This function fit a hierarchical or a fixed-effect model, using Bayeisan sampling. We use pMCMC, with a suite of DE-MCMC, DGMC, and simply, crossover (i.e., DE-MC), mutation, or migration operators. Note that the latter two operators essentially are random-walk Metroplolis, so they will be very inefficient, if been applied alone, even with our fast C++ implementation.

1 2 3 4 5 | ```
run(samples, report = 100, ncore = 1, pm = 0, qm = 0, hpm = 0,
hqm = 0, gammamult = 2.38, ngroup = 5, force = FALSE,
sampler = "DE-MCMC", slice = FALSE)
CheckConverged(samples)
``` |

`samples` |
a sample list generated by calling DMC's samples.dmc. |

`report` |
how many iterations to return a report |

`ncore` |
parallel core for run_many |

`pm` |
probability of migration |

`qm` |
probability of mutation |

`hpm` |
probability of migration at the hyper level |

`hqm` |
probability of mutation at the hyper level |

`gammamult` |
a tuning parameter, affecting the size of jump |

`ngroup` |
number of distributed groups |

`force` |
set force to FALSE for turning off recalculation of PDA. Set it as an integer between 1 and 10, forcing to re-calculate new likelihood, every e.g., 1, 2, 3 step. |

`sampler` |
which sampler to run MCMC, "DE-MCMC" or "DGMC" |

`slice` |
use for debugging blocked sampling |

ggdmc documentation built on Sept. 2, 2018, 1:03 a.m.

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