Function to calculate the method of moments visual fast count estimator

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Description

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).

Usage

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

Arguments

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 ("pois") or Negative Binomial Distribution ("nb") is assumed

k

Size parameter of the Negative Binomial distribution

lowint

Minimum value for MOM estimate (default=0)

highint

Maximum value for MOM estimate (default=100)

Value

The method of moments estimate for the transect is returned

Author(s)

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

References

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

See Also

GVFC, expected.pois, expected.nb

Examples

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counts = c(0, 0, 0, 0, 1, 1, 1, 2, 1)
GVFCMOM(counts, s=9, d=2, method='nb', lowint=0, highint=100)
GVFCMOM(counts, s=9, d=1, method='nb', lowint=0, highint=100)
GVFCMOM(counts, s=9, d=1, method='pois', lowint=0, highint=100)

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