ciBailey: Confidence Intervals for the Bailey Estimator

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

View source: R/ci.R

Description

Calculates approximate confidence intervals(s) for the Bailey estimator, using bootstrapping, the Normal approximation, or both.

The bootstrap interval is created by resampling the data in the second sampling event, with replacement; that is, drawing bootstrap values of m2 from a binomial distribution with probability parameter m2/n2. This technique has been shown to better approximate the distribution of the abundance estimator. Resulting CI endpoints both have larger values than those calculated from a normal distribution, but this better captures the positive skew of the estimator. Coverage has been investigated by means of simulation under numerous scenarios and has consistently outperformed the normal interval. The user is welcomed to investigate the coverage under relevant scenarios.

Usage

1
ciBailey(n1, n2, m2, conf = 0.95, method = "both", bootreps = 10000)

Arguments

n1

Number of individuals captured and marked in the first sample

n2

Number of individuals captured in the second sample

m2

Number of marked individuals recaptured in the second sample

conf

The confidence level of the desired intervals. Defaults to 0.95.

method

Which method of confidence interval to return. Allowed values are "norm", "boot", or "both". Defaults to "both".

bootreps

Number of bootstrap replicates to use. Defaults to 10000.

Value

A list with the abundance estimate and confidence interval bounds for the normal-distribution and/or bootstrap confidence intervals.

Note

Any Petersen-type estimator (such as this) depends on a set of assumptions:

Author(s)

Matt Tyers

See Also

NBailey, vBailey, seBailey, rBailey, pBailey, powBailey

Examples

1
ciBailey(n1=100, n2=100, m2=20)

mbtyers/recapr documentation built on Sept. 13, 2021, 11:54 a.m.