BaM: Run BaM

In BaM-tools/RBaM: Bayesian Modeling: Estimate a Computer Model and Make Uncertain Predictions

Run BaM

Description

Run BaM.exe

Usage

BaM(
  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,
  workspace = file.path(getwd(), "BaM_workspace"),
  dir.exe = file.path(find.package("RBaM"), "bin"),
  name.exe = "BaM",
  predMaster_fname = "Config_Pred_Master.txt"
)

Arguments

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!
workspace	Character, directory where config and result files are stored.
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.

Value

Nothing: just write config files and runs the executable.

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(mod=M,data=D,run=FALSE,workspace=workspace)

BaM-tools/RBaM documentation built on April 11, 2025, 10:01 p.m.