From 7bdb2e0a3a88206ff7e53f6682990d5f3680292a Mon Sep 17 00:00:00 2001 From: e-perl-NOAA Date: Thu, 5 Dec 2024 21:57:17 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20nmfs-ost?= =?UTF-8?q?/ss3-doc@a283495084455f86698787485307fe3613bbae71=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Getting_Started_SS3.html | 419 --------------------------------- Getting_Started_SS3.pdf | Bin 438996 -> 0 bytes SS330_User_Manual_release.html | 4 +- ss3_model_tips.html | 368 ----------------------------- ss3_model_tips.pdf | Bin 228320 -> 0 bytes 5 files changed, 2 insertions(+), 789 deletions(-) delete mode 100644 Getting_Started_SS3.html delete mode 100644 Getting_Started_SS3.pdf delete mode 100644 ss3_model_tips.html delete mode 100644 ss3_model_tips.pdf diff --git a/Getting_Started_SS3.html b/Getting_Started_SS3.html deleted file mode 100644 index badb4e5..0000000 --- a/Getting_Started_SS3.html +++ /dev/null @@ -1,419 +0,0 @@ - - - - - - - - - - - - - - -Getting Started with Stock Synthesis (SS3) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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1 Scope

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Stock Synthesis (SS3) is a statistical fisheries population dynamics modeling framework built in AD Model Builder. This Getting Started Guide introduces users to running SS3. We assume some experience with population dynamics models and a basic understanding of how to use the command line.

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If you have never used the command line, please see the resources in Appendix 1.

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To follow along, we recommend downloading the simple example (Instructions for downloading) and the latest release of SS3. Simple is a working SS3 model that runs in less than three minutes and should allow you to experiment with SS3 workflows without having to worry about the model’s contents.

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By the end of using this guide, you should:

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  1. Understand the input and output file structure for SS3
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  3. Be able to run an SS3 model via the command line
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  5. Understand which tools are available to work with SS3 models
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  7. Troubleshoot basic problems associated with running an SS3 model
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-Figure 1. SS3 input and output files, and associated tools. Note that some tools are used with both input and output model files. -

-Figure 1. SS3 input and output files, and associated tools. Note that some tools are used with both input and output model files. -

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2 Stock Synthesis file structures and tools

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SS3 uses text input files and produces text output files. In this section, the SS3 input and output files are described, as are tools that can be used with these files. See how they relate in Figure 1.

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2.1 SS3 files: Required inputs

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Four required input files are read by the SS3 executable. Throughout this document, we will refer to the SS3 executable as ss3.exe. Keep in mind that the Linux and Mac versions of SS3 have no file extension (e.g., ss3), and the executable can be renamed by the user as desired (e.g.,ss3.exe, ss_win.exe, ss_3.30.18.exe). These input files are:

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  1. starter.ss: Required file containing file names of the data file and the control file plus other run controls. Must be named starter.ss.
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  3. data file: File containing model dimensions and the data. The data file can have any name, as specified in the starter file, but typically ends in .ss or .dat.
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  5. control file: File containing set-up for the parameters. The control file can have any name, as specified in the starter file, but typically ends in .ss or .ctl.
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  7. forecast.ss: File containing specifications for reference points and forecasts. Must be named forecast.ss.
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2.2 SS3 files: Optional inputs

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The two optional input files for SS3 are:

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  1. ss3.par: (previously ss.par) A text file with one line per parameter where order matters. Usually created from a previous model run. This file can provide initial parameter values instead of using the initial values in the control file. The option to use the ss3.par file or the control file for initial parameter values is selected in starter.ss.
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  3. wtatage.ss: File containing empirical input of body weight by fleet and population and empirical fecundity-at-age. The option to use the wtatage.ss file is selected in a line of the control file.
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2.3 SS3 output files

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Many output text files are created during a model run. The most useful output files can be divided into:

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  1. Files containing results. These files include ss_summary.sso and report.sso that contain model results summarized in different ways.
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  3. .ss_new files. These files echo the SS3 input files, but include standardized comments. The values should be the same as the input files, except for control.ss_new, which updates the initial values to the final estimated values. The .ss_new files can be useful to standardize comments in the input files and can be checked to make sure that SS3 interpreted the inputs as the user intended.
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  5. Files used for debugging. These files include warnings.sso and echoinput.sso.
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2.4 Tool definitions

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  • Any text editor can be used to view and edit SS3 input and output files. Defining a custom syntax highlighting scheme for SS3 files may improve SS3 file readability. Some commonly used text editors include Atom, Emacs, Notepad++, and Sublime Text.

