Set up survey parameterization for the operating model simulation

Populates sim_list with all survey-related inputs needed by the operating model: catchability, selectivity, survey timing, index type, and age/length composition likelihood settings including overdispersion and correlation parameters. Must be called after Setup_Sim_Dim.

Usage

Setup_Sim_Survey(
  sim_list,
  srv_sel_input,
  ObsSrvIdx_SE = array(0.2, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_srv_fleets)),
  ln_sigmaSrvIdxAA = array(log(0.2), dim = c(sim_list$n_ages, sim_list$n_sexes,
    sim_list$n_srv_fleets)),
  UseSrvIdxAA = array(0, dim = c(sim_list$n_regions, sim_list$n_yrs, sim_list$n_seas,
    sim_list$n_ages, sim_list$n_sexes, sim_list$n_srv_fleets)),
  ObsSrvIdxAA_SE = NULL,
  SrvIdxAA_Type = "spltRaggS",
  SrvIdxAA_LikeType = "lognormal",
  SrvIdxAA_sigma_form = "none",
  use_srv_idx_aa = rep(0, sim_list$n_srv_fleets),
  ObsSrvIdx_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$n_srv_fleets)),
  srv_q_input = array(1, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_srv_fleets, sim_list$n_sims)),
  t_srv = array(1, dim = c(sim_list$n_regions, sim_list$n_seas, sim_list$n_srv_fleets)),
  srv_idx_type = array(1, dim = c(sim_list$n_srv_fleets)),
  SrvIdx_LikeType = rep(0, sim_list$n_srv_fleets),
  SrvIdx_seas_Type = NULL,
  SrvIdx_pop_seas_Type = NULL,
  SrvAgeComps_seas_Type = NULL,
  SrvIdx_Cov = NULL,
  UseSrvIdx = NULL,
  comp_srv_caal_like = rep(999, sim_list$n_srv_fleets),
  ISS_Srv_caal = NULL,
  ln_Srv_caal_theta = NULL,
  ln_Srv_caal_theta_agg = NULL,
  Srv_caal_Type = array(999, dim = c(sim_list$n_yrs, sim_list$n_srv_fleets)),
  comp_srvage_like = rep(0, sim_list$n_srv_fleets),
  ISS_SrvAgeComps = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_srv_fleets, sim_list$n_sims)),
  ln_SrvAge_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_srv_fleets)),
  ln_SrvAge_theta_agg = rep(log(1), sim_list$n_srv_fleets),
  SrvAge_corr_pars_agg = rep(0.01, sim_list$n_srv_fleets),
  SrvAge_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_srv_fleets, 2)),
  SrvAgeComps_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_srv_fleets)),
  comp_srvlen_like = rep(0, sim_list$n_srv_fleets),
  ISS_SrvLenComps = array(100, dim = c(sim_list$n_regions, sim_list$n_yrs,
    sim_list$n_seas, sim_list$n_sexes, sim_list$n_srv_fleets, sim_list$n_sims)),
  ln_SrvLen_theta = array(log(1), dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_srv_fleets)),
  ln_SrvLen_theta_agg = rep(log(1), sim_list$n_srv_fleets),
  SrvLen_corr_pars_agg = rep(0.01, sim_list$n_srv_fleets),
  SrvLen_corr_pars = array(0.01, dim = c(sim_list$n_regions, sim_list$n_sexes,
    sim_list$n_srv_fleets, 2)),
  SrvLenComps_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_srv_fleets)),
  comp_srvage_pop_like = rep(0, sim_list$n_srv_fleets),
  ISS_SrvAgeComps_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_srv_fleets,
    sim_list$n_sims)),
  ln_SrvAge_pop_theta = array(log(1), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_srv_fleets)),
  ln_SrvAge_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop, sim_list$n_srv_fleets)),
  SrvAge_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_srv_fleets, 2)),
  SrvAge_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop, sim_list$n_srv_fleets)),
  SrvAgeComps_pop_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_srv_fleets)),
  comp_srvlen_pop_like = rep(0, sim_list$n_srv_fleets),
  ISS_SrvLenComps_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_srv_fleets,
    sim_list$n_sims)),
  ln_SrvLen_pop_theta = array(log(1), dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_srv_fleets)),
  ln_SrvLen_pop_theta_agg = array(log(1), dim = c(sim_list$n_pop, sim_list$n_srv_fleets)),
  SrvLen_pop_corr_pars = array(0.01, dim = c(sim_list$n_pop, sim_list$n_regions,
    sim_list$n_sexes, sim_list$n_srv_fleets, 2)),
  SrvLen_pop_corr_pars_agg = array(0.01, dim = c(sim_list$n_pop, sim_list$n_srv_fleets)),
  SrvLenComps_pop_Type = array(2, dim = c(sim_list$n_yrs, sim_list$n_srv_fleets))
)

