Function reference • SSMSE

All functions

EM(): Use EM as the management strategy option.

Interim(): Interim assessment management strategy

SSMSE-package SSMSE: SSMSE: A package for Management Strategy Evaluation (MSE) using Stock Synthesis (SS)

SSMSE_summary_all(): Get results in a list for 1 scenario

SSMSE_summary_iter(): Get results in a list for 1 iteration

SSMSE_summary_scen(): Get results in a list for 1 scenario

Sim_comp(): Calculate uncertainty and biases in historic composition data

add_OM_devs(): Add in future parameter values

add_dev_changes(): Add the deviation changes from the list obj to an existing df

add_new_dat(): Add new data to an existing EM dataset

add_sample_struct(): Add in years of sampling data needed

calc_comp_var(): Calculate uncertainty and biases in historic composition data

calc_par_trend(): Calculate the parameter trend

change_dat(): Change dataset from OM into format for EM

change_yrs_fcast(): Change the years in the forecast file

check_EM_forecast(): Check structure of forecast is suitable to use in the EM

check_OM_dat(): check that an OM data set has at least the same data as an estimation model

check_avail_dat(): check all index years/fleets in EM available in OM. (but not vice versa) a general function that can be used

check_catch_df(): Check the catch dataframe

check_convergence(): Flag potential convergence issues in SS3 model runs

check_dir(): Check that the directory for an OM is valid

check_future_catch(): Check future catch smaller than the last year's population size.

check_future_om_list_str(): Check the general structure of a future OM list and standardize values

check_future_om_list_vals(): Check structure of a future OM list against the scen_list and standardize output

check_sample_struct(): Check sample_struct_list

check_scen_list(): Check structure of the object scen_list

clean_init_mod_files(): clean the initial model files

combine_cols(): function that creates a combined column to the list_item of interest

convert_future_om_list_to_devs_df(): Create the devs dataframe for a scenario and iteration from user input

convert_to_r4ss_names(): Convert user input to r4ss data names

copy_model_files(): Copy OM and EM model files

create_OM(): Create the OM

create_future_om_list(): Helper function to create future om list objects

create_out_dirs(): create the OM directory

create_sample_struct(): Create the sample_struct list

create_scen_list(): Create scen_list object to use in run_SSMSE function.

develop_OMs(): Develop different operating models

get_EM_catch_df(): Get the EM catch data frame

get_EM_dat(): Change the OM data to match the format of the original EM data

get_F(): Get the Fishing mortality from the timeseries Report.sso table

get_SSB_avg(): Example Performance Metric: calculate the average SSB over a range of years for each iteration

get_avg_catch(): Example Performance Metric: Calculate average catch over a range of years

get_bin(): Get SS3 binary/executable location in package

get_catch_cv(): Example Performance Metric: Calculate the coefficient of variation of catch

get_catch_sd(): Example Performance Metric: Calculate Standard Deviation of Catch

get_dead_catch(): Get dead catch from the timeseries Report.sso table

get_full_sample_struct(): Get the full sample structure from user input

get_impl_error_matrix(): Put implementation error of 0 into a matrix

get_init_samp_scheme(): Get the sampling scheme in a data file.

get_input_value(): return a value from a data frame

get_no_EM_catch_df(): Get the data frame of catch for the next iterations when not using an estimation model.

get_performance_metrics(): get basic data to calculate performance metrics

get_rel_SSB_avg(): Example Performance Metric: Calculate the avg relative SSB (SSB/SSB unfished) over a range of years for each iteration

get_retained_catch(): Get retained catch from the timeseries Report.sso table

get_total_catch(): Example Performance Metric: Calculate total catch over a range of years

last_yr_catch(): Last year catch used in the future for management strategy

locate_in_dirs(): Locate the OM model files

match_parname(): Match parameter name to parameter names in the par file

no_catch(): No Catch in the future management strategy

parse_MS(): Parse management strategy options

plot_comp_sampling(): Plot comp data, expected values, and sampled data for 1 scenario

plot_index_sampling(): Plot index data, expected values, and sampled data for 1 scenario

r4ss_obj_err(): Error if object is not an r4ss object

rm_sample_struct_hist(): Remove the historical sampling structure

rm_vals(): remove vals in 2 list components with the same name

run_EM(): Run the estimation model

run_OM(): Initial run of the OM

run_SSMSE(): run an MSE using SS OMs

run_SSMSE_iter(): Run one iteration of an MSE using SS OM

run_SSMSE_scen(): Run an MSE scenario using SS OM

run_ss_model(): Run an operating or estimation model

sample_vals(): Sample vals from normal random, lognormal random, or modified AR-1 process.

set_MSE_seeds(): Set the initial global, scenario, and iteration seeds

test_no_par(): Change a model from running with par to running without par

update_OM(): Extend the OM forward using next years' catch

update_basevals_blocks(): Update a sequence of base parameter annual values to account for a time varying block effects

update_basevals_dev(): Update a sequence of base parameter annual values to account for a time varying deviation effects

update_basevals_env(): Update a sequence of base parameter annual values to account for a time varying environmental effects