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  • r4ss: An R package to plot SS3 model results and manipulate SS3 input and output files. Available at: https://github.com/r4ss/r4ss

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  • SSI: Stock Synthesis Interface (i.e., the SS3 GUI). The latest version of SSI can be downloaded from GitHub. SSI can be used to edit, save, run, and visualize model inputs and outputs. Note that SSI is not maintained for Stock Synthesis versions after v.3.30.21.

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  • Stock Assessment Continuum Tool: Available through github at https://github.com/shcaba/SS-DL-tool, the Stock Assessment Continuum Tool (previously known as the Stock Synthesis Data-limited Tool) is a Shiny-based application that provides an interface to upload catch time-series, age composition, length composition, and abundance index data and define model options within the application, which then writes the Stock Synthesis input files.

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3 Running SS3

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SS3 is typically run through the command line (although it can also be run indirctly via the commandline through an R console). We will introduce the one folder approach, where SS3 is in the same folder as the model files. Other possible approaches to running SS3 include, which are detailed in the “Running Stock Synthesis” section of the user manual.

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3.1 The one folder approach and demonstration of an SS3 model run

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The one folder approach is so named because the model input files and SS3 executable are in the same folder. The one folder approach is the simplest way to run SS3 via the command line, so we will walk through an example run using this approach.

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3.1.1 Setup for the 1 folder approach

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Create a folder and add:

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  • Control File (Must match name in starter.ss)
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  • Data File (Must match name in starter.ss)
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  • forecast.ss
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  • ss3.exe
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  • Conditional files: wtatage.ss (if doing empirical wt-at-age approach) and/or ss3.par (previously ss.par; to continue from a previous run)
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For example, here is what should be included for a model with no conditional files (where the control and data file names specified in starter.ss are control.ss and data.ss, respectively):

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3.1.2 Run SS3

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Once all of the model files and the SS3 executable are in the same folder, you can open your command window of choice at the location of the model files.

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To do this, you can typically click to highlight the folder the model files are in, then shift + right click on the same folder and select the option from the menu to open the command line of choice (e.g., Windows Powershell). This should bring up a command window. Then, type ss3 (or other name of the ss3 exe) into the command prompt and hit enter. Note that if you are using Windows Powershell, you will need to type ./ss3.

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The exact instructions for running SS3 can differ depending on the command window used. If you have trouble, search for resources that describe running an executable for your specific command line.

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If you still have issues, double-check that you are calling the correct exe name (e.g., you would need to type ss_win to run an executable called ss_win.exe.) and that the command line has the correct directory.

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After starting the run, you should see output to the command line similar to:

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If the output gets past Estimating…please wait…, then the structure of the SS3 inputs is correct. The volume of information displayed during the run within the command window is controlled in starter.ss file on the line with the comment # run display detail (0,1,2). The 0 setting is the least verbose option, while the 2 setting is the most verbose.

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SS3 always opens and reads files in the same order (starter.ss, data file, forecast.ss, control, then wtatage.ss if using, and finally ss3.par if using), writing to echoinput.sso (a file for debugging runs) and warnings.sso (a file which collects warnings from SS3) as it reads. SS3 inputs are read in order by value, without reference to comments (i.e., anything after # on a line). Linebreaks and whitespace are both treated as separators. Depending on which options are selected, SS3 will expect a particular number of values in a certain order, so failing to provide them will result in errors or at least a different model specification than intended.

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After reading, SS3 proceeds immediately to pre-processing the data and creating internal parameter labels. As SS3 executes, it continues to write debugging output to echoinput.sso and warnings to warnings.sso.

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SS3 next goes to the procedure section where iterative parameter changes are made by ADMB to minimize the negative log likelihood. When ADMB achieves convergence, control passes to the sd_phase for calculation of parameter variance (unless –nohess option is specified; See command line options). After the sd_phase, SS3 proceeds to the benchmark and forecast code section and then to final reporting. Output files containing results are typically written to the directory in which the model was run from (except when using this special subfolder option).

