The RCM (Rapid Conditioning Model) returns a single-fleet operating model, implying constant effort among fleets for projections. Here, we convert the single-fleet OM to a multi-fleet OM, preserving the multiple fleet structure used in the conditioning model for projections. This allows for testing management procedures that explicitly specify fleet allocation in the management advice.
Examples
data(pcod)
mat_ogive <- pcod$OM@cpars$Mat_age[1, , 1]
OM <- MSEtool::SubCpars(pcod$OM, 1:3)
#> ✔ Removing simulations: 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
#> ✔ Set OM@nsim = 3
out <- RCM(OM = pcod$OM, data = pcod$data,
condition = "catch", mean_fit = TRUE,
selectivity = "free", s_selectivity = rep("SSB", ncol(pcod$data@Index)),
start = list(vul_par = matrix(mat_ogive, length(mat_ogive), 1)),
map = list(vul_par = matrix(NA, length(mat_ogive), 1),
log_early_rec_dev = rep(1, pcod$OM@maxage)),
prior = pcod$prior)
#> ℹ Checking data...
#> ✔ 1 fleet(s) detected.
#> ✔ RCM is conditioned on:
#> ✔ Fleet 1: catch
#> ✔ 65 years of data detected.
#> ✔ First year in model: 1956
#> ✔ Last year in model: 2020
#> ✔ 5 survey(s) detected.
#> ✔ Checking OM and getting biological parameters...
#> ✔ Mean weight data found.
#> ✔ Maximum F in RCM will be 3. OM@maxF is also updated.
#>
#> ℹ No fishery length or age compositions were provided. Selectivity is fixed to values from OM.
#>
#> ℹ Fishery selectivity setup:
#> ℹ Fleet 1: individual parameters at age (free)
#>
#> ℹ Index selectivity setup:
#> ℹ Index 1: spawning biomass
#> ℹ Index 2: spawning biomass
#> ℹ Index 3: spawning biomass
#> ℹ Index 4: spawning biomass
#> ℹ Index 5: spawning biomass
#>
#> ✔ Beverton-Holt stock-recruitment relationship used.
#> ✔ Prior for q found.
#> ℹ Fitting model (48 simulations) ...
#> ℹ Generating additional model fit from mean values of parameters in the operating model...
#> ✔ Updating operating model:
#>
#> ✔ Range of unfished age-0 recruitment (OM@cpars$R0): 6383.46 - 13782.9
#> ✔ Range of initial spawning depletion: 0.44 - 1.45
#> ✔ Range of spawning depletion (OM@cpars$D): 0.14 - 0.45
#> ✔ Historical F set with OM@cpars$Find and OM@cpars$qs.
#> ✔ Annual selectivity at age set in OM@cpars$V. Projection period uses selectivity of last historical year.
#> ✔ RCMdata length bins will be added to OM.
#> ✔ Recruitment standard deviation set in OM@cpars$Perr: 0.8 - 0.8
#> ✔ Historical recruitment deviations set in OM@cpars$Perr_y.
#> ✔ Range of recruitment autocorrelation OM@AC: 0.21 - 0.31
#> ✔ Future recruitment deviations in OM@cpars$Perr_y sampled with autocorrelation.
#> ✔ Growth, maturity, natural mortality, and stock recruit parameters from RCM are set in OM@cpars.
#>
#> ℹ Adding some RCMdata inputs into OM@cpars$Data:
#>
#> ✔ Historical catch data added to OM@cpars$Data@Cat.
#> ✔ Historical indices added to OM@cpars$Data@AddInd.
#> ✔ Complete.
MOM <- RCM2MOM(out)