# Perform a Maximum-Likelihood Analysis of a Sample of Genes in a Subdivided Population

### Description

Perform a maximum-likelihood analysis of a sample of genes in a subdivided population.

### Usage

1 | ```
maximum.likelihood(sample,alpha,M,pi,graphics,true.M,true.pi)
``` |

### Arguments

`sample` |
an object generated by the |

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

`M` |
the support for M |

`pi` |
the support for pi |

`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 |

### Details

Once the `sim.inference.model`

or by the `sim.coalescent`

command lines have been executed, `maximum.likelihood`

can be used to compute the maximum likelihood analysis of the sample of genes.

### Value

a ‘sample’ object

### Examples

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 compute Nei's unbiased heterozygosity for that sample
maximum.likelihood(sample)
``` |