run.tfr3.mcmc {bayesTFR} | R Documentation |

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 |

`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 |

`thin` |
Thinning interval between consecutive observations to be stored on disk. |

`replace.output` |
If |

`my.tfr.file` |
File name containing user-specified TFR time series for one or more countries. See description of this argument in |

`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 |

`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 |

`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 |

`parallel` |
Logical determining if the simulation should run multiple chains in parallel. If it is |

`nr.nodes` |
Relevant only if |

`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 |

`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 |

`chain.ids` |
Array of chain identifiers that should be resumed. If it is |

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 |

`mcmc.list` |
A list of objects of class |

Hana Sevcikova

Raftery, A.E., Alkema, L. and Gerland, P. (2014). Bayesian Population Projections for the United Nations. Statistical Science, Vol. 29, 58-68. doi: 10.1214/13-STS419.

```
## 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)
```

[Package *bayesTFR* version 7.1-1 Index]