bayesLife.mcmc {bayesLife} | R Documentation |

MCMC simulation object `bayesLife.mcmc`

containing information about one MCMC chain. A set of such objects belonging to the same simulation together with a `bayesLife.mcmc.meta`

object constitute a `bayesLife.mcmc.set`

object.

An object `bayesLife.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.e0.mcmc(...)`

.

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

object.

A `bayesLife.mcmc`

object contains parameters of the Bayesian hierarchical model, more specifically, their initial values (all names with the suffix `.ini`

) and values from the last iteration. These are:

`Triangle/Triangle.ini, lambda/lambda.ini`

- world parameters, containing four values each. They correspond to model parameters *Delta_1, …, Delta_4* and *lambda_1, … lambda_4*, respectively.

`k/k.ini, z/z.ini, omega/omega.ini, lambda.k/lambda.k.ini,`

`lambda.z/lambda.z.ini`

- world parameters, containing one value each. They correspond to model parameters *k*, *z*, *omega*, *lambda_k*, and *lambda_z*, respectively.

`Triangle.c`

- country-specific parameter *Delta^c_1, …, Delta^c_4* with four values for each country, i.e. an *4 x C* matrix where *C* is the number of countries.

`k.c, z.c`

- country-specific parameters *k^c* and *z^c* (1d arrays of length *C*).

Furthermore, the object 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. |

`meta` |
Object of class |

Hana Sevcikova

`run.e0.mcmc`

, `get.e0.mcmc`

, `bayesLife.mcmc.set`

, `bayesLife.mcmc.meta`

sim.dir <- file.path(find.package("bayesLife"), "ex-data", "bayesLife.output") # loads traces from one chain m <- get.e0.mcmc(sim.dir, low.memory = FALSE, burnin = 40, chain.ids = 1) # should have 20 rows, since 60 iterations in total minus 40 burnin dim(e0.mcmc(m, 1)$traces) summary(m)

[Package *bayesLife* version 5.0-1 Index]