Description Usage Arguments Details Value

This functions runs a TCSAM02 model multiple times, jittering the initial staarting values to assess model convergence.

1 2 3 4 5 6 7 8 | ```
runJitter(os = "osx", path = ".", model = "tcsam02",
path2model = "", configFile = "", numRuns = 3, minPhase = 1,
onlyEvalJitter = FALSE, in.csv = "jitterInfo.csv",
out.csv = "jitterResults.csv", calcOFL = FALSE,
calcOFLJitter = FALSE, mcmc = FALSE, mc.N = 1e+06,
mc.save = 1000, mc.scale = 1000, saveResults = FALSE,
cleanup = TRUE, keepFiles = c("tmp.sh", "tcsam02.par"),
cleanupAll = FALSE)
``` |

`os` |
- 'win', 'mac', 'osx', or 'linux' |

`path` |
- path for model output |

`model` |
- TCSAM02 model executable name |

`path2model` |
- path to model executable |

`configFile` |
- path to model configuration file |

`numRuns` |
- number of jitter runs to make |

`minPhase` |
- phase in which to start optimization |

`onlyEvalJitter` |
- flag (T/F) to only evaluate a (previous) set of jitter runs, not make new runs |

`in.csv` |
- filename for jitter info (seed, obj fun value) from ADMB model run |

`out.csv` |
- filename for jittered results |

`calcOFL` |
- flag (T/F) to perform OFL calculations for "best" model |

`calcOFLJitter` |
- flag (T/F) to perform OFL calculations while jittering |

`mcmc` |
- flag (T/F) to run mcmc on "best" model |

`mc.N` |
- number of mcmc iterations to make |

`mc.save` |
- number of iterations to skip when saving mcmc calculations |

`mc.scale` |
- number of iterations to adjust scale for mcmc calculations |

`saveResults` |
- T/F to save final results to best/ModelResults.RData |

`cleanup` |
- T/F to clean up SOME model output files after each run |

`keepFiles` |
- vector of file names to keep, not clean up, after model run |

`cleanupAll` |
- T/F to clean up ALMOST ALL model output files after each run |

For each model run, this function creates a shell script ('./tmp.sh') in the working directory and uses it to run the ADMB version of the TCSAM02 model. Initial model parameters are jittered based on the system clock time as a seed to the random number generator. The seed and final objective function value are saved for each model run in a csv file (the value of out.csv).

When all the models requested have been run, the function determines the seed associated with the 1st model run that yielded the smallest value for the objective function and re-runs the model using this seed to re-create the model run resulting in the minimum objectve function to recreate the model output files. The final model run is done estimating the hessian, so standard deviations for estimated model parameters are available in the .std file.

- list w/ 4 elements: imx - index of (1st) smallest value for the objective function seed - seed resulting in the smallest objective function par - dataframe with par results from run w/ smallest objective function objFuns - vector of objective function values from all model runs parList - list of par dataframes for each model run

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