inst/examples/plotTimeSeriesResultsHelp.R

In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

# Create input data for the model
# see help for the VSEM model
PAR <- VSEMcreatePAR(1:1000)

# load reference parameter definition (upper, lower prior)
refPars <- VSEMgetDefaults()
# this adds one additional parameter for the likelihood standard deviation (see below)
refPars[12,] <- c(2, 0.1, 4) 
rownames(refPars)[12] <- "error-sd"
head(refPars)

# create some simulated test data 
# generally recommended to start with simulated data before moving to real data
referenceData <- VSEM(refPars$best[1:11], PAR) # model predictions with reference parameters  
referenceData[,1] = 1000 * referenceData[,1] 
# this adds the error - needs to conform to the error definition in the likelihood
obs <- referenceData + rnorm(length(referenceData), sd = refPars$best[12])

parSel = c(1:6, 12)

# here is the likelihood 
likelihood <- function(par, sum = TRUE){
  # set parameters that are not calibrated on default values 
  x = refPars$best
  x[parSel] = par
  predicted <- VSEM(x[1:11], PAR) # replace here VSEM with your model 
  predicted[,1] = 1000 * predicted[,1] # this is just rescaling
  diff <- c(predicted[,1:4] - obs[,1:4]) # difference betweeno observed and predicted
  # univariate normal likelihood. Note that there is a parameter involved here that is fit
  llValues <- dnorm(diff, sd = x[12], log = TRUE)  
  if (sum == FALSE) return(llValues)
  else return(sum(llValues))
}

# optional, you can also directly provide lower, upper in the createBayesianSetup, see help
prior <- createUniformPrior(lower = refPars$lower[parSel], 
                            upper = refPars$upper[parSel], best = refPars$best[parSel])

bayesianSetup <- createBayesianSetup(likelihood, prior, names = rownames(refPars)[parSel])

# settings for the sampler, iterations should be increased for real applicatoin
settings <- list(iterations = 2000, nrChains = 2)

\dontrun{

out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "DEzs", settings = settings)

# Posterior predictive simulations

# Create a prediction function
createPredictions <- function(par){
  # set the parameters that are not calibrated on default values 
  x = refPars$best
  x[parSel] = par
  predicted <- VSEM(x[1:11], PAR) # replace here VSEM with your model 
  return(predicted[,1] * 1000)
}

# Create an error function
createError <- function(mean, par){
  return(rnorm(length(mean), mean = mean, sd = par[7]))
}


# plot prior predictive distribution and prior predictive simulations
plotTimeSeriesResults(sampler = out, model = createPredictions, observed = obs[,1],
                      error = createError, prior = TRUE, main = "Prior predictive")
  
}