Description Usage Arguments Details Value Functions Required Data Optional Data Note Author(s) References See Also Examples
A simple delaydifference assessment model using a timeseries 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 statespace version, recruitment deviations from the stockrecruit 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 
Stockrecruit 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/statespace 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
: Observationerror 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 119120:6679.
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  #### Observationerror 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
### Statespace 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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