cDD: Continuous Delay-differential assessment model

Description Usage Arguments Details Value Required Data Optional Data Author(s) References See Also Examples

View source: R/assess_cDD.R

Description

A catch and index-based assessment model. Compared to the discrete delay-difference (annual time-step in production and fishing), the delay-differential model (cDD) is based on continuous recruitment and fishing mortality within a time-step. The continuous model works much better for populations with high turnover (e.g. high F or M, continuous reproduction). This model is conditioned on catch and fits to the observed index. In the state-space version (cDD_SS), recruitment deviations from the stock-recruit relationship are estimated.

Usage

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cDD(
  x = 1,
  Data,
  AddInd = "B",
  SR = c("BH", "Ricker"),
  rescale = "mean1",
  start = NULL,
  fix_h = TRUE,
  dep = 1,
  LWT = NULL,
  n_itF = 5L,
  silent = TRUE,
  opt_hess = FALSE,
  n_restart = ifelse(opt_hess, 0, 1),
  control = list(iter.max = 5000, eval.max = 10000),
  ...
)

cDD_SS(
  x = 1,
  Data,
  AddInd = "B",
  SR = c("BH", "Ricker"),
  rescale = "mean1",
  start = NULL,
  fix_h = TRUE,
  fix_sigma = FALSE,
  fix_tau = TRUE,
  dep = 1,
  LWT = NULL,
  n_itF = 5L,
  integrate = FALSE,
  silent = TRUE,
  opt_hess = FALSE,
  n_restart = ifelse(opt_hess, 0, 1),
  control = list(iter.max = 5000, eval.max = 10000),
  inner.control = list(),
  ...
)

Arguments

x

An index for the objects in Data when running in closed loop simulation. Otherwise, equals to 1 when running an assessment.

Data

An object of class Data.

AddInd

A vector of integers or character strings indicating the indices to be used in the model. Integers assign the index to the corresponding index in Data@AddInd, "B" (or 0) represents total biomass in Data@Ind, "VB" represents vulnerable biomass in Data@VInd, and "SSB" represents spawning stock biomass in Data@SpInd.

SR

Stock-recruit function (either "BH" for Beverton-Holt or "Ricker").

rescale

A multiplicative factor that rescales the catch in the assessment model, which can improve convergence. By default, "mean1" scales the catch so that time series mean is 1, otherwise a numeric. Output is re-converted back to original units.

start

Optional list of starting values. Entries can be expressions that are evaluated in the function. See details.

fix_h

Logical, whether to fix steepness to value in Data@steep in the assessment model.

dep

The initial depletion in the first year of the model. A tight prior is placed on the model objective function to estimate the equilibrium fishing mortality corresponding to the initial depletion. Due to this tight prior, this F should not be considered to be an independent model parameter.

LWT

A vector of likelihood weights for each survey.

n_itF

Integer, the number of iterations to solve F conditional on the observed catch.

silent

Logical, passed to MakeADFun, whether TMB will print trace information during optimization. Used for dignostics for model convergence.

opt_hess

Logical, whether the hessian function will be passed to nlminb during optimization (this generally reduces the number of iterations to convergence, but is memory and time intensive and does not guarantee an increase in convergence rate). Ignored if integrate = TRUE.

n_restart

The number of restarts (calls to nlminb) in the optimization procedure, so long as the model hasn't converged. The optimization continues from the parameters from the previous (re)start.

control

A named list of parameters regarding optimization to be passed to nlminb.

...

Additional arguments (not currently used).

fix_sigma

Logical, whether the standard deviation of the index is fixed. If TRUE, sigma is fixed to value provided in start (if provided), otherwise, value based on Data@CV_Ind.

fix_tau

Logical, the standard deviation of the recruitment deviations is fixed. If TRUE, tau is fixed to value provided in start (if provided), otherwise, equal to 1.

integrate

Logical, whether the likelihood of the model integrates over the likelihood of the recruitment deviations (thus, treating it as a state-space variable). Otherwise, recruitment deviations are penalized parameters.

inner.control

A named list of arguments for optimization of the random effects, which is passed on to newton via MakeADFun.

Details

To provide starting values for cDD, a named list can be provided for R0 (unfished recruitment) and and h (steepness) via the start argument (see example).

For cDD_SS, additional start values can be provided for and sigma and tau, the standard deviation of the index and recruitment variability, respectively.

Value

An object of Assessment containing objects and output from TMB.

Required Data

Optional Data

Author(s)

Q. Huynh

References

Hilborn, R., and Walters, C., 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York.

See Also

DD_TMB plot.Assessment summary.Assessment retrospective profile make_MP

Examples

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#### Observation-error delay difference model
res <- cDD(Data = DLMtool::Red_snapper)

# Provide starting values
start <- list(R0 = 1, h = 0.95)
res <- cDD(Data = DLMtool::Red_snapper, start = start)

summary(res@SD) # Parameter estimates

### State-space version
### Set recruitment variability SD = 0.6 (since fix_tau = TRUE)
res <- cDD_SS(Data = Red_snapper, start = list(tau = 0.6))

tcarruth/MSEtool documentation built on Sept. 16, 2020, 5:59 a.m.