eval = FALSE}
# calculate Mohn's rho, a diagnostic value
rho_output <- SSmohnsrho(
  summaryoutput = retroSummary,
  endyrvec = endyrvec,
  startyr = retroSummary[["endyrs"]] - 5,
  verbose = FALSE
)
```

### Jittering

Another commonly used diagnostic with Stock Synthesis models is
"jittering". Model initial values are changed randomly (by some
fraction in a transformed parameter space) and the model is reoptimized. 
The `jitter()` function performs this routine for the number of times 
specified by the user. For a stock Synthesis model in a folder called 
`jitter_dir` jittering starting values can be run 100 times (note this
could take a while as they will be run in sequence):

```{r, eval=FALSE}
# define a new directory
jitter_dir <- file.path(mod_path, "jitter")
# copy over the stock synthesis model files to the new directory
copy_SS_inputs(dir.old = mod_path, dir.new = jitter_dir)
# run the jitters
jitter_loglike <- jitter(
  dir = jitter_dir,
  Njitter = 100,
  jitter_fraction = 0.1 # a typically used jitter fraction
)
```
The output from `jitter()` is saved in `jitter_loglike`, which is
a table of the different negative log likelihoods produced from
jittering. If there are any negative log likelihoods smaller than the
original model's log likelihood, this indicates that the original
model's log likelihood is a local minimum and not the global minimum. On
the other hand, if there are no log likelihoods lower than the original
model's log likelihood, then this is evidence (but not proof) that the
original model's negative log likelihood could be the global minimum.

Jittering starting values can also provide evidence about the
sensitivity of the model to starting values. If many different
likelihood values are arrived at during the jitter analysis, then the
model is sensitive to starting values. However, if many of the models
converge to the same negative log likelihood value, this indicates the
model is less sensitive to starting values.

### Tuning composition data

Three different routines are available to tune (or weight) composition
data in Stock Synthesis. The McAllister-Ianelli (MI) and Francis tuning
methods are iterative reweighting routines, while the
Dirichlet-multinomial (DM) option incorporates weighting parameters
directly in the original model.

Because tuning is commonly used with Stock Synthesis models, and users
may be interested in exploring the same model, but  using different
tuning methods, `tune_comps()` can start from the same model and
transform it into different tuning methods.

As an example, we will illustrate how to run Francis tuning on an
example Stock Synthesis model built into the r4ss package. First, we
make a copy of the model to avoid changing the original model files 
```{r}
# define a new directory in a temporary location
mod_path <- file.path(tempdir(), "simple_mod")
# Path to simple model in r4ss and copy files to mod_path
example_path <- system.file("extdata", "simple_small", package = "r4ss")
# copy model input files
copy_SS_inputs(dir.old = example_path, dir.new = mod_path)
# copy over the Report file to provide information about the last run
file.copy(
  from = file.path(example_path, "Report.sso"),
  to = file.path(mod_path, "Report.sso")
)
# copy comp report file  to provide information about the last run of this model
file.copy(
  from = file.path(example_path, "CompReport.sso"),
  to = file.path(mod_path, "CompReport.sso")
)
```

The following call to `tune_comps()` runs Francis weighting for 1
iteration and allows upweighting. Assume that an executable called
"ss or ss.exe" is available in the mod_path folder.
 
```{r, eval=FALSE}
tune_info <- tune_comps(
  option = "Francis",
  niters_tuning = 1,
  dir = mod_path,
  allow_up_tuning = TRUE,
  verbose = FALSE
)
# see the tuning table, and the weights applied to the model.
tune_info
```

Now, suppose we wanted to run the same model, but using
Dirichlet-multinomial parameters to weight. The model can be copied over
to a new folder, then the `tune_comps()` function could be used to
add Dirichlet-multinomial parameters (1 for each fleet with composition
data and for each type of composition data) and re-run the model.

```{r, eval=FALSE}
# create additional temporary directory
mod_path_dm <- file.path(tempdir(), "simple_mod_dm")
# copy model files
copy_SS_inputs(dir.old = mod_path, dir.new = mod_path_dm, copy_exe = TRUE)
# copy over the Report file to provide information about the last run
file.copy(
  from = file.path(mod_path, "Report.sso"),
  to = file.path(mod_path_dm, "Report.sso")
)
# copy comp report file  to provide information about the last run of this model
file.copy(
  from = file.path(mod_path, "CompReport.sso"),
  to = file.path(mod_path_dm, "CompReport.sso")
)
# Add Dirichlet-multinomial parameters and rerun. The function will
# automatically remove the MI weighting and add in the DM parameters.
DM_parm_info <- tune_comps(
  option = "DM",
  niters_tuning = 1, # must be 1 or greater to run, through DM is not iterative
  dir = mod_path_dm
)
# see the DM parameter estimates
DM_parm_info[["tuning_table_list"]]
```

There are many options in the `tune_comps()` function; please see the
documentation (`?tune_comps` in the R console) for more details and
examples.