# pChapman: Hypothesis Testing Using the Chapman Estimator In mbtyers/recapr: Estimating, Testing, and Simulating Abundance in a Mark-Recapture

## Description

Approximates a p-value for a hypothesis test of the Chapman estimator by means of many simulated draws from the null distribution, conditioned on sample sizes.

## Usage

 1 2 pChapman(estN = NULL, nullN, n1, n2, m2 = NULL, nsim = 1e+05, alternative = "less")

## Arguments

 estN The estimated abundance. Either this or the number of recaptures (m2) must be specified. nullN The abundance given by the null hypothesis n1 Number of individuals captured and marked in the first sample n2 Number of individuals captured in the second sample m2 Number of recaptures. Either this or the estimated abundance (estN) must be specified. nsim Number of simulated values to draw. Defaults to 100000. alternative Direction of the alternative hypothesis. Allowed values are "less", "greater", or "2-sided". Defaults to "less".

## Value

An approximate p-value for the specified hypothesis test. If m2 is specified rather than estN, output will be returned as a list with two elements: the estimated abundance and p-value.

## Note

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

• The population is closed; that is, that there are no births, deaths, immigration, or emigration between sampling events

• All individuals have the same probability of capture in one of the two events, or complete mixing occurs between events

• Marking in the first event does not affect probability of recapture in the second event

• Individuals do not lose marks between events

• All marks will be reported in the second event

Matt Tyers