The function takes data in the form of counts per segment along a transect and uses the
raw Generalised Visual Fast Count estimator (as calculated by `GVFCMOM`

and its
expectation (as calculated by `expected.pois`

for Poisson or `expected.nb`

for
negative binomial) to calculate
a method of moments estimator. This effectively,
adjusts the biased raw GVFC estimate. The function allows counts to have either a Poisson or a Negative Binomial
distribution. The method is a generalisation of the methods in Barry and Coggan (2010).

1 | ```
GVFCMOM(counts, s, d, method, k=1, lowint=0, highint=100)
``` |

`counts` |
Vector of length s that contains a count for each segment |

`s` |
Number of segments |

`d` |
Number of positive segment counts needed before counting stops |

`method` |
Whether Poisson ( |

`k` |
Size parameter of the Negative Binomial distribution |

`lowint` |
Minimum value for MOM estimate (default=0) |

`highint` |
Maximum value for MOM estimate (default=100) |

The method of moments estimate for the transect is returned

Jon Barry: Jon.Barry@cefas.co.uk

Barry J, Eggleton J, Ware S and Curtis M (2015) Generalizing Visual Fast Count Estimators for Underwater Video Surveys. Ecosphere. http://www.esajournals.org/doi/full/10.1890/ES15-00093.1

`GVFC`

, `expected.pois`

, `expected.nb`

1 2 3 4 |

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

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