DDe: Effort-based Delay - Difference Stock Assessment

Description Usage Arguments Details Value Functions Required Data Rendered Equations See Also Examples

View source: R/MPs_Input.R

Description

A simple delay-difference assessment with UMSY and MSY as leading parameters that estimates E_{\textrm{MSY}} using a time-series of catches and a relative abundance index.

Usage

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DDe(x, Data, reps = 100, plot = FALSE)

DDes(x, Data, reps = 100, plot = FALSE, LB = 0.9, UB = 1.1)

DDe75(x, Data, reps = 100, plot = FALSE)

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?

LB

The lowest permitted factor of previous fishing effort

UB

The highest permitted factor of previous fishing effort

Details

This DD model is observation error only and has does not estimate process error (recruitment deviations). Assumption is that knife-edge selectivity occurs at the age of 50 models it depends on a whole host of dubious assumptions such as temporally 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.

The method is conditioned on effort and estimates catch. The effort is calculated as the ratio of catch and index. Thus, to get a complete effort time series, a full time series of catch and index is also needed. Missing values are linearly interpolated.

A detailed description of the delay-difference model can be found in Chapter 9 of Hilborn and Walters (1992).

Value

An object of class Rec with the TAE slot(s) populated

Functions

Required Data

See Data for information on the Data object

DDe: Cat, Ind, L50, MPeff, MaxAge, Mort, vbK, vbLinf, vbt0, wla, wlb

Rendered Equations

See Online Documentation for correctly rendered equations

See Also

Other Delay-Difference MPs: DD

Examples

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DLMtool documentation built on Dec. 6, 2019, 9:06 a.m.