DD_TMB: Delay - Difference Stock Assessment in TMB

In tcarruth/MSEtool: Management Strategy Evaluation Toolkit

Description

A simple delay-difference assessment model using a time-series of catches and a relative abundance index and coded in TMB. The model can be conditioned on either (1) effort and estimates predicted catch or (2) catch and estimates a predicted index. In the state-space version, recruitment deviations from the stock-recruit relationship are estimated.

Usage

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

DD_SS(
  x = 1,
  Data,
  condition = c("catch", "effort"),
  AddInd = "B",
  SR = c("BH", "Ricker"),
  rescale = "mean1",
  start = NULL,
  fix_h = TRUE,
  fix_sd = FALSE,
  fix_tau = TRUE,
  dep = 1,
  LWT = NULL,
  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.
condition	A string to indicate whether to condition the model on catch or effort (ratio of catch and index).
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 exploitation rate that corresponds 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.
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_sd	Logical, whether the standard deviation of the data in the likelihood (index for conditioning on catch or catch for conditioning on effort). If TRUE, the SD is fixed to value provided in start (if provided), otherwise, value based on either Data@CV_Cat or 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 random effects/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 DD_TMB, a named list can be provided for R0 (virgin recruitment), h (steepness), and q (catchability coefficient) via the start argument (see example).

For DD_SS, additional start values can be provided for and omega and tau, the standard deviation of the catch and recruitment variability, respectively.

Value

An object of Assessment containing objects and output from TMB.

Functions

• DD_TMB: Observation-error only model

Required Data

• DD_TMB: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge

• DD_SS: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge

Optional Data

• DD_TMB: steep

• DD_SS: steep, CV_Cat

Note

Similar to many other assessment models, the model depends on assumptions such as stationary productivity and proportionality between the abundance index and real abundance. Unsurprisingly the extent to which these assumptions are violated tends to be the biggest driver of performance for this method.

Author(s)

T. Carruthers & Z. Siders. Zach Siders coded the TMB function.

References

Carruthers, T, Walters, C.J,, and McAllister, M.K. 2012. Evaluating methods that classify fisheries stock status using only fisheries catch data. Fisheries Research 119-120:66-79.

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

See Also

plot.Assessment summary.Assessment retrospective profile make_MP

Examples

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

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

summary(res@SD) # Parameter estimates

### State-space version
### Set recruitment variability SD = 0.3 (since fix_tau = TRUE)
res <- DD_SS(Data = Red_snapper, start = list(tau = 0.3))

tcarruth/MSEtool documentation built on Oct. 19, 2020, 6:09 a.m.