Description Usage Arguments Details Value Functions Required Data Optional Data Note Author(s) References See Also Examples
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.
1 2 3 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 | 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(),
...
)
|
x |
An index for the objects in |
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 |
rescale |
A multiplicative factor that rescales the catch in the assessment model, which
can improve convergence. By default, |
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 |
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 |
opt_hess |
Logical, whether the hessian function will be passed to |
n_restart |
The number of restarts (calls to |
control |
A named list of parameters regarding optimization to be passed to
|
... |
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 |
fix_tau |
Logical, the standard deviation of the recruitment deviations is fixed. If |
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 |
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.
An object of Assessment
containing objects and output from TMB.
DD_TMB
: Observation-error only model
DD_TMB
: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge
DD_SS
: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge
DD_TMB
: steep
DD_SS
: steep, CV_Cat
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.
T. Carruthers & Z. Siders. Zach Siders coded the TMB function.
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.
plot.Assessment summary.Assessment retrospective profile make_MP
1 2 3 4 5 6 7 8 9 10 11 12 | #### 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))
|
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