calibrateModel_beyesian: Calibrate models using Bayesian methods - employing local...
In W-Mohammed/calibrater: Calibration of Decision-Analytic Models

calibrateModel_beyesian

R Documentation

Calibrate models using Bayesian methods - employing local IMIS_()

Description

Calibrate models using Bayesian methods - employing local IMIS_()

Usage

calibrateModel_beyesian(
  .b_method = "SIR",
  .func,
  .args,
  .l_targets,
  .l_params,
  .samples,
  .n_resample = 1000,
  .IMIS_sample = 1000,
  .IMIS_iterations = 30,
  .MCMC_burnIn = 10000,
  .MCMC_samples = 50000,
  .MCMC_thin = 5,
  .MCMC_rerun = TRUE,
  .transform = FALSE,
  .diag_ = FALSE
)

Arguments

`.b_method`	Character defining the Bayesian method to use in the calibration process. Currently supported methods are `SIR` and `IMIS`.
`.func`	A function defining the decision analytic model to be calibrated.
`.args`	A list of arguments passed to the model function.
`.l_targets`	A list containing a vector of targets' names, a vector of targets' weights, a vector of targets' distributions, and a table for each target that contains the values (column name 'value') and standard errors (column name 'sd') of the corresponding target.
`.l_params`	A list that contains a vector of parameter names, distributions and distributions' arguments.
`.samples`	A table or vector of sampled parameter values
`.n_resample`	the desired number of draws from the posterior
`.IMIS_sample`	the incremental sample size at each IMIS iteration
`.IMIS_iterations`	the maximum number of iterations in IMIS
`.MCMC_burnIn`	the number of samples before starting to retain samples
`.MCMC_samples`	the total number of samples the MCMC algorithm should generate including the burn-in sample including the .MCMC_burnIn. This value should not be equal to or less than .MCMC_burnIn.
`.MCMC_thin`	the value used to thin the resulting chain
`.MCMC_rerun`	use the proposal distribution covariance matrix from the first run to re-run the MCMC chain.
`.transform`	Logical for whether to back-transform parameters to their original scale.
`.diag_`	Logical for whether to print diagnostics