mig.diagnose {bayesMig} | R Documentation |

## MCMC convergence diagnostics

### Description

Runs convergence diagnostics of existing migration Markov chains
using the `raftery.diag`

function from the `coda`

package.

### Usage

```
mig.diagnose(
sim.dir,
thin = 80,
burnin = 2000,
express = FALSE,
country.sampling.prop = NULL,
keep.thin.mcmc = FALSE,
verbose = TRUE
)
mig.raftery.diag(
mcmc = NULL,
sim.dir = NULL,
burnin = 0,
country = NULL,
par.names = NULL,
par.names.cs = NULL,
country.sampling.prop = 1,
verbose = TRUE,
...
)
estimate.a.hw(mcmc, burnin = 0, thin = NULL)
```

### Arguments

`sim.dir` |
Directory with MCMC simulation results. |

`thin` |
Thinning interval. |

`burnin` |
Number of iterations to discard from the beginning of the parameter traces. |

`express` |
Logical. If |

`country.sampling.prop` |
Proportion of countries to include in the diagnostics. If it is |

`keep.thin.mcmc` |
Logical. If |

`verbose` |
Logical value. Switches log messages on and off. |

`mcmc` |
A |

`country` |
Name or code of a country. If it is given, only country-specific parameters parameters of that country are considered. |

`par.names` |
Names of country-independent parameters for which the Raftery diagnostics should be computed. By default all parameters are used. |

`par.names.cs` |
Names of country-specific parameters for which the Raftery diagnostics should be computed. By default all parameters are used. |

`...` |
Additional arguments passed to the |

### Details

The `mig.diagnose`

function invokes the `mig.raftery.diag`

function separately for country-independent parameters and for country-specific
parameters. It results in two possible states: red, i.e. it did not converge, and green,
i.e. it converged. The resulting object is stored in
‘{sim.dir}/diagnostics/bayesMig.convergence_{thin}_{burnin}.rda’
and can be accessed using the function `get.mig.convergence`

.

Function `has.mcmc.converged`

from the bayesTFR package
can be used to check if the existing diagnostics converged.

For details on the `mig.raftery.diag`

function, see `tfr.raftery.diag`

.

The `estimate.a.hw`

function estimates an optimal value for the `a.half.width`

argument in `run.mig.mcmc`

.

### Value

`mig.diagnose`

returns an object of class `bayesMig.convergence`

containing summaries of the convergence check inputs and outputs. It has the
same structure as `bayesTFR.convergence`

.
In addition it has an element `a.hw.est`

which is the estimated value for
the `a.half.width`

argument in `run.mig.mcmc`

.

### See Also

`tfr.raftery.diag`

, `raftery.diag`

, `get.mig.convergence`

### Examples

```
# See examples in ?bayesMig and ?get.mig.convergence
```

*bayesMig*version 0.4-6 Index]