Assessment emulator as a shortcut to model fitting in closed-loop simulation
Source:R/assess_shortcut.R
Shortcut.RdFunctions (class Assessment) that emulate a stock assessment by sampling the operating model biomass, abundance, and
fishing mortality (with observation error, autocorrelation, and bias) instead of fitting a model. This output can then
be passed onto a harvest control rule (HCR function). Shortcut is the base function that samples the OM with an error
distribution. Shortcut2, the more preferable option, fits SCA in the last historical year of the operating
model, estimates the error parameters using a vector autoregressive model of the residuals, and then generates model "estimates"
using predict.varest. Perfect assumes no error in the assessment model and is useful for comparing the behavior of
different harvest control rules. To utilize the shortcut method in closed-loop simulation, use make_MP with these functions as
the Assessment model. N.B. the functions do not work with runMSE(parallel = TRUE) for MSEtool v3.4.0 and earlier.
Arguments
- x
An index for the objects in
Datawhen running in runMSE. Otherwise, equals to 1 When running an assessment interactively.- Data
An object of class Data.
- method
Indicates where the error in the OM is located. For "B", OM biomass is directly sampled with error. For "N", OM abundance-at-age is sampled and biomass subsequently calculated. For "RF", recruitment and F are sampled to calculate abundance and biomass. There is no error in biological parameters for "N" and "RF". By default, "B" is used for
Shortcutand "N" forShortcut2.- B_err
If
method = "B", a vector of length three that specifies the standard deviation (in logspace), autocorrelation, and bias (1 = unbiased) for biomass.- N_err
Same as B_err, but for abundance when
method = "N".- R_err
Same as B_err, but for recruitment when
method = "RF".- F_err
Same as B_err. Always used regardless of
methodto report F and selectivity for HCR.- VAR_model
An object returned by VAR to generate emulated assessment error. Used by
Shortcut2.- ...
Other arguments (not currently used).
- SCA_args
Additional arguments to pass to SCA. Currently, arguments
SRandvulnerabilityare obtained from the operating model.- VAR_args
Additional arguments to pass to VAR. By default, argument
type = "none"(stationary time series with mean zero is assumed).
Value
An object of class Assessment.
Details
Currently there is no error in FMSY (frequently the target F in the HCR).
See Wiedenmann et al. (2015) for guidance on the magnitude of error for the shortcut emulator.
References
Wiedenmann, J., Wilberg, M.J., Sylvia, A., and Miller, T.J. 2015. Autocorrelated error in stock assessment estimates: Implications for management strategy evaluation. Fisheries Research 172: 325-334.
Examples
Shortcut_4010 <- make_MP(Shortcut, HCR40_10)
Shortcut_Nerr <- make_MP(Shortcut, HCR40_10, method = "N", N_err = c(0.1, 0.1, 1)) # Highly precise!
# Fits SCA first and then emulate it in the projection period
Shortcut2_4010 <- make_MP(Shortcut2, HCR40_10)
# \donttest{
# Compare the shortcut method vs. fitting an SCA model with a 40-10 control rule
MSE <- runMSE(testOM, MPs = c("Shortcut_4010", "SCA_4010"))
#> ℹ Checking OM for completeness
#> ✔ Loading operating model
#> ✔ Optimizing for user-specified movement
#> ✔ Calculating MSY reference points for each year
#> ✔ Optimizing for user-specified depletion in last historical year
#> ✔ Calculating historical stock and fishing dynamics
#> ✔ Calculating per-recruit reference points
#> ✔ Calculating B-low reference points
#> ✔ Calculating reference yield - best fixed F strategy
#> ✔ Simulating observed data
#> ✔ Running forward projections
#> ✔ 1 / 2 Running MSE for Shortcut_4010
#>
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#>
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#>
# }
# Compare the performance of three HCRs
Perfect_4010 <- make_MP(Perfect, HCR40_10)
Perfect_6020 <- make_MP(Perfect, HCR60_20)
Perfect_8040MSY <- make_MP(Perfect, HCR_ramp, OCP_type = "SSB_SSBMSY", TOCP = 0.8, LOCP = 0.4)
# \donttest{
MSE <- runMSE(testOM, MPs = c("Perfect_4010", "Perfect_6020", "Perfect_8040MSY"))
#> ℹ Checking OM for completeness
#> ✔ Loading operating model
#> ✔ Optimizing for user-specified movement
#> ✔ Calculating MSY reference points for each year
#> ✔ Optimizing for user-specified depletion in last historical year
#> ✔ Calculating historical stock and fishing dynamics
#> ✔ Calculating per-recruit reference points
#> ✔ Calculating B-low reference points
#> ✔ Calculating reference yield - best fixed F strategy
#> ✔ Simulating observed data
#> ✔ Running forward projections
#> ✔ 1 / 3 Running MSE for Perfect_4010
#>
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#>
#> ✔ 2 / 3 Running MSE for Perfect_6020
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#>
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#>
# }