RBaM (version 1.1.1)

BaM: Run BaM

Description

Run BaM.exe

Usage

BaM(
  workspace,
  mod,
  data,
  remnant = rep(list(remnantErrorModel()), mod$nY),
  mcmc = mcmcOptions(),
  cook = mcmcCooking(),
  summary = mcmcSummary(),
  residuals = residualOptions(),
  pred = NULL,
  doCalib = TRUE,
  doPred = FALSE,
  na.value = -9999,
  run = TRUE,
  preClean = FALSE,
  dir.exe = .BAM_PATH,
  name.exe = "BaM",
  predMaster_fname = "Config_Pred_Master.txt",
  stout = ""
)

Value

Nothing: just write config files and runs the executable.

Arguments

workspace

Character, directory where config and result files are stored.

mod

model object, the model to be calibrated

data

dataset object, calibration data

remnant

list of remnantErrorModel objects. WARNING: make sure you use a list of length mod$nY (even if mod$nY=1!)

mcmc

mcmcOptions object, MCMC simulation number and options

cook

mcmcCooking object, properties of MCMC cooking (burn and slice)

summary

mcmcSummary object, properties of MCMC summary

residuals

residualOptions object, properties of residual analysis

pred

list of prediction objects, properties of prediction experiments

doCalib

Logical, do Calibration? (mcmc+cooking+summary+residuals)

doPred

Logical, do Prediction?

na.value

numeric, value used for NAs when writing the dataset in BaM format

run

Logical, run BaM? if FALSE, just write config files.

preClean

Logical, start by cleaning up workspace? Be careful, this will delete all files in the workspace, including old results!

dir.exe

Character, directory where BaM executable stands.

name.exe

Character, name of the executable without extension ('BaM' by default).

predMaster_fname

Character, name of configuration file pointing to all prediction experiments.

stout

Character string, standard output (see ?system2). In particular, stout="" (default) shows BaM messages in the console, stout=NULL discards BaM messages, stout='log.txt' saves BaM messages in file "log.txt".

Examples

# Fitting a rating curve - see https://github.com/BaM-tools/RBaM
workspace=tempdir()
D=dataset(X=SauzeGaugings['H'],Y=SauzeGaugings['Q'],Yu=SauzeGaugings['uQ'],data.dir=workspace)
# Parameters of the low flow section control: activation stage k, coefficient a and exponent c
k1=parameter(name='k1',init=-0.5,prior.dist='Uniform',prior.par=c(-1.5,0))
a1=parameter(name='a1',init=50,prior.dist='LogNormal',prior.par=c(log(50),1))
c1=parameter(name='c1',init=1.5,prior.dist='Gaussian',prior.par=c(1.5,0.05))
# Parameters of the high flow channel control: activation stage k, coefficient a and exponent c
k2=parameter(name='k2',init=1,prior.dist='Gaussian',prior.par=c(1,1))
a2=parameter(name='a2',init=100,prior.dist='LogNormal',prior.par=c(log(100),1))
c2=parameter(name='c2',init=1.67,prior.dist='Gaussian',prior.par=c(1.67,0.05))
# Define control matrix: columns are controls, rows are stage ranges.
controlMatrix=rbind(c(1,0),c(0,1))
# Stitch it all together into a model object
M=model(ID='BaRatin',
        nX=1,nY=1, # number of input/output variables
        par=list(k1,a1,c1,k2,a2,c2), # list of model parameters
        xtra=xtraModelInfo(object=controlMatrix)) # use xtraModelInfo() to pass the control matrix
# Call BaM to write configuration files. To actually run BaM, use run=TRUE,
# but BaM executable needs to be downloaded first (use downloadBaM())
BaM(workspace=workspace,mod=M,data=D,run=FALSE)