Perform a Bayesian analysis of a sample of genes in a subdivided population.

1 |

`sample` |
an object generated by the |

`chain.length` |
total length of the chain |

`burnin` |
number of initial steps to discard from the chain |

`range.M` |
the support for M |

`delta.M` |
width of the uniform proposal for the parameter M |

`delta.pi` |
width of the uniform proposals for the parameters pi |

`alpha` |
the alpha-level, which takes the default value alpha = 0.05 |

`graphics` |
a logical variable, which is TRUE if the user wants graphics to be plotted |

`true.M` |
true (simulated) value of M |

`true.pi` |
true (simulated) value of pi |

Once the `sim.inference.model`

or by the `sim.coalescent`

command lines have been executed, `run.mcmc`

can be used to compute a Bayesian analysis of the sample of genes.

A MCMC chain.

1 2 3 4 5 6 7 8 9 10 | ```
## This is to simulate a sample of genes (at a single locus), using the inference model, with
## 50 genes collected in each of 10 sampled demes. In this example, the product of
## twice the effective population size and migration rate is 2,
## and the frequency of allele A in the migrant pool is 0.5
sample <- sim.inference.model(number.of.sampled.demes = 10,sample.sizes = 50,M = 2,pi = 0.5)
## This is to run a MCMC for that sample
run.mcmc(sample)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.