DLMtool (version 4.4.1)

StochasticSRA2: Stochastic SRA construction of operating models

Description

Specify an operating model, using catch composition data and a historical catch series. Returns and operating model with depletion (D), selectivity parameters (L5, LFS) and effort trajectory (Effyears, EffLower, EffUpper) filled.

Usage

StochasticSRA2(OM, CAA, Chist, Cobs = 0.1, sigmaR = 0.5, Umax = 0.9,
  nsim = 48, proyears = 50, Jump_fac = 1, nits = 4000, burnin = 500,
  thin = 10, ESS = 300, ploty = T, nplot = 6, SRAdir = NA)

Arguments

OM

An operating model object with M, growth, stock-recruitment and maturity parameters specified.

CAA

A matrix nyears (rows) by nages (columns) of catch at age (age 1 to maxage in length)

Chist

A vector of historical catch observations (nyears long) going back to unfished conditions

Cobs

A numeric value representing catch observation error as a log normal sd

sigmaR

A numeric value representing the prior standard deviation of log space recruitment deviations

Umax

A numeric value representing the maximum harvest rate for any age class (rejection of sims where this occurs)

nsim

The number desired draws of parameters / effort trajectories

proyears

The number of projected MSE years

Jump_fac

A multiplier of the jumping distribution variance to increase acceptance (lower Jump_fac) or decrease acceptance rate (higher Jump_fac)

nits

The number of MCMC iterations

burnin

The number of initial MCMC iterations to discard

thin

The interval over which MCMC samples are extracted for use in graphing / statistics

ESS

Effective sample size - the weighting of the catch at age data

ploty

Do you want to see diagnostics plotted?

nplot

how many MCMC samples should be plotted in convergence plots?

SRAdir

A directory where the SRA diagnostics / fit are stored

Value

A list with three positions. Position 1 is the filled OM object, position 2 is the custompars data.frame that may be submitted as an argument to runMSE() and position 3 is the matrix of effort histories [nyears x nsim] vector of objects of classclassy

References

Walters, C.J., Martell, S.J.D., Korman, J. 2006. A stochastic approach to stock reduction analysis. Can. J. Fish. Aqua. Sci. 63:212-213.

Examples

# NOT RUN {
setup()
sim<-SRAsim(testOM,patchy=0.8)
CAA<-sim$CAA
Chist<-sim$Chist
testOM<-StochasticSRA(testOM,CAA,Chist,nsim=30,nits=1000)
runMSE(testOM)
# }