bayesTFR.mcmc {bayesTFR} | R Documentation |

MCMC simulation object `bayesTFR.mcmc`

containing information about one MCMC chain, either from Phase II or Phase III simulation. A set of such objects belonging to the same simulation together with a `bayesTFR.mcmc.meta`

object constitute a `bayesTFR.mcmc.set`

object.

An object `bayesTFR.mcmc`

points to a place on disk (element `output.dir`

) where MCMC results from all iterations are stored. They can be retrieved to the memory using `get.tfr.mcmc(...)`

(Phase II) or `get.tfr3.mcmc(...)`

(Phase III), and `tfr.mcmc(...)`

.

The object is in standard cases not to be manipulated by itself, but rather as part of a `bayesTFR.mcmc.set`

object.

A `bayesTFR.mcmc`

object contains parameters of the Bayesian hierarchical model, more specifically, their values from the last iteration. If it is a **Phase II** object these parameters are:

`psi, chi, a_sd, b_sd, const_sd, S_sd, sigma0, mean_eps_tau, sd_eps_tau, Triangle4`

- non-country specific parameters, containing one value each.

`alpha, delta`

- non-country specific parameters, containing three values each.

`U_c, d_c, Triangle_c4`

- country-specific parameters (1d array).

`gamma_ci`

- country-specific parameter with three values for each country, i.e. an `n \times 3`

matrix where `n`

is the number of countries.

**Phase III** MCMC objects contain single-value parameters `mu`

, `rho`

, `sigma.mu`

, `sigma.rho`

, `sigma.eps`

and `n`

-size vectors `mu.c`

and `rho.c`

.

Furthermore, the object (independent of Phase) contains components:

`iter` |
Total number of iterations the simulation was started with. |

`finished.iter` |
Number of iterations that were finished. Results from the last finished iteration are stored in the parameters above. |

`length` |
Length of the MCMC stored on disk. It differs from |

`thin` |
Thinning interval used when simulating the MCMCs. |

`id` |
Identifier of this chain. |

`output.dir` |
Subdirectory (relative to |

`traces` |
This is a placeholder for keeping whole parameter traces in the memory. If the processing operates in a low memory mode, it will be 0. It can be filled in using the function |

`traces.burnin` |
Burnin used to retrieve the traces, i.e. how many stored iterations are missing from the beginning in the |

`rng.state` |
State of the random number generator at the end of the last finished interation. |

`compression.type` |
Type of compression of the underlying files. |

`meta` |
Object of class |

Hana Sevcikova

`run.tfr.mcmc`

, `get.tfr.mcmc`

, `run.tfr3.mcmc`

, `get.tfr3.mcmc`

, `bayesTFR.mcmc.set`

, `bayesTFR.mcmc.meta`

```
sim.dir <- file.path(find.package("bayesTFR"), "ex-data", "bayesTFR.output")
# loads traces from one chain
m <- get.tfr.mcmc(sim.dir, low.memory=FALSE, burnin=35, chain.ids=1)
# should have 25 rows, since 60 iterations in total minus 35 burnin
dim(tfr.mcmc(m, 1)$traces)
summary(m, chain.id=1)
```

[Package *bayesTFR* version 7.1-1 Index]