SBT1 {DLMtool} | R Documentation |
An MP that makes incremental adjustments to TAC recommendations based on the apparent trend in CPUE, a an MP that makes incremental adjustments to TAC recommendations based on index levels relative to target levels (BMSY/B0) and catch levels relative to target levels (MSY).
SBT1( x, Data, reps = 100, plot = FALSE, yrsmth = 10, k1 = 1.5, k2 = 3, gamma = 1 ) SBT2(x, Data, reps = 100, plot = FALSE, epsR = 0.75, tauR = 5, gamma = 1)
x |
A position in the data object |
Data |
A data object |
reps |
The number of stochastic samples of the MP recommendation(s) |
plot |
Logical. Show the plot? |
yrsmth |
The number of years for evaluating trend in relative abundance indices |
k1 |
Control parameter |
k2 |
Control parameter |
gamma |
Control parameter |
epsR |
Control parameter |
tauR |
Control parameter |
For SBT1
the TAC is calculated as:
where λ is the slope of index over the last yrmsth
years, and
K_1, K_2, and γ are arguments to the MP.
For SBT2
the TAC is calculated as:
\textrm{TAC}_y = 0.5 (C_{y-1} + C_\textrm{targ}δ)
where C_{y-1} is catch in the previous year, C_{\textrm{targ}}
is a target catch (Data@Cref
), and :
where \textrm{epsR} is a control parameter and:
R = \frac{\bar{r}}{φ}
where \bar{r} is mean recruitment over last tauR
years and φ
is mean recruitment over last 10 years.
This isn't exactly the same as the proposed methods and is stochastic in this implementation. The method doesn't tend to work too well under many circumstances possibly due to the lack of 'tuning' that occurs in the real SBT assessment environment. You could try asking Rich Hillary at CSIRO about this approach.
An object of class Rec-class
with the TAC
slot populated with a numeric vector of length reps
SBT1
: Simple SBT MP
SBT2
: Complex SBT MP
See Data-class
for information on the Data
object
SBT1
: Cat, Ind, Year
SBT2
: Cat, Cref, Rec
See Online Documentation for correctly rendered equations
T. Carruthers
http://www.ccsbt.org/site/recent_assessment.php
SBT1(1, Data=MSEtool::SimulatedData, plot=TRUE) SBT2(1, Data=MSEtool::SimulatedData, plot=TRUE)