Setup Simulation Fishing Inputs

Initializes and validates fishing-related inputs for a simulation list (`sim_list`). This includes fishing mortality, selectivity, catchability, observation error, and age- and length-composition parameters for both aggregate and population-specific data.

Usage

Setup_Sim_Fishing(
  sim_list,
  ln_sigmaC = array(log(0.02), dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_fish_fleets)),
  ln_sigmaC_pop = array(log(0.02), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_fish_fleets)),
  ln_sigmaCAA = array(log(0.2), dim = c(sim_list$n_ages, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  ln_sigmaDAA = array(log(0.2), dim = c(sim_list$n_ages, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  UseCatchAA = array(0, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_ages, sim_list$n_sexes, sim_list$n_fish_fleets)),
  UseDiscardAA = array(0, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_ages, sim_list$n_sexes, sim_list$n_fish_fleets)),
  ObsCatchAA_SE = NULL,
  ObsDiscardAA_SE = NULL,
  CatchAA_Type = "spltRaggS",
  DiscardAA_Type = "spltRaggS",
  CatchAA_LikeType = "lognormal",
  DiscardAA_LikeType = "lognormal",
  CatchAA_sigma_form = "none",
  DiscardAA_sigma_form = "none",
  use_catch_aa = rep(0, sim_list$n_fish_fleets),
  use_discard_aa = rep(0, sim_list$n_fish_fleets),
  catch_units = array(1, dim = c(sim_list$n_fish_fleets)),
  init_F_val = array(0, dim = c(sim_list$n_regions, sim_list$n_seas,
    sim_list$n_fish_fleets)),
  Fmort_input = array(0.1, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_fish_fleets, sim_list$n_sims)),
  fish_sel_input,
  fish_q_input = array(1, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_fish_fleets, sim_list$n_sims)),
  ObsFishIdx_SE = array(0.2, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_fish_fleets)),
  ObsFishIdx_pop_SE = array(0.2, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_fish_fleets)),
  fish_idx_type = array(1, dim = c(sim_list$n_regions, sim_list$n_fish_fleets)),
  FishIdx_LikeType = rep(0, sim_list$n_fish_fleets),
  Catch_seas_Type = NULL,
  Catch_pop_seas_Type = NULL,
  FishIdx_seas_Type = NULL,
  FishIdx_pop_seas_Type = NULL,
  FishAgeComps_seas_Type = NULL,
  FishIdx_Cov = NULL,
  UseFishIdx = NULL,
  t_fish = array(0, dim = c(sim_list$n_regions, sim_list$n_seas, sim_list$n_fish_fleets)),
  comp_fish_caal_like = rep(999, sim_list$n_fish_fleets),
  ISS_Fish_caal = NULL,
  ln_Fish_caal_theta = NULL,
  ln_Fish_caal_theta_agg = NULL,
  Fish_caal_Type = array(999, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishage_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishAgeComps = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets, sim_list$n_sims)),
  ln_FishAge_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  ln_FishAge_theta_agg = rep(log(1), sim_list$n_fish_fleets),
  FishAge_corr_pars_agg = rep(0.01, sim_list$n_fish_fleets),
  FishAge_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets, 2)),
  FishAgeComps_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishlen_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishLenComps = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets, sim_list$n_sims)),
  ln_FishLen_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  ln_FishLen_theta_agg = rep(log(1), sim_list$n_fish_fleets),
  FishLen_corr_pars_agg = rep(0.01, sim_list$n_fish_fleets),
  FishLen_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets, 2)),
  FishLenComps_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishage_pop_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishAgeComps_pop = array(100, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets,
    sim_list$n_sims)),
  ln_FishAge_pop_theta = array(log(1), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets)),
  ln_FishAge_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishAge_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets, 2)),
  FishAge_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishAgeComps_pop_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishlen_pop_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishLenComps_pop = array(100, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets,
    sim_list$n_sims)),
  ln_FishLen_pop_theta = array(log(1), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets)),
  ln_FishLen_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishLen_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets, 2)),
  FishLen_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishLenComps_pop_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  ret_sel_input = array(1, dim = c(sim_list$n_pop, sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_ages, sim_list$n_sexes, sim_list$n_fish_fleets,
    sim_list$n_sims)),
  dmr_input = array(0, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_fish_fleets, sim_list$n_sims)),
  discard_units = array(3, dim = c(sim_list$n_fish_fleets)),
  ln_sigmaD = array(log(0.02), dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_fish_fleets)),
  ln_sigmaD_pop = array(log(0.02), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_fish_fleets)),
  comp_fishage_discard_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishAgeComps_discard = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets, sim_list$n_sims)),
  ln_FishAge_discard_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  ln_FishAge_discard_theta_agg = rep(log(1), sim_list$n_fish_fleets),
  FishAge_discard_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets, 2)),
  FishAge_discard_corr_pars_agg = rep(0.01, sim_list$n_fish_fleets),
  FishAgeComps_discard_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishlen_discard_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishLenComps_discard = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets, sim_list$n_sims)),
  ln_FishLen_discard_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets)),
  ln_FishLen_discard_theta_agg = rep(log(1), sim_list$n_fish_fleets),
  FishLen_discard_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_fish_fleets, 2)),
  FishLen_discard_corr_pars_agg = rep(0.01, sim_list$n_fish_fleets),
  FishLenComps_discard_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_fish_fleets)),
  comp_fishage_discard_pop_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishAgeComps_discard_pop = array(100, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets,
    sim_list$n_sims)),
  ln_FishAge_discard_pop_theta = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_regions, sim_list$n_sexes, sim_list$n_fish_fleets)),
  ln_FishAge_discard_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishAge_discard_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets, 2)),
  FishAge_discard_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishAgeComps_discard_pop_Type = array(2, dim = c(sim_list$n_yrs,
    sim_list$n_fish_fleets)),
  comp_fishlen_discard_pop_like = rep(0, sim_list$n_fish_fleets),
  ISS_FishLenComps_discard_pop = array(100, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_yrs, sim_list$n_seas, sim_list$n_sexes, sim_list$n_fish_fleets,
    sim_list$n_sims)),
  ln_FishLen_discard_pop_theta = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_regions, sim_list$n_sexes, sim_list$n_fish_fleets)),
  ln_FishLen_discard_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishLen_discard_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_fish_fleets, 2)),
  FishLen_discard_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop,
    sim_list$n_fish_fleets)),
  FishLenComps_discard_pop_Type = array(2, dim = c(sim_list$n_yrs,
    sim_list$n_fish_fleets))
)

