run.tfr3.mcmc: Running Markov Chain Monte Carlo for Parameters of Total...
In jasonacollins/bayesTFR1: Bayesian Fertility Projection

Description Usage Arguments Details Value Author(s) References See Also Examples

Runs (or continues running) MCMCs for simulating phase III total fertility rate, using a Bayesian hierarchical version of an AR(1) model.

run.tfr3.mcmc(sim.dir, nr.chains = 3, iter = 50000, thin = 10, 
    replace.output = FALSE, my.tfr.file = NULL, buffer.size = 100, 
    use.extra.countries = FALSE, 
    mu.prior.range = c(0, 2.1), rho.prior.range = c(0, 1 - .Machine$double.xmin), 
    sigma.mu.prior.range = c(1e-05, 0.318), sigma.rho.prior.range = c(1e-05, 0.289), 
    sigma.eps.prior.range = c(1e-05, 0.5), 
    mu.ini = NULL, mu.ini.range = mu.prior.range, 
    rho.ini = NULL, rho.ini.range = rho.prior.range, 
    sigma.mu.ini = NULL, sigma.mu.ini.range = sigma.mu.prior.range, 
    sigma.rho.ini = NULL, sigma.rho.ini.range = sigma.rho.prior.range, 
    sigma.eps.ini = NULL, sigma.eps.ini.range = sigma.eps.prior.range, 
    seed = NULL, parallel = FALSE, nr.nodes = nr.chains, compression.type = "None", 
    auto.conf = list(max.loops = 5, iter = 50000, iter.incr = 20000, nr.chains = 3, 
                    thin = 60, burnin = 10000), 
    verbose = FALSE, verbose.iter = 1000, ...)
        
continue.tfr3.mcmc(sim.dir, iter, chain.ids=NULL, 
    parallel = FALSE, nr.nodes = NULL, auto.conf = NULL,
    verbose=FALSE, verbose.iter = 1000, ...)

`sim.dir`	Directory with an existing simulation of phase II TFR (see `run.tfr.mcmc`).
`nr.chains`	Number of MCMC chains to run.
`iter`	Number of iterations to run in each chain. In addition to a single value, it can have the value ‘auto’ in which case the function runs for the number of iterations given in the `auto.conf` list (see below), then checks if the MCMCs converged (using the `auto.conf` settings). If it did not converge, the procedure is repeated until convergence is reached or the number of repetition exceeded `auto.conf$max.loops`.
`thin`	Thinning interval between consecutive observations to be stored on disk.
`replace.output`	If `TRUE`, previously stored results of a phase III simulation will be overwritten.
`my.tfr.file`	File name containing user-specified TFR time series for one or more countries. See description of this argument in `run.tfr.mcmc`.
`buffer.size`	Buffer size (in number of iterations) for keeping data in the memory.
`use.extra.countries`	By default, only countries are used in the MCMCs that were assigned for estimation (i.e. their ‘include_code’ is 2 in the include) dataset and are in phase III at present time (argument `present.year` in `run.tfr.mcmc`). If this argument is `TRUE`, countries that were added using `run.tfr.mcmc.extra` and are in phase III are also included.
`mu.prior.range, rho.prior.range, sigma.mu.prior.range, sigma.rho.prior.range, sigma.eps.prior.range`	Min and max for the prior (uniform) distribution of these paraemters.
`mu.ini, rho.ini, sigma.mu.ini, sigma.rho.ini, sigma.eps.ini`	Initial value(s) of the parameters. It can be a single value or an array of the size `nr.chains`. By default, if `nr.chains` is 1, it is the middle point of the corresponding range. Otherwise, it is uniformly randomly distributed within the range.
`mu.ini.range, rho.ini.range, sigma.mu.ini.range, sigma.rho.ini.range, sigma.eps.ini.range`	Min and max for the initial values.
`seed`	Seed of the random number generator. If `NULL` no seed is set.
`parallel`	Logical determining if the simulation should run multiple chains in parallel. If it is `TRUE`, the package snowFT is required.
`nr.nodes`	Relevant only if `parallel` is `TRUE`. It gives the number of nodes for running the simulation in parallel.
`compression.type`	One of ‘None’, ‘gz’, ‘xz’, ‘bz’, determining type of a compression of the MCMC files. Important: Do not use this option for a long MCMC simulation as this tends to cause very long run times due to slow reading!
`auto.conf`	List containing a configuration for an ‘automatic’ run (see description of argument `iter`). Item `iter` gives the number of iterations in the first chunk of the MCMC simulation; item `iter.incr` gives the number of iterations in the following chunks; `nr.chains` gives the number of chains in all chunks of the MCMC simulation; items `thin` and `burnin` are used in the convergence diagnostics following each chunk; `max.loops` controls the maximum number of chunks. All items must be integer values. This argument is only used if the function argument `iter` is set to ‘auto’.
`verbose`	Logical switching log messages on and off.
`verbose.iter`	Integer determining how often (in number of iterations) messages are outputted during the estimation.
`...`	Additional parameters to be passed to the function `performParallel`, if `parallel` is `TRUE`.
`chain.ids`	Array of chain identifiers that should be resumed. If it is `NULL`, all existing chains are resumed.

The MCMCs are stored in sim.dir in a subdirectory called “phaseIII”. It has exactly the same structure as phase II MCMCs described in run.tfr.mcmc.

An object of class bayesTFR.mcmc.set which is a list with two components:

`meta`	An object of class `bayesTFR.mcmc.meta`.
`mcmc.list`	A list of objects of class `bayesTFR.mcmc`, one for each MCMC.

Hana Sevcikova

Raftery, A.E., Alkema, L. and Gerland, P. (2014). Bayesian Population Projections for the United Nations. Statistical Science, Vol. 29, 58-68.

run.tfr.mcmc, get.tfr3.mcmc

## Not run: 
sim.dir <- tempfile()
# Runs Phase II MCMCs (must be run before Phase III)
m <- run.tfr.mcmc(nr.chains=1, iter=5, output.dir=sim.dir, verbose=TRUE)
# Runs Phase III MCMCs
m3 <- run.tfr3.mcmc(sim.dir=sim.dir, nr.chains=2, iter=50, thin=1, verbose=TRUE)
m3 <- continue.tfr3.mcmc(sim.dir=sim.dir, iter=10, verbose=TRUE)
summary(m3, burnin=10)
unlink(sim.dir, recursive=TRUE)

## End(Not run)