runMSE: Run a Management Strategy Evaluation

View source: R/runMSE.R

SimulateR Documentation

Run a Management Strategy Evaluation

Description

Functions to run the Management Strategy Evaluation (closed-loop simulation) for a specified operating model

Usage

Simulate(OM = MSEtool::testOM, parallel = FALSE, silent = FALSE)

Project(
  Hist = NULL,
  MPs = NA,
  parallel = FALSE,
  silent = FALSE,
  extended = FALSE,
  checkMPs = TRUE
)

runMSE(
  OM = MSEtool::testOM,
  MPs = NA,
  Hist = FALSE,
  silent = FALSE,
  parallel = FALSE,
  extended = FALSE,
  checkMPs = TRUE
)

Arguments

OM

An operating model object (class OM or class Hist)

parallel

Logical or a named list. Should MPs be run using parallel processing? For runMSE, can also be "sac" to run the entire MSE in parallel using the split-apply-combine technique. See Details for more information.

silent

Should messages be printed out to the console?

Hist

Should model stop after historical simulations? Returns an object of class 'Hist' containing all historical data

MPs

A vector of methods (character string) of class MP

extended

Logical. Return extended projection results? if TRUE, MSE@Misc$extended is a named list with extended data (including historical and projection by area), and extended version of MSE@Hist is returned.

checkMPs

Logical. Check if the specified MPs exist and can be run on SimulatedData?

Details

Running MPs in parallel

For most MPs, running in parallel can actually lead to an increase in computation time, due to the overhead in sending the information over to the cores. Consequently, by default the MPs will not be run in parallel if parallel=TRUE (although other internal code will be run in parallel mode).

To run MPs in parallel, specify a named list with the name of the MP(s) assigned as TRUE. For example,parallel=list(AvC=TRUE) will run the AvC MP in parallel mode.

Split-apply-combine MSE in parallel

Additional savings in computation time can be achieved by running the entire simulation in batches. Individual simulations of the operating model are divided into separate cores using SubCpars, Simulate and Project are applied independently for each core via snowfall::sfClusterApplyLB, and the output (a list of MSE objects) is stitched back together into a single MSE object using joinMSE.

The ideal number of cores will be determined based on the number of simulations and available cores.

There are several issues to look out for when using this split-apply-combine technique:

  • Numerical optimization for depletion may fail in individual cores when OM@cpars$qs is not specified.

  • Length bins should be specified in the operating model in OM@cpars$CAL_bins. Otherwise, length bins can vary by core and create problems when combining into a single object.

  • Compared to non-parallel runs, sampled parameters in the operating model will vary despite the same value in OM@seed.

  • If there is an error in individual cores or while combining the parallel output into a single Hist or MSE object, the list of output (from the cores) will be returned.

Value

Functions return objects of class Hist or MSE

  • Simulate - An object of class Hist

  • Project - An object of class MSE

  • runMSE - An object of class MSE if Hist = TRUE otherwise a class Hist object

Functions

  • Simulate(): Run the Historical Simulations from an object of class OM

  • Project(): Run the Forward Projections

  • runMSE(): Run the Historical Simulations and Forward Projections from an object of class 'OM


MSEtool documentation built on Oct. 19, 2022, 5:29 p.m.