CoverageEstimator: Coverage Estimator, using Chao1 Index, Turing-Good or...

In jcdterry/cassandRa: Finds Missing Links and Metric Confidence Intervals in Ecological Bipartite Networks

Description

An estimate of the sample coverage, which tries to use the most appropriate method

Usage

CoverageEstimator(x, cutoff = 5, BayesPrior = "Flat")

Arguments

x	A vector of integers, the observed sample counts
cutoff	When to switch from binomial model to Chao1 estimator
BayesPrior	Prior to use. Either 'Flat' or 'Jeffereys'.

Details

Sample coverage is defined as the probability that the next interaction drawn is of a type not yet seen

If the sample size is at or below a cutoff (5) or if all the samples are singletons, this is calculated as the posterior mean of a binomial model using a flat prior (this can be changed to a Jeffereys).

If there are singletons but no doubletons, the Turing-Good estimate is used: c_hat = 1 - (f1/n)

If there are both singletons and doubletons, the Chao1 index is used:

c_hat = 1 -( (f1/n) * ( (f1*(n-1))/((n-1)*(f1+(2*f2))) ) )

Value

c_hat, the estimated coverage. (i.e. 1- C_def)

jcdterry/cassandRa documentation built on July 3, 2019, 4:49 p.m.