Arguments

sim_list: A list containing simulation settings, including the number of populations (`n_pop`), regions (`n_regions`), years (`n_yrs`), seasons (`n_seas`), ages (`n_ages`), sexes (`n_sexes`), fishing fleets (`n_fish_fleets`), and simulations (`n_sims`).
ln_sigmaC: Numeric array. Log-scale observation SD for total catch, dimensions `n_regions x n_yrs x n_seas x n_fish_fleets`. Default: log(0.02).
ln_sigmaC_pop: Numeric array. Log-scale observation SD for population-specific catch, dimensions `n_pop x n_regions x n_yrs x n_seas x n_fish_fleets`. Default: log(0.02).
ln_sigmaCAA, ln_sigmaDAA: Log-scale observation error for the at-age data sources, `n_ages x n_sexes x n_fish_fleets`. An array without the sex dim is required.
UseCatchAA, UseDiscardAA: Integer arrays `n_regions x n_yrs x n_seas x n_ages x n_sexes x n_fish_fleets`, `1` where an at-age observation is drawn. The sex dim is required: a data source summed over sexes has its flag in sex slot one.
ObsCatchAA_SE, ObsDiscardAA_SE: Reported standard errors shaped like the use arrays, read only when the data source's `sigma_form` asks for them.
CatchAA_Type, DiscardAA_Type: Which dims each fleet reports separately: `"agg"`, `"spltRaggS"` (default), `"aggRspltS"` or `"spltRspltS"`. A summed dim is drawn once, into slot one.
CatchAA_LikeType, DiscardAA_LikeType: `"lognormal"` (default) or `"normal"`, per fleet.
CatchAA_sigma_form, DiscardAA_sigma_form: Where the observation error comes from: `"none"` (default), `"data"`, `"est_additive"` or `"est_quadrature"`.
use_catch_aa, use_discard_aa: Integer vectors `n_fish_fleets`, `1` for fleets whose at-age data sources are drawn.
catch_units: Numeric vector. Catch units (0 = abundance, 1 = biomass), length `n_fish_fleets`. Default: 1.
init_F_val: Numeric array. Initial fishing mortality, dimensions `n_regions x n_seas x n_fish_fleets`. Default: 0.
Fmort_input: Numeric array. Fishing mortality, dimensions `n_regions x n_yrs x n_seas x n_fish_fleets x n_sims`. Default: 0.1.
fish_sel_input: Numeric array. Fishery selectivity, dimensions `n_pop x n_regions x n_yrs x n_seas x n_ages x n_sexes x n_fish_fleets x n_sims`.
fish_q_input: Numeric array. Catchability, dimensions `n_regions x n_yrs x n_fish_fleets x n_sims`. Default: 1.
ObsFishIdx_SE: Numeric array. Observation SD for fishery indices, dimensions `n_regions x n_yrs x n_seas x n_fish_fleets`. Default: 0.2.
ObsFishIdx_pop_SE: Numeric array. Observation SD for population-specific fishery indices, dimensions `n_pop x n_regions x n_yrs x n_seas x n_fish_fleets`. Default: 0.2.
fish_idx_type: Numeric array. Index type (0 = abundance, 1 = biomass), dimensions `n_regions x n_fish_fleets`. Default: 1.
FishIdx_LikeType: Character or numeric vector, length `n_fish_fleets`. Error structure each fleet's index is drawn under: "lognormal" (0), "normal" (1), or "mvn" (2), matching the estimation model's FishIdx_LikeType. An mvn fleet draws from FishIdx_Cov through a common-factor decomposition (see cov_to_factor) instead of ObsFishIdx_SE, and its population-specific data source stays lognormal. Default: lognormal for every fleet.
Catch_seas_Type, Catch_pop_seas_Type, FishIdx_seas_Type, FishIdx_pop_seas_Type, FishAgeComps_seas_Type: Whether the operating model reports a data source once a season ("spltSeas", the default) or once a year as a season total ("aggSeas"). One value for every fleet or one per fleet. An annual total is written into season one with the other seasons left at zero, and the observation error is applied once to that total rather than to each season, so an estimation model reading it should mark season one in its Use array and set the matching argument in Setup_Mod_Catch_and_F or Setup_Mod_FishIdx_and_Comps.
FishIdx_Cov: List with one element per fishery fleet holding the fixed covariance over that fleet's fitted index observations, ordered by scanning UseFishIdx in array order (region fastest, then year, then season). Required for mvn fleets. Default: NULL.
UseFishIdx: Numeric array [n_regions x n_yrs x n_seas x n_fish_fleets] of fit flags from the estimation model, used to position each simulated cell in the covariance. Its year dimension may be shorter than the simulation, in which case later years draw with the mean factor scale and loading. Required for mvn fleets. Default: NULL.
