StochasticSRA: Stochastic SRA construction of operating models
In zanbi/DLMtool: Data-Limited Methods Toolkit

Description Usage Arguments Value Author(s) References Examples

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.

StochasticSRA(
  OM,
  CAA,
  Chist,
  Ind = NA,
  ML = NA,
  CAL = NA,
  mulen = NA,
  wts = c(1, 1, 0.5, 0.1, 1),
  Jump_fac = 1,
  nits = 4000,
  burnin = 500,
  thin = 10,
  ESS = 300,
  MLsd = 0.1,
  ploty = T,
  nplot = 6,
  SRAdir = NA
)

`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
`Ind`	A vector of historical abundance index observations (assumed proportional to SSB)
`ML`	A vector of historical mean length (in catch) observations
`CAL`	A matrix of nyears (row) by n length bins (columns) of catch at length samples
`mulen`	A vector mean length by length bin, a vector the same as the number of columns of CAL
`wts`	A vector of relative weights for the likelihood functions of CAA, Chist, Ind, ML and CAL
`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
`MLsd`	The lognormal sd of the mean length observations
`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

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

T. Carruthers (Canadian DFO grant)

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.

## 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)

## End(Not run)