estimateCounts: Estimate counts and model from one or more noisy datasets.

In StatisticsNZ/demest: Bayesian Demographic Estimation and Forecasting

Estimate counts and model from one or more noisy datasets.

Description

Infer the contents of a demographic array, and fit a model describing the array, using one or more noisy datasets.

Usage

estimateCounts(
  model,
  y,
  exposure = NULL,
  dataModels,
  datasets,
  concordances = list(),
  jointUpdate = TRUE,
  filename = NULL,
  nBurnin = 1000,
  nSim = 1000,
  nChain = 4,
  nThin = 1,
  parallel = TRUE,
  nCore = NULL,
  outfile = NULL,
  nUpdateMax = 50,
  verbose = FALSE,
  useC = TRUE
)

Arguments

model	An object of class SpecModel, specifying the model to be fit.
y	An object of class Counts with the same structure as the counts to be estimated.
exposure	A Counts object specifying exposure or sample size.
dataModels	A list of objects of class SpecModel describing the relationship between the datasets and counts.
datasets	A named list of objects of class Counts.
concordances	A named list of concordances, which are applied to y before it is supplied to the corresponding data model.
jointUpdate	If TRUE (the default), jointly update the counts and rates/probabilities. If FALSE, revert to the original behaviour of estimateCounts, which is to update counts and rates/probabilities separately. The new behaviour generally leads to faster convergence.
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.

Details

See the documentation for estimateModel for details on model output and on MCMC settings.

dataModels is a list of specificiations for data models, and datasets is a named list of datasets. The response for each data model must be the name of a dataset. See below for examples.

See Also

estimateModel, estimateAccount

Examples

nat <- demdata::sim.admin.nat
health <- demdata::sim.admin.health
survey <- demdata::sim.admin.survey
nat <- Counts(nat, dimscales = c(year = "Points"))
health <- Counts(health, dimscales = c(year = "Points"))
survey <- Counts(survey)
y <- health + 10
model <- Model(y ~ Poisson(mean ~ age + sex + region,
                           useExpose = FALSE))
dataModels <- list(Model(nat ~ PoissonBinomial(prob = 0.98)),
                   Model(health ~ Poisson(mean ~ age)),
                   Model(survey ~ Binomial(mean ~ 1)))
datasets <- list(nat = nat, health = health, survey = survey)
filename <- tempfile()
## in a real example, nBurnin and nSim would be much larger
## Not run: 
estimateCounts(model = model,
               y = y,
               dataModels = dataModels,
               datasets = datasets,
               filename = filename,
               nBurnin = 50,
               nSim = 50,
               nThin = 2,
               nChain = 2,
               parallel = FALSE)
fetchSummary(filename)

## End(Not run)

StatisticsNZ/demest documentation built on Nov. 2, 2023, 7:56 p.m.