GVFCMOM: Function to calculate the method of moments visual fast count...

Description Usage Arguments Value Author(s) References See Also Examples

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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