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When the run completes, SS3 shows !! Run has completed !! and the number of warnings:

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Examine warnings.sso for issues and suggestions, regardless of if the run completed successfully or not. If SS3 exits on error, you will not see the message !! Run has completed !!. Here is an example of a run that exited on error due to invalid inputs:

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3.1.3 Examine the output

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.ss_new files are generated if a model run completes. For more information on the .ss_new files, see the “Output Files” section of the SS3 user manual.

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ss_summary.sso files are included with the examples on vlab so that you can compare it with the ss_summary.sso file generated by your model run. You should see similar if not exactly the same values for the likelihood and parameter values if using the same version of SS3.

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Output from SS3 can be read into r4ss or the excel viewer.

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4 Options for running SS3

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4.1 Command line options

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ADMB options can be added to the run when calling the SS3 executable from the command line. The most commonly used option is ss3 -nohess to skip standard errors (for quicker results or to get Report.sso if the hessian does not invert).

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To list all command line options, use one of these calls: SS3 -? or SS3 -help. More info about the ADMB command line options is available in the ADMB Manual (Chapter 12: Command line options).

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To run SS3 without estimation use: ss3 -stopph 0. This will speed up your run by not optimizing. Often -stopph 0 is used with the -nohess option to speed up the run even more. To run SS3 with no estimation in v.3.30.15 and earlier, change the max phase in the starter.ss file to 0 and run the exe with the –nohess option. Prior to version v.3.30.22.1, the par file name was ss3.par, but now the default name is ss3.par. To have SS3 create a par file with a different name, such as ss4you.par, use: ss3 modelname ss4you.

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4.2 Using ss3.par (previously ss.par) for initial values

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Typically, SS3 uses initial parameter values as specified in the parameter lines of the control file. However, initial values can be read from the ss3.par file (prior to v.3.30.22.1 this was the ss.par file) instead. To use ss3.par, in starter.ss below the names of the data and control files, set the value to 1 rather than 0 on the line with comment:

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# 0=use init values in control file; 1=use ss3.par.

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Using the ss3.par file is helpful when you want to use different initial values without modifying the control file. An ss3.par file is an output file created during each model run and contains the ending parameter values. Using this ss3.par file from a previous model run with a similar model (that still has the same parameters) will speed up run time because the initial values are likely closer to the maximum likelihood estimation (MLE) parameter estimates.

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4.3 Using wtatage.ss

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As stated in the manual, SS3 has the capability to read empirical body weight at age for the population and each fleet, in lieu of generating these weights internally from the growth parameters, weight-at-length, and size-selectivity. Please see the Empirical Weight-at-Age (wtatage.ss) section of the SS3 user manual for more information on using wtatage.ss with a model. There is also a weight at age SS3 example model available if you would like to try using this feature without having to build your own model.

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5 Plotting results and basic troubleshooting

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5.1 Using r4ss to organize and plot SS3 output

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To plot the results of an SS3 model after it has run, use:

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library(r4ss) # use r4ss package on github
-# create list of quantities for the model in the directory mydir
-replist <- SS_output(mydir)
-SS_plots(replist)# create plots for SS3 run
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See https://github.com/r4ss/r4ss for {r4ss} installation instructions. r4ss includes other functionality for working with SS3 models, see the r4ss vignette for an introduction.

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5.2 What to do when SS3 doesn’t run

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Here are some basic checks for when SS3 does not run:

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  • Make sure that all files and directories are named correctly.
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  • Check that starter.ss references the correct names of the control and data files.
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  • If SS3 starts to read files and then crashes, check warnings.sso and echoinput.sso. The warnings.sso will reveal potential issues with the model, while echoinput.sso will show how far SS3 was able to run. Work backwards from the bottom of echoinput.sso, looking for where SS3 stopped and if the inputs are being read corectly or not.
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For further information on troubleshooting, please refer to the SS3 User Manual “Running Stock Synthesis” subsections, especially “Re-Starting a Run” and “Debugging Tips”.