t_fish: Numeric array [n_regions x n_seas x n_fish_fleets] giving the fishery index timing, the fraction of the season elapsed when the index is observed. Numbers at age are decayed by exp(-t_fish * ZAA) before the index is formed, matching t_srv for surveys and the estimation model's own t_fish. Defaults to 0 (start of season).
comp_fish_caal_like: Character or numeric vector `n_fish_fleets` giving the conditional age-at-length likelihood per fleet: `"Multinomial"` (0), `"Dirichlet-Multinomial"` (1), or `"none"` (999). Only these two families exist for CAAL: a CAAL row is the age composition of the otoliths taken from one length bin, usually a small and mostly zero sample, which the logistic-normal forms cannot support. Default: `"none"` for every fleet.
ISS_Fish_caal: Numeric array. Number of fish aged within each length bin, dimensions `n_regions x n_yrs x n_seas x n_lens x n_sexes x n_fish_fleets x n_sims`. A bin whose sample size rounds to zero is skipped. `NULL` (the default) draws no CAAL; supplying it alongside a likelihood other than `"none"` is what switches `do_fish_caal` on. Requires `n_lens`.
ln_Fish_caal_theta: Numeric array. Log overdispersion for the Dirichlet-multinomial, dimensions `n_regions x n_sexes x n_fish_fleets`. Read under the split types, `[r, s, f]` when sexes are split and `[r, 1, f]` when they are joint, and ignored under the multinomial. Default: log(1).
ln_Fish_caal_theta_agg: Numeric vector `n_fish_fleets`. The aggregated type's counterpart to `ln_Fish_caal_theta`. Default: log(1).
Fish_caal_Type: Numeric or character array giving the composition structure per year and fleet, dimensions `n_yrs x n_fish_fleets`: `"agg"` (0) pools regions and sexes and is drawn once when the region loop reaches the last region, `"spltRspltS"` (1) draws each sex in a bin as its own sample, `"spltRjntS"` (2) draws one sample across the age by sex stack, and `"none"` (999) skips the fleet in that year. Unlike the estimation model, which parses `"CompType_Year_x-y_Fleet_z"` strings, the simulator takes the year by fleet array directly. Default: `"none"` throughout.
comp_fishage_like: Numeric vector. Likelihood for age composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants), length `n_fish_fleets`. Default: 0.
ISS_FishAgeComps: Numeric array. Effective sample sizes for age compositions, dimensions `n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishAge_theta: Numeric array. Log-scale overdispersion for fishery age compositions, dimensions `n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishAge_theta_agg: Numeric vector. Aggregated log-scale overdispersion for fishery age compositions, length `n_fish_fleets`. Default: log(1).
FishAge_corr_pars_agg: Numeric vector. Aggregated correlation parameters for fishery age compositions, length `n_fish_fleets`. Default: 0.01.
FishAge_corr_pars: Numeric array. Correlation parameters for fishery age compositions, dimensions `n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishAgeComps_Type: Numeric array. Composition structure for fishery age compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishlen_like: Numeric vector. Likelihood for length composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants), length `n_fish_fleets`. Default: 0.
ISS_FishLenComps: Numeric array. Effective sample sizes for length compositions, dimensions `n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishLen_theta: Numeric array. Log-scale overdispersion for fishery length compositions, dimensions `n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishLen_theta_agg: Numeric vector. Aggregated log-scale overdispersion for fishery length compositions, length `n_fish_fleets`. Default: log(1).
FishLen_corr_pars_agg: Numeric vector. Aggregated correlation parameters for fishery length compositions, length `n_fish_fleets`. Default: 0.01.
