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