This vignette introduces how to run MSE for a single stock with multiple fleets and surveys:

1. Load “WHAM” and “whamMSE”:

library(wham)
library(whamMSE)

main.dir = here::here()

2. Generate basic information

year_start  <- 1  # starting year in the burn-in period
year_end    <- 20  # end year in the burn-in period
MSE_years   <- 30     # number of years in the feedback loop
# Note: no need to include MSE_years in simulation-estimation 

info <- generate_basic_info(n_stocks = 1,
                            n_regions = 1,
                            n_indices = 2,
                            n_fleets = 2,
                            n_seasons = 1,
                            catch_info = list(catch_cv = 0.1, catch_Neff = 100, use_agg_catch = 1, use_catch_paa = 1),
                            index_info = list(index_cv = 0.1, index_Neff = 100, fracyr_indices = 0.5, q = 0.2,
                                              use_indices = 1, use_index_paa = 1, units_indices = 2, units_index_paa = 2),
                            base.years = year_start:year_end,
                            n_feedback_years = MSE_years,
                            life_history = "medium",
                            n_ages = 12,
                            fracyr_spawn = 0.5) 

basic_info = info$basic_info # collect basic information
catch_info = info$catch_info # collect fleet catch information
index_info = info$index_info # collect survey information
F_info = info$F # collect fishing information

3. Configure selecitvity and natural mortality

n_stocks  <- as.integer(basic_info['n_stocks'])
n_regions <- as.integer(basic_info['n_regions'])
n_fleets  <- as.integer(basic_info['n_fleets'])
n_indices <- as.integer(basic_info['n_indices'])
n_ages    <- as.integer(basic_info['n_ages'])

# Selectivity Configuration
fleet_pars <- c(5,1)
index_pars <- c(2,1)
sel <- list(model=rep("logistic",n_fleets+n_indices),
            initial_pars=c(rep(list(fleet_pars),n_fleets),rep(list(index_pars),n_indices)))

# M Configuration
M <- list(model="constant",initial_means=array(0.2, dim = c(n_stocks,n_regions,n_ages)))

4. Configure numbers-at-age (NAA)

sigma        <- "rec+1"
re_cor       <- "iid"
ini.opt      <- "equilibrium" 
sigma_vals   <-  array(0.5, dim = c(n_stocks, n_regions, n_ages)) # NAA sigma

NAA_re <- list(N1_model=rep(ini.opt,n_stocks),
               sigma=rep(sigma,n_stocks),
               cor=rep(re_cor,n_stocks),
               recruit_model = 2,  # rec random around the mean
               sigma_vals = sigma_vals) # NAA_where must be specified in basic_info!

5. Generate wham input

Here we use prepare_wham_input() function to generate a wham input using the basic information we set above:

input <- prepare_wham_input(basic_info = basic_info, 
                            selectivity = sel, 
                            M = M, 
                            NAA_re = NAA_re, 
                            catch_info = catch_info, 
                            index_info = index_info, 
                            F = F_info)

6. Change observation error for survey indices in the feedback period

agg_index_sigma = input$data$agg_index_sigma
agg_index_sigma[21:50,] = 0.5 # Increase CV for both survey indices in the feedback period
index_Neff = input$data$index_Neff
index_Neff[21:50,] = 50 # Decrease ESS for both survey indices in the feedback period
input <- update_input_index_info(input, agg_index_sigma, index_Neff) # Update input file

7. Generate operating model

random = input$random # check what processes are random effects
input$random = NULL # so inner optimization won't change simulated RE
om <- fit_wham(input, do.fit = F, do.brps = T, MakeADFun.silent = TRUE)
# Note: do.fit must be FALSE (no modeling fitting yet)

8. Generate dataset

om_with_data <- update_om_fn(om, seed = 123, random = random)

9. Specify assessment interval and assessment year in the feedback loop

assess.interval <- 3 # Note: assessment interval is 3 years, given the feedback period is 3 years, there will be only 1 assessment
base.years      <- year_start:year_end # Burn-in period
first.year      <- head(base.years,1)
terminal.year   <- tail(base.years,1)
assess.years    <- seq(terminal.year, tail(om$years,1)-assess.interval,by = assess.interval)

10. Use the EM same as OM to conduct MSE

mod1 = loop_through_fn(om = om_with_data,
                       em_info = info, 
                       random = random,
                       M_em = M, 
                       sel_em = sel,
                       NAA_re_em = NAA_re,
                       age_comp_em = "multinomial",
                       em.opt = list(separate.em = FALSE, 
                                     separate.em.type = 3, 
                                     do.move = FALSE, 
                                     est.move = FALSE),
                       assess_years = assess.years, 
                       assess_interval = assess.interval, 
                       base_years = base.years,
                       year.use = 20,
                       update_index_info = list(agg_index_sigma = agg_index_sigma,index_Neff = index_Neff),
                       # Assuming we know the true observation error of the data
                       # agg_index_sigma and index_Neff can be different than the true values in the OM
                       add.years = TRUE, 
                       # assessment will use 20 years of data from historical period + new years in the feedback period
                       seed = 123,
                       save.sdrep = TRUE,
                       save.last.em = TRUE,
                       do.retro = FALSE, # Perform retrospective analysis
                       do.osa = FALSE) # Perform OSA residual analysis

11. Use the EM with semi selfweighting likelihoods for age composition data

mod2 = loop_through_fn(om = om_with_data,
                      em_info = info, 
                      random = random,
                      M_em = M, 
                      sel_em = sel,
                      NAA_re_em = NAA_re, 
                      age_comp_em = "logistic-normal-miss0", # semi self-weighting
                      em.opt = list(separate.em = FALSE, 
                                    separate.em.type = 3, 
                                    do.move = FALSE, 
                                    est.move = FALSE),
                      assess_years = assess.years, 
                      assess_interval = assess.interval, 
                      base_years = base.years,
                      year.use = 20,
                      update_index_info = list(agg_index_sigma = agg_index_sigma,index_Neff = index_Neff),
                      # Assuming we know the true observation error of the data
                      # agg_index_sigma and index_Neff can be different than the true values in the OM
                      add.years = TRUE, 
                      # assessment will use 20 years of data from historical period + new years in the feedback period
                      seed = 123,
                      save.sdrep = TRUE,
                      save.last.em = TRUE,
                      do.retro = FALSE, # Perform retrospective analysis
                      do.osa = FALSE) # Perform OSA residual analysis

12. Performance

par(mfrow = c(1,3))

plot(mod1$om$rep$SSB, type = "o", col = "red", main = "SSB")
lines(mod2$om$rep$SSB, type = "o", col = "blue")

legend("topright", legend=c("Multinomial", "Logistic-normal"),
       col=c("red", "blue"), lty=c(1,1), cex=0.8)

plot(mod1$om$rep$Fbar[,3], type = "o", col = "red", main = "F", ylim = c(0,1))
lines(mod2$om$rep$Fbar[,3], type = "o", col = "blue")

plot(rowSums(mod1$om$rep$pred_catch), type = "o", col = "red", main = "Catch")
lines(rowSums(mod2$om$rep$pred_catch), type = "o", col = "blue")
Figure 1
Figure 1