FishLen_corr_pars: Numeric array. Correlation parameters for fishery length compositions, dimensions `n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishLenComps_Type: Numeric array. Composition structure for fishery length compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishage_pop_like: Numeric vector. Likelihood for population-specific fishery age composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants), length `n_fish_fleets`. Default: 0.
ISS_FishAgeComps_pop: Numeric array. Effective sample sizes for population-specific fishery age compositions, dimensions `n_pop x n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishAge_pop_theta: Numeric array. Log-scale overdispersion for population-specific fishery age compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishAge_pop_theta_agg: Numeric array. Aggregated log-scale overdispersion for population-specific fishery age compositions, dimensions `n_pop x n_fish_fleets`. Default: log(1).
FishAge_pop_corr_pars: Numeric array. Correlation parameters for population-specific fishery age compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishAge_pop_corr_pars_agg: Numeric array. Aggregated correlation parameters for population-specific fishery age compositions, dimensions `n_pop x n_fish_fleets`. Default: 0.01.
FishAgeComps_pop_Type: Numeric array. Composition structure for population-specific fishery age compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishlen_pop_like: Numeric vector. Likelihood for population-specific fishery length composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants), length `n_fish_fleets`. Default: 0.
ISS_FishLenComps_pop: Numeric array. Effective sample sizes for population-specific fishery length compositions, dimensions `n_pop x n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishLen_pop_theta: Numeric array. Log-scale overdispersion for population-specific fishery length compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishLen_pop_theta_agg: Numeric array. Aggregated log-scale overdispersion for population-specific fishery length compositions, dimensions `n_pop x n_fish_fleets`. Default: log(1).
FishLen_pop_corr_pars: Numeric array. Correlation parameters for population-specific fishery length compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishLen_pop_corr_pars_agg: Numeric array. Aggregated correlation parameters for population-specific fishery length compositions, dimensions `n_pop x n_fish_fleets`. Default: 0.01.
FishLenComps_pop_Type: Numeric array. Composition structure for population-specific fishery length compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
ret_sel_input: Numeric array. Retained selectivity at age, dimensions `n_pop x n_regions x n_yrs x n_seas x n_ages x n_sexes x n_fish_fleets x n_sims`. Default: 1.
dmr_input: Numeric array. Discard mortality rate, dimensions `n_regions x n_yrs x n_seas x n_fish_fleets x n_sims`. Default: 0.
discard_units: Numeric vector. Discard units (0 = abundance, 1 = biomass, 2 = abundance fraction, 3 = biomass fraction), length `n_fish_fleets`. Default: 3.
ln_sigmaD: Numeric array. Log-scale observation SD for discards, dimensions `n_regions x n_yrs x n_seas x n_fish_fleets`. Default: log(0.02).
ln_sigmaD_pop: Numeric array. Log-scale observation SD for population-specific discards, dimensions `n_pop x n_regions x n_yrs x n_seas x n_fish_fleets`. Default: log(0.02).
comp_fishage_discard_like: Numeric vector. Likelihood for discard age composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants, 999 = none), length `n_fish_fleets`. Default: 0.
ISS_FishAgeComps_discard: Numeric array. Effective sample sizes for discard age compositions, dimensions `n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishAge_discard_theta: Numeric array. Log-scale overdispersion for discard age compositions, dimensions `n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishAge_discard_theta_agg: Numeric vector. Aggregated log-scale overdispersion for discard age compositions, length `n_fish_fleets`. Default: log(1).
FishAge_discard_corr_pars: Numeric array. Correlation parameters for discard age compositions, dimensions `n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishAge_discard_corr_pars_agg: Numeric vector. Aggregated correlation parameters for discard age compositions, length `n_fish_fleets`. Default: 0.01.
