simulateAccount: Create a synthetic demographic account
In StatisticsNZ/demest: Bayesian Demographic Estimation and Forecasting

simulateAccount

R Documentation

Create a synthetic demographic account

Description

WARNING - THIS FUNCTION IS STILL UNDER DEVELOPMENT

Usage

simulateAccount(
  account,
  systemModels,
  datasets = list(),
  dataModels = list(),
  concordances = list(),
  weights = list(),
  dominant = c("Female", "Male"),
  usePriorPopn = TRUE,
  updateSystemModel = NULL,
  updateDataModel = NULL,
  updateInitialPopn = TRUE,
  probSmallUpdate = 0,
  scaleNoise = 0,
  filename = NULL,
  nBurnin = 1000,
  nSim = 1000,
  nChain = 4,
  nThin = 1,
  parallel = TRUE,
  nCore = NULL,
  outfile = NULL,
  nUpdateMax = 50,
  verbose = TRUE,
  useC = TRUE
)

Arguments

`account`	An object of class `DemographicAccount`, giving the initial values for the estimation of the account.
`systemModels`	A list of objects of class `SpecModel` specifying models for the demographic series.
`datasets`	A named list of objects of class `Counts`.
`dataModels`	A list of objects of class `SpecModel` specifying models for the datasets.
`concordances`	A named list of `concordances`, which are applied to the series in the account before they are supplied to the corresponding data model.
`weights`	A named list of `Counts` objects, providing weights for any Normal models among the system models.
`dominant`	Either `"Female"` (the default) or `"Male"`. Determines which sex is used to generate exposures in the system model for births.
`usePriorPopn`	Whether to take account of the prior model for population when inferring values for the account. Defaults to `TRUE`.
`updateSystemModel`	Names of any system models containing parameters that need to be estimated (as oppsed to simply drawn from the prior distribution) as part of the simulation.
`updateDataModel`	Names of any data models containing parameters that need to be estimated (as oppsed to simply drawn from the prior distribution) as part of the simulation.
`updateInitialPopn`	If `TRUE` (the default) population counts in the first year of the account are inferred as part of the overall estimation process. If `FALSE`, the values supplied for the first year are treated as error-free and never updated.
`probSmallUpdate`	Proportion of updates of components that are 'small', ie that only consist of exchanging values between two neighbouring Lexis triangles.
`scaleNoise`	Governs noise added to Metropolis-Hastings ratio. Should be non-zero only when trying to generate initial values. Currently experimental, and may change.
`filename`	The name of a file where output is collected.
`nBurnin`	Number of iteration discarded before recording begins.
`nSim`	Number of iterations carried out during recording.
`nChain`	Number of independent chains to use.
`nThin`	Thinning interval.
`parallel`	Logical. If `TRUE` (the default), parallel processing is used.
`nCore`	The number of cores to use, when `parallel` is `TRUE`. If no value supplied, defaults to `nChain`.
`outfile`	Where to direct the ‘stdout’ and ‘stderr’ connection output from the workers when parallel processing. Passed to function `[parallel]{makeCluster}`.
`nUpdateMax`	Maximum number of iterations completed before releasing memory. If running out of memory, setting a lower value than the default may help.
`verbose`	Logical. If `TRUE` (the default) a message is printed at the end of the calculations.
`useC`	Logical. If `TRUE` (the default), the calculations are done in C. Setting `useC` to `FALSE` may be useful for debugging.