5 Helper functions in SSMSE

run_SSMSE() requires some detailed inputs that are time consuming to make. In the interest of automating routines as much as possible, helper functions are available in SSMSE.

5.1 Creating multiple operating models with develop_OMs()

This function allows users to change a parameter in a Stock Synthesis OM and, if desired, refit the operating model to data. Typically, users will want to create multiple operating models to use in different scenarios in order to account for uncertainties in parameter values, such as biological parameters relating to natural mortality or growth.

5.2 Set up sampling for a scenario with create_sample_struct()

run_SSMSE() requires the input sample_struct, which outlines the future sampling structure to use to generate data sets. This object could be difficult to create manually and users may want to just continue sampling as previously done in an existing SS3 model. create_sample_struct() will use the time patterns in sampling for different data types found in an SS3 data file and extend it forward in time. If no pattern is found, the function will return NAs and provide a warning to the user. If any NAs are returned (except to indicate that an entire data type is missing), the user will need to remove or fill in the NA value before usign the list as an input to run_SSMSE().

See use of create_sample_struct() in simple example.

5.3 Get examples of the future_om_list input with create_future_om_list()

Users can modify the future structure of their operating model through the future_om_list input to run_SSMSE. The structure and names of this object matter, so examples are provided by calling create_future_om_list().

See more details about the specification of future_om_list objects.