FishAgeComps_discard_Type: Numeric array. Composition structure for discard age compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishlen_discard_like: Numeric vector. Likelihood for discard length composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants, 999 = none), length `n_fish_fleets`. Default: 0.
ISS_FishLenComps_discard: Numeric array. Effective sample sizes for discard length compositions, dimensions `n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishLen_discard_theta: Numeric array. Log-scale overdispersion for discard length compositions, dimensions `n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishLen_discard_theta_agg: Numeric vector. Aggregated log-scale overdispersion for discard length compositions, length `n_fish_fleets`. Default: log(1).
FishLen_discard_corr_pars: Numeric array. Correlation parameters for discard length compositions, dimensions `n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishLen_discard_corr_pars_agg: Numeric vector. Aggregated correlation parameters for discard length compositions, length `n_fish_fleets`. Default: 0.01.
FishLenComps_discard_Type: Numeric array. Composition structure for discard length compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishage_discard_pop_like: Numeric vector. Likelihood for population-specific discard age composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants, 999 = none), length `n_fish_fleets`. Default: 0.
ISS_FishAgeComps_discard_pop: Numeric array. Effective sample sizes for population-specific discard age compositions, dimensions `n_pop x n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishAge_discard_pop_theta: Numeric array. Log-scale overdispersion for population-specific discard age compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishAge_discard_pop_theta_agg: Numeric array. Aggregated log-scale overdispersion for population-specific discard age compositions, dimensions `n_pop x n_fish_fleets`. Default: log(1).
FishAge_discard_pop_corr_pars: Numeric array. Correlation parameters for population-specific discard age compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishAge_discard_pop_corr_pars_agg: Numeric array. Aggregated correlation parameters for population-specific discard age compositions, dimensions `n_pop x n_fish_fleets`. Default: 0.01.
FishAgeComps_discard_pop_Type: Numeric array. Composition structure for population-specific discard age compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.
comp_fishlen_discard_pop_like: Numeric vector. Likelihood for population-specific discard length composition (0 = Multinomial, 1 = Dirichlet-Multinomial, 2-4 = Logistic-Normal variants, 999 = none), length `n_fish_fleets`. Default: 0.
ISS_FishLenComps_discard_pop: Numeric array. Effective sample sizes for population-specific discard length compositions, dimensions `n_pop x n_regions x n_yrs x n_seas x n_sexes x n_fish_fleets x n_sims`. Default: 100.
ln_FishLen_discard_pop_theta: Numeric array. Log-scale overdispersion for population-specific discard length compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets`. Default: log(1).
ln_FishLen_discard_pop_theta_agg: Numeric array. Aggregated log-scale overdispersion for population-specific discard length compositions, dimensions `n_pop x n_fish_fleets`. Default: log(1).
FishLen_discard_pop_corr_pars: Numeric array. Correlation parameters for population-specific discard length compositions, dimensions `n_pop x n_regions x n_sexes x n_fish_fleets x 2`. Default: 0.01.
FishLen_discard_pop_corr_pars_agg: Numeric array. Aggregated correlation parameters for population-specific discard length compositions, dimensions `n_pop x n_fish_fleets`. Default: 0.01.
FishLenComps_discard_pop_Type: Numeric array. Composition structure for population-specific discard length compositions (0 = aggregated, 1 = split region/sex, 2 = split region joint sex, 999 = none), dimensions `n_yrs x n_fish_fleets`. Default: 2.

Value

A modified `sim_list` with validated fishing-related inputs.

Usage

Arguments

Value

See also