This will return return the log-likelihood value given a value of N.

1 | ```
NB.likelihood(N, infile, alleles, sample.interval)
``` |

`N` |
The effective population size dor diploid individuals. |

`infile` |
Your input data file in a plain text format. This data contains the allele counts at each locus and from each sampling time point. |

`alleles` |
a vector containing the number of alleles at each locus. For example, c(4, 4, 4) would mean that 3 loci are sampled, with 4 alleles each. |

`sample.interval` |
a vector stating at which generations the samples were taken. For example, c(0, 8) would indicate that two samples were collected from the 0th and 8th generation. |

More details please see `NB.estimator`

.

This function returns one single element, the log-likelihood of the model given the effective population size N and your inputs.

This function allows you to use your own optimisation algorithms or customise the parameters using `optim`

or `nlm`

. Otherwise please use `NB.estimator`

which has the internal `optim`

wrapped inside.

`NB.estimator`

.

1 2 3 4 5 6 7 8 9 10 11 | ```
## CREATE SAMPLE DATASET
NB.example.dataset()
##SEE WHAT'S THE LOG-LIKELIHOOD VALUE IS WHEN N=1000
NB.likelihood(N=1000, infile='sample_data.txt',
alleles=rep(4, 50), sample.interval=c(0, 8))
#####
# NUMERICAL RESULT
#[1] -544.0405
#####
``` |

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