# SBT simple MP

`SBT1.Rd`

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).

## Usage

```
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)
```

## Arguments

- 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

## Value

An object of class `Rec-class`

with the `TAC`

slot populated with a numeric vector of length `reps`

## Details

For `SBT1`

the TAC is calculated as:
$$\textrm{TAC}_y =
\left\{\begin{array}{ll}
C_{y-1} (1+K_2 \left| \lambda \right| ) & \textrm{if } \lambda \geq 0 \\
C_{y-1} (1-K_1 \left| \lambda \right| ^\gamma) & \textrm{if } \lambda < 0\\
\end{array}\right.
$$
where \(\lambda\) is the slope of index over the last `yrmsth`

years, and
\(K_1\), \(K_2\), and \(\gamma\) are arguments to the MP.

For `SBT2`

the TAC is calculated as:
$$\textrm{TAC}_y = 0.5 (C_{y-1} + C_\textrm{targ}\delta)$$
where \(C_{y-1}\) is catch in the previous year, \(C_{\textrm{targ}}\)
is a target catch (`Data@Cref`

), and :
$$\delta=
\left\{\begin{array}{ll}
R^{1-\textrm{epsR}} & \textrm{if } R \geq 1 \\
R^{1+\textrm{epsR}} & \textrm{if } R < 1 \\
\end{array}\right.
$$
where \(\textrm{epsR}\) is a control parameter and:
\(R = \frac{\bar{r}}{\phi}\)
where \(\bar{r}\) is mean recruitment over last `tauR`

years and \(\phi\)
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.

## Required Data

See `Data-class`

for information on the `Data`

object

`SBT1`

: Cat, Ind, Year

`SBT2`

: Cat, Cref, Rec

## Rendered Equations

See Online Documentation for correctly rendered equations

## Examples

```
SBT1(1, Data=MSEtool::SimulatedData, plot=TRUE)
#> TAC (median)
#> 2119.745
SBT2(1, Data=MSEtool::SimulatedData, plot=TRUE)
#> TAC (median)
#> 1408.56
```