bayestransmission (version 0.1.0)

runMCMC: Run Bayesian Transmission MCMC

Description

Run Bayesian Transmission MCMC

Usage

runMCMC(
  data,
  modelParameters,
  nsims,
  nburn = 100L,
  outputparam = TRUE,
  outputfinal = FALSE,
  verbose = FALSE
)

Value

A list with the following elements:

• Parameters the MCMC chain of model parameters (if outputparam=TRUE)

• LogLikelihood the log likelihood of the model at each iteration (if outputparam=TRUE)

• MCMCParameters the MCMC parameters used

• ModelParameters the model parameters used

• ModelName the name of the model

• nstates the number of states in the model

• waic1 the WAIC1 estimate

• waic2 the WAIC2 estimate

• and optionally (if outputfinal=TRUE) FinalModel the final model state.

Arguments

data

Data frame with columns, in order: facility, unit, time, patient, and event type.

modelParameters

List of model parameters, see LogNormalModelParams().

nsims

Number of MCMC samples to collect after burn-in.

nburn

Number of burn-in iterations.

outputparam

Whether to output parameter values at each iteration.

outputfinal

Whether to output the final model state.

verbose

Print progress messages.

See Also

mcmc_to_dataframe

Examples

  # Minimal example: create parameters and run a very short MCMC
  params <- LinearAbxModel(nstates = 2)
  data(simulated.data_sorted, package = "bayestransmission")
  results <- runMCMC(
    data = simulated.data_sorted,
    modelParameters = params,
    nsims = 3,
    nburn = 0,
    outputparam = TRUE,
    outputfinal = FALSE,
    verbose = FALSE
  )
  str(results)