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6 Where to get additional help

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7 Appendix 1: An introduction to the command line

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If you have never used the command line before, it will be helpful to learn a few basics. Some potential resources include:

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  • Appendix A to the Happy Git and GitHub for the useR book, which introduces the command line on Linux, OSX, and Windows systems.
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  • Introduction to the Command Line by Launch School has even more detail about using the command line specifically on Linux (which should also apply to OSX systems)
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  • A web search on “Command Line” will reveal plenty of other resources
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Stock Synthesis User Manual
-Version 3.30.24.6

+Version 3.30.23.1

Richard D. Methot Jr., Chantel R. Wetzel, Ian G. Taylor, Kathryn L. Doering,
Elizabeth F. Perl, and Kelli F. Johnson
diff --git a/ss3_model_tips.html b/ss3_model_tips.html deleted file mode 100644 index ec2644c..0000000 --- a/ss3_model_tips.html +++ /dev/null @@ -1,368 +0,0 @@ - - - - - - - - - - - - - - -Developing your first Stock Synthesis (SS3) model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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1 Scope

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The developing your first SS3 model guide teaches users how to develop a basic Stock Synthesis model. We assume that these users have had previous population dynamics modeling experience and already understand how to run an existing SS3 model.

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If you are a new SS3 user who is not yet comfortable running an SS3 model, we suggest trying to run an example working model using advice in the Getting Started guide before attempting to develop and run your own model as outlined here.

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By the end of using this guide, you should be able to:

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  1. Develop your own SS3 model development workflow based on the tools and methods suggested
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  3. Find information on the required inputs for different model specifications
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  5. Understand how to use phases within an SS3 model
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  7. Debug basic issues with input in Stock Synthesis models and identify when there are more advanced model problems
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2 How to create new SS3 models

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There are many potential workflows for developing a new SS3 model, but a common technique is to start with an existing model and change it in a piece-wise fashion. The basic technique is:

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  1. Find an existing working model (perhaps one of the SS3 example models available in the ss3-user-examples repository or perhaps a model more similar in form to the model you plan to create).
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  3. Edit portions of the model and try running as you go to check that your inputs can be read correctly by SS3 and make logical sense (tip: use options -stopph 0 -nohess to reduce run time with each iteration of checking inputs by not estimating anything and not inverting the hessian).
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Some commonly used tools for editing the SS3 input files are:

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  1. Stock Synthesis Interface (SSI; the SS3 GUI). The SSI allows you to read in a model, performs some checks to ensure valid inputs, make modifications to the model, and offers visualizations of inputs. You can also run models from SSI. Note that SSI is not maintained for Stock Synthesis versions after v.3.30.21.
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  3. Your favorite text editor.
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  5. The SS_read* and SS_write* functions in the R package r4ss. These functions allow you to read in SS3 input files to R, manipulate them from within R, then write them out to a file. The r4ss vignette demonstrates how to use these functions
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  7. Stock Assessment Continuum Tool. Available through github at https://github.com/shcaba/SS-DL-tool.
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3 Guidance on model specification

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SS3 has a rich set of features. Some required inputs are conditional on other inputs, so it is important to be mindful that changing an option might result in SS3 expecting a different number of values in the input files than it did previously. Most of the time, these conditional inputs are read right after the option.

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The SS3 user manual can be used as a guide to help you edit your model. Conditional inputs are noted in the manual. The SSI can also help guide you through changes in model inputs required as you select different SS3 model options.

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If you are unsure if you got the setup right (e.g., adding the correct number of parameter lines for a chosen catchability setup), try running the model with maxphase = 0 in the starter file and ADMB option -nohess (or for v.3.30.16 and greater, run the model with command line options -stopph 0 -nohess, no need to change the starter file). If the model run completes, you can compare the control.ss_new file and the first data set in data.ss_new to your SS3 input files to make sure SS3 interpreted the values as intended. If the run exits before completion, you can look at warning.sso and echoinput.sso for clues as to what was wrong with your setup.

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For additional help with model specification, please post your questions in the GitHub discussions or on the vlab forums (for registered SS3 users) or send an email to the SS3 team at .

