runJitter: Function to run TCSAM2015 multiple times using jittered...

In wStockhausen/rTCSAM2015: A set of tools to set up input for, and interact with output from, a TCSAM2015 model run, as well as to compare TCSAM2015 and rsimTCSAM runs.

Description

This functions runs a TCSAM2015 model multiple times, jittering the initial staarting values to assess model convergence.

Usage

runJitter(os = "osx", path = ".", model = "tcsam2015", path2model = "",
  configFile = "", numRuns = 3, onlyEvalJitter = FALSE,
  in.csv = "jitterInfo.csv", out.csv = "jitterResults.csv", mcmc = FALSE,
  mc.N = 1e+06, mc.save = 1000, mc.scale = 1000, plotResults = FALSE)

Arguments

os	- 'win' or 'mac' or 'osx'
path	- path for model output
model	- TCSAM2015 model executable name
path2model	- path to model executable
configFile	- path to model configuration file
numRuns	- number of jitter runs to make
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
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
plotResults	- T/F to plot final results using ???

Details

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 TCSAM2015 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 modelusing 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.

Value

- 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

wStockhausen/rTCSAM2015 documentation built on May 3, 2019, 7:14 p.m.