Description Usage Arguments Details Value Required Data Optional Data Author(s) References See Also Examples
A catch and indexbased assessment model. Compared to the discrete delaydifference (annual timestep in production and fishing), the delaydifferential model (cDD) is based on continuous recruitment and fishing mortality within a timestep. 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 statespace version (cDD_SS), 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  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(),
...
)

x 
An index for the objects in 
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 
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 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 
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_sigma 
Logical, whether the standard deviation of the index is fixed. 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 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 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.
An object of Assessment
containing objects and output
from TMB.
cDD
: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge
cDD_SS
: Cat, Ind, Mort, L50, vbK, vbLinf, vbt0, wla, wlb, MaxAge
cDD
: steep
cDD_SS
: steep, CV_Ind, sigmaR
Q. Huynh
Hilborn, R., and Walters, C., 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York.
DD_TMB 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 < 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
### Statespace version
### Set recruitment variability SD = 0.6 (since fix_tau = TRUE)
res < cDD_SS(Data = Red_snapper, start = list(tau = 0.6))

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