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4 Phases in SS3 and ADMB

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Phases are used within ADMB to tell the program in what order the parameters should be estimated. For more details about phases in ADMB, see the ADMB user manual. ADMB phases can be specified for parameters in SS3.

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A negative phase indicates that the parameter is not estimated, but specified. Note that all negative phases are equivalent; i.e., there is no difference in how parameters with phases -1 and -99 are dealt with. The exceptions are:

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  • -9999, which is a special value within SS3 (see the profilevalues.ss section of the SS3 user manual).
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  • Values more negative than -999 for parameters in the tagging section of the control file.
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A positive phase indicates that the parameter will be estimated. Parameters with a lower positive phase will be estimated before those with a higher positive phase. Which phase should you put an estimated parameter in? While there is variability among analysts and models in how phasing is used, in general, parameters with low positive phases should be for values which define the scale of the population (i.e., R0, natural mortality, steepness, selectivity slope). Parameters for fine-tuning fits to the data are often estimated in later phases (i.e., catchability, growth, and recruitment deviation parameters). Many models use positive phases from about 1 to 5, although it is possible to use higher phasing values.

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When a model is run without estimating anything, the phases are ignored; because there is no estimation, running without estimation is like including a negative phase for all parameters.

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5 Basic debugging: use echoinput.sso and warning.sso

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The most useful tools for debugging SS3 issues are the SS3 output files echoinput.sso and warning.sso. As SS3 runs, it writes back to echoinput.sso. When SS3 exits without finishing, looking at the last contents written to echoinput.sso and comparing it with your input files can provide clues as to where (and possibly why) the run failed. Note that SS3 reads inputs in order and will read them incorrectly if the wrong number of inputs is provided. Any warnings generated during the run are written to warning.sso. The warnings.sso file should always be examined at the end of the run, even if the run was successful, as it includes suggestions for improving the model specification and warnings about common model sepecification mistakes.

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6 Visualizing results

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Visualization is helpful as you iteratively develop your model and for presentation of final model results. The output of an SS3 model can be read into r4ss. The r4ss vignette has more information on how to use the r4ss R package.

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7 Proposed workflow for arriving at a “final” model

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It’s important to remember that arriving at a model takes time and many iterations! You should plan on making small revisions, rerunning the model, and looking at how the model has changed with each small revision. Typically, many intermediate model runs are required before you reach a final model. At a minimum, keep notes inside of the run folder or elsewhere regarding the run specific changes. Using version control could also be helpful for tracking model changes. Below, we provide some suggestions on the steps to take as you iterate toward your “final” model.

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7.1 First runs without estimation

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First, check that SS3 reads the input files as intended. Use -stopph 0 -nohess command line options to run the model without estimation. Then examine warning.sso, echoinput.sso, and the .ss_new files produced to see how SS3 interpreted your input files and if it was as you intended.

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After you have verified that SS3 is reading your input correctly, you can also consider adjustment to selectivity, growth, and recruitment parameters.

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7.2 Runs estimating some parameters

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Try changing max phase to be greater than 0 (perhaps 2 or 3) to estimate some but not all parameters. Do this by using -stopph x -nohess, where x should the maximum phase desired (e.g., 2 or 3). -nohess is still used to reduce run time. During this stage of model runs, you should try to adjust the model to resolve all major patterns in the residuals. This may include considering and making changes to the model structure.

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7.3 Runs estimating all parameters

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Once all major patterns in the residuals have been resolved, try estimating all parameters in the model. Change max phase to include all of your model phases (i.e., equal to or higher than the highest phase specified in your model). You’ll want to do multiple runs for different model refinement purposes at this time:

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  1. Do one or more run(s) with the hessian estimated to get variance of the recruitment deviations and then use this vector of variances to adjust the bias adjustment ramp (see the SS3 User Manual for additional details).
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  3. Do runs with or without estimating the hessian to tune the data weightings.
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  5. Run the model multiple times and jitter the starting values (option to jitter is selected in starter.ss). This runs the model with new starting parameter values to determine if a better model fit is possible using different starting values.
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After these runs, your model should be fine-tuned and ready for final runs.

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8 Advanced debugging

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In this section, we have listed some common problems that you may encounter and some techniques to resolving them within your model.