Arguments

sim_list: Simulation list returned by Setup_Sim_Dim.
srv_sel_input: Survey selectivity array [n_pop x n_regions x n_yrs x n_seas × n_ages × n_sexes × n_srv_fleets × n_sims]. No default; must be provided.
ObsSrvIdx_SE: Lognormal observation error SD for survey index, array [n_regions × n_yrs × n_seas × n_srv_fleets]. Default: 0.2.
ln_sigmaSrvIdxAA: Log-scale observation error for the index at age, `n_ages x n_sexes x n_srv_fleets`. The sex dim is required.
UseSrvIdxAA: Integer array `n_regions x n_yrs x n_seas x n_ages x n_sexes x n_srv_fleets`, `1` where a survey index at age is drawn. The sex dim is required: a data source summed over sexes has its flag in sex slot one.
ObsSrvIdxAA_SE: Reported standard errors shaped like `UseSrvIdxAA`, read only when `SrvIdxAA_sigma_form` asks for them.
SrvIdxAA_Type: Which dims each fleet reports separately: `"agg"`, `"spltRaggS"` (default), `"aggRspltS"` or `"spltRspltS"`.
SrvIdxAA_LikeType: `"lognormal"` (default) or `"normal"`, per fleet.
SrvIdxAA_sigma_form: Where the observation error comes from: `"none"` (default), `"data"`, `"est_additive"` or `"est_quadrature"`.
use_srv_idx_aa: Integer vector `n_srv_fleets`, `1` for fleets whose index at age is drawn.
ObsSrvIdx_pop_SE: As above, but for population-specific indices, array [n_pop × n_regions × n_yrs × n_seas × n_srv_fleets].
srv_q_input: Survey catchability array [n_regions × n_yrs × n_srv_fleets × n_sims]. Default: 1 for all cells.
t_srv: Survey timing as fraction of year or season, array [n_regions × n_seas × n_srv_fleets]. Default: 1.
srv_idx_type: Integer vector [n_srv_fleets] specifying survey index type. Default: all 1 (biomass). Options: 0/“abd” (abundance), 1/“biom” (biomass).
SrvIdx_LikeType: Character or numeric vector, length `n_srv_fleets`. Error structure each fleet's index is drawn under: "lognormal" (0), "normal" (1), or "mvn" (2), matching the estimation model's SrvIdx_LikeType. An mvn fleet draws from SrvIdx_Cov through a common-factor decomposition (see cov_to_factor) instead of ObsSrvIdx_SE, and its population-specific data source stays lognormal. Default: lognormal for every fleet.
SrvIdx_seas_Type, SrvIdx_pop_seas_Type, SrvAgeComps_seas_Type: Whether the operating model reports a survey data source once a season ("spltSeas", the default) or once a year as a season total ("aggSeas"). One value for every survey or one per survey. 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, so an estimation model reading it should mark season one in its Use array and set the matching argument in Setup_Mod_SrvIdx_and_Comps.
SrvIdx_Cov: List with one element per survey fleet holding the fixed covariance over that fleet's fitted index observations, ordered by scanning UseSrvIdx in array order (region fastest, then year, then season). Required for mvn fleets. Default: NULL.
UseSrvIdx: Numeric array [n_regions x n_yrs x n_seas x n_srv_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.
comp_srv_caal_like: Character or numeric vector `n_srv_fleets` giving the conditional age-at-length likelihood per fleet: `"Multinomial"` (0), `"Dirichlet-Multinomial"` (1), or `"none"` (999). The survey twin of `comp_fish_caal_like`, and only these two families exist for CAAL. Default: `"none"` for every fleet.
ISS_Srv_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_srv_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"` switches `do_srv_caal` on. Requires `n_lens`.
ln_Srv_caal_theta: Numeric array. Log overdispersion for the Dirichlet-multinomial, dimensions `n_regions x n_sexes x n_srv_fleets`, read under the split types and ignored under the multinomial. Default: log(1).
ln_Srv_caal_theta_agg: Numeric vector `n_srv_fleets`. The aggregated type's counterpart to `ln_Srv_caal_theta`. Default: log(1).
Srv_caal_Type: Numeric or character array giving the composition structure per year and fleet, dimensions `n_yrs x n_srv_fleets`, with the same codes as `Fish_caal_Type`: `"agg"` (0), `"spltRspltS"` (1), `"spltRjntS"` (2), `"none"` (999). The simulator takes the year by fleet array directly rather than the estimation model's `"CompType_Year_x-y_Fleet_z"` strings. Default: `"none"` throughout.
comp_srvage_like: Integer or character vector [n_srv_fleets] specifying likelihood for survey age compositions. Default: all 0 (multinomial). Options: 0/“Multinomial”, 1/“Dirichlet-Multinomial”, 2/“iid-Logistic-Normal”, 3/“1d-Logistic-Normal”, 4/“2d-Logistic-Normal”.
ISS_SrvAgeComps: Array [n_regions × n_yrs × n_seas × n_sexes × n_srv_fleets × n_sims] of sample sizes or overdispersion for survey age compositions. Default: 100.
ln_SrvAge_theta: Log-scale overdispersion array [n_regions × n_sexes × n_srv_fleets]. Used for likelihoods 1-4. Default: log(1).
ln_SrvAge_theta_agg: Log-scale overdispersion for aggregated survey age compositions, vector [n_srv_fleets]. Default: log(1).
SrvAge_corr_pars_agg: Vector [n_srv_fleets] for aggregated survey age correlations. Only for likelihood 3. Default: 0.01.
SrvAge_corr_pars: Correlation parameters array [n_regions × n_sexes × n_srv_fleets × 2] (age AR1, sex). Only for likelihoods 3-4. Default: 0.01.
SrvAgeComps_Type: Array [n_yrs × n_srv_fleets] specifying composition structure. Default: 2 (split by region, joint sexes). Options: 0/“agg”, 1/“spltRspltS”, 2/“spltRjntS”, 999/“none”.
comp_srvlen_like: Integer or character vector [n_srv_fleets] specifying likelihood for survey length compositions. Default: all 0.
ISS_SrvLenComps: Array [n_regions × n_yrs × n_seas × n_sexes × n_srv_fleets × n_sims] of sample sizes or overdispersion for survey length compositions. Default: 100.
ln_SrvLen_theta: Log-scale overdispersion array [n_regions × n_sexes × n_srv_fleets]. Default: log(1).
ln_SrvLen_theta_agg: Vector [n_srv_fleets] for aggregated length composition overdispersion. Default: log(1).
SrvLen_corr_pars_agg: Vector [n_srv_fleets] for aggregated length composition correlations. Default: 0.01.
SrvLen_corr_pars: Array [n_regions × n_sexes × n_srv_fleets × 2] correlation parameters for length comps. Default: 0.01.
SrvLenComps_Type: Array [n_yrs × n_srv_fleets] specifying length composition structure. Default: 2.
comp_srvage_pop_like: Integer or character vector [n_srv_fleets] specifying likelihood for population-specific survey age compositions. Default: all 0.
ISS_SrvAgeComps_pop: Array [n_pop × n_regions × n_yrs × n_seas × n_sexes × n_srv_fleets × n_sims] of population-specific sample sizes or overdispersion. Default: 100.
ln_SrvAge_pop_theta: Log-scale overdispersion array [n_pop × n_regions × n_sexes × n_srv_fleets]. Default: log(1).
ln_SrvAge_pop_theta_agg: Array [n_pop × n_srv_fleets] for aggregated population-specific overdispersion. Default: log(1).
SrvAge_pop_corr_pars: Array [n_pop × n_regions × n_sexes × n_srv_fleets × 2] correlation parameters (age AR1, sex) for population-specific age compositions. Default: 0.01.
SrvAge_pop_corr_pars_agg: Array [n_pop × n_srv_fleets] for aggregated population-specific age correlations. Default: 0.01.
SrvAgeComps_pop_Type: Array [n_yrs × n_srv_fleets] specifying population-specific age composition structure. Default: 2.
comp_srvlen_pop_like: Integer or character vector [n_srv_fleets] specifying likelihood for population-specific survey length compositions. Default: all 0.
ISS_SrvLenComps_pop: Array [n_pop × n_regions × n_yrs × n_seas × n_sexes × n_srv_fleets × n_sims] of population-specific sample sizes or overdispersion. Default: 100.
ln_SrvLen_pop_theta: Array [n_pop × n_regions × n_sexes × n_srv_fleets] log-scale overdispersion for population-specific lengths. Default: log(1).
ln_SrvLen_pop_theta_agg: Array [n_pop × n_srv_fleets] for aggregated population-specific length overdispersion. Default: log(1).
SrvLen_pop_corr_pars: Array [n_pop × n_regions × n_sexes × n_srv_fleets × 2] correlation parameters for population-specific length comps. Default: 0.01.
SrvLen_pop_corr_pars_agg: Array [n_pop × n_srv_fleets] for aggregated population-specific length correlations. Default: 0.01.
SrvLenComps_pop_Type: Array [n_yrs × n_srv_fleets] specifying population-specific length composition structure. Default: 2.

