get.thinned.tfr.mcmc {bayesTFR} | R Documentation |

## Creating and Accessing Thinned MCMCs

### Description

The function `get.thinned.tfr.mcmc`

accesses
a thinned and burned version of the given Phase II MCMC set. `create.thinned.tfr.mcmc`

creates or updates such a set.

### Usage

```
get.thinned.tfr.mcmc(mcmc.set, thin = 1, burnin = 0)
create.thinned.tfr.mcmc(mcmc.set, thin = 1, burnin = 0,
output.dir = NULL, verbose = TRUE, uncertainty = FALSE,
update.with.countries = NULL)
```

### Arguments

`mcmc.set` |
Object of class |

`thin` , `burnin` |
Thinning interval and burnin used for creating or identifying the thinned object. |

`output.dir` |
Output directory. It is only used if the output goes to a non-standard directory. |

`verbose` |
Logical switching log messages on and off. |

`uncertainty` |
If users want to save the thinned estimated TFR in the new mcmc object, this parameter should be set |

`update.with.countries` |
If an existing set is to be updated, this should be a vector of country indices for the update. |

### Details

The function `create.thinned.tfr.mcmc`

is called from `tfr.predict`

and thus, the resulting object contains exactly the same MCMCs used for generating projections. In addition, it can be also called from `tfr.diagnose`

if desired, so that the projection process can re-use such a set that leads to a convergence.

The thinning is done as follows: The given `burnin`

is removed from the beginning of each chain in the original MCMC set. Then each chain is thinned by `thin`

using equal spacing and all chains are collapsed into one single chain per parameter. They are stored in the main simulation directory under the name ‘thinned_mcmc_*t*_*b*’ where *t* is the value of `thin`

and *b* the value of `burnin`

.

If `uncertainty=TRUE`

, the estimated TFR is thinned and saved as well.

### Value

Both functions return an object of class `bayesTFR.mcmc.set`

. `get.thinned.tfr.mcmc`

returns `NULL`

if such object does not exist.

### Author(s)

Hana Sevcikova

### See Also

`bayesTFR.mcmc.set`

, `tfr.predict`

, `tfr.diagnose`

### Examples

```
## Not run:
sim.dir <- tempfile()
m <- run.tfr.mcmc(nr.chains=2, iter=30, seed=1, output.dir=sim.dir, verbose=TRUE)
tfr.predict(m, burnin=15, use.tfr3=FALSE) # creates thinned MCMCs
mb <- get.thinned.tfr.mcmc(m, thin=1, burnin=15)
summary(mb, meta.only=TRUE) # length 30 = 2chains x (30-15)iters.
unlink(sim.dir, recursive=TRUE)
## End(Not run)
```

*bayesTFR*version 7.4-2 Index]