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8.1 If the residuals have substantial patterns

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It is often difficult to pinpoint the cause of residuals that have substantial patterns, but two potential reasons this could happen are unrealistic starting values or poor model specification. If you have residuals with substantial pattern, you can try changing the starting values. If a parameter is not moving from the starting value, there may not be enough information to determine its value and it may be necessary to fix the value in the model. If the parameter does move with different starting values, it is possible that the starting value used before was not reasonable and thus changing the starting value to a reasonable value fixes the issue.

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8.2 If SS3 runs, but there is a large gradient at the end

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Large gradients are usually reported in warnings.sso file, so this problem should be clear when examining the file after a model run. If this is the case, try the following

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  • Examine Report.sso for parameters that are on or near bounds, as these may be the parameters that are causing issues. It depends on the value, but you can consider changing your bounds or the parameters, depending on what seems most plausible given your model.
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  • Examine residuals to identify if there is overall poor lack-of-fit. This may indicate that the ADMB algorithm failed to find the global minimum. Try alternative starting values to attempt to solve this problem. However, it is also possible that there is large disagreement between your model structure and your model file, in which case you may want to reconsider the model specification - perhaps there is an alternative model structure that makes more sense for the population.
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8.3 If the hessian does not invert

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The hessian information is reported in the Report.sso file, so go to this section to confirm if this is an issue. This is a difficult issue to solve, but considering alternative models may be necessary if the hessian will not invert.

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9 Advanced ADMB run options (MCMC, adnuts)

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SS3 models are typically run using maximum likelihood estimation (MLE), but it is possible to run SS3 models using Markov Chain Monte Carlo (MCMC). MCMC is outside the scope of this document, but below are some resources to check out in case you are interested in pursuing it further:

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  1. See the ADMB user manual for more information on using MCMC in ADMB and the section on Running MCMC in the SS3 user manual for more specific information on running MCMC in SS3.
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  3. See the adnuts repository for more information on using the No-U-Turn sampler (NUTS) in ADMB and this article by Monnahan et al. (2019) to better understand the benefits of using NUTS for assessment models.
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10 Some questions to consider during model development

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There are many elements to consider in an SS3 model. Below are some questions and considerations that experienced SS3 users have used to check that their model makes sense throughout the iterative model development process.

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10.1 Does my model model make sense?

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  • Does the biology make sense? Selectivity?
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  • Is the model fitting the indices? Length and age data?
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  • What is the pattern in recruitment?
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  • Does the prior type match what I thought I was specifying?
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10.2 Does my model model fit well?

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  • The r4ss::SS_output() function prints some very useful diagnostics to the R console window, including a list of the estimated parameters, the estimated value, whether the parameter is hitting a bound, the gradient, and the prior type. Use this (or other SS3 output files) to answer:

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    • Did the parameter move from the initial value?
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    • Is the parameter gradient low?
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  • Below the parameter estimates that r4ss::SS_output() prints to the console, there is a specific section that highlights the parameters with the highest model gradients. Examining this list might indicate which (if any) parameters may need to be fixed due to lack of information in the data. For example, there may be selectivity parameters included in the list where small changes in the parameter value do not change the model results (or the form of the selectivity).

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10.3 Does my data weighting make sense?

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The r4ss::SS_output() function prints information on tuning to the R console. This includes tuning information for length and age data that can be used for data weighting when using McAllister Ianelli weighting (i.e., weighting by the harmonic mean). The r4ss::SS_plots() function creates mean length and ages figures with captions showing the recommended Francis weights for each data source. Even if you are using an alternative data weighting approach (i.e., Francis, Dirichlet, or other), it is always good practice to explore an alternative approach and see how data weighting changes the model.

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10.4 Did I fix variation in recruitment correctly?

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Check the sigmaR (i.e., recruitment devs standard deviation) information. The sigmaR parameter is typically fixed within the model, so if it is not fixed within your model, you should consider whether or not this makes sense for the population and given the quality and quantity of data.

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For a fixed value of sigmaR, a section of the output will provide diagnostics to determine if the fixed sigmaR value is capturing the estimated variations in recruitment. The r4ss::SS_output() function will print a recommended value based on the variation in the estimated recruitment deviations that you may want to consider using.

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11 Where to get additional help

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