Value

The input sim_list with survey-related fields appended: $srv_sel, $srv_q, $ObsSrvIdx_SE, $ObsSrvIdx_pop_SE, $t_srv, $srv_idx_type, $comp_srvage_like, $ISS_SrvAgeComps, $ln_SrvAge_theta, $ln_SrvAge_theta_agg, $SrvAge_corr_pars_agg, $SrvAge_corr_pars, $SrvAgeComps_Type, $comp_srvlen_like, $ISS_SrvLenComps, $ln_SrvLen_theta, $ln_SrvLen_theta_agg, $SrvLen_corr_pars_agg, $SrvLen_corr_pars, $SrvLenComps_Type, $comp_srvage_pop_like, $ISS_SrvAgeComps_pop, $ln_SrvAge_pop_theta, $ln_SrvAge_pop_theta_agg, $SrvAge_pop_corr_pars_agg, $SrvAge_pop_corr_pars, $SrvAgeComps_pop_Type, $comp_srvlen_pop_like, $ISS_SrvLenComps_pop, $ln_SrvLen_pop_theta, $ln_SrvLen_pop_theta_agg, $SrvLen_pop_corr_pars_agg, $SrvLen_pop_corr_pars, $SrvLenComps_pop_Type. Character-coded inputs are converted to integer equivalents before storage.

Set up survey parameterization for the operating model simulation

Usage

Arguments

Value

See also