powBailey: Power for Hypothesis Testing Using the Bailey Estimator

In mbtyers/recapr: Two Event Mark-Recapture Experiment

Description

Approximates the power of a hypothesis test of the Bailey estimator by means of many simulated draws from a specified alternative distribution, conditioned on sample sizes.

Usage

powBailey(
  nullN,
  trueN,
  n1,
  n2,
  alpha = 0.05,
  nsim = 10000,
  alternative = "less"
)

Arguments

nullN	The abundance given by the null hypothesis
trueN	The assumed abundance for the power calculation
n1	Number of individuals captured and marked in the first sample
n2	Number of individuals captured in the second sample
alpha	The alpha level for the test
nsim	Number of simulated values to draw. Defaults to 10000.
alternative	Direction of the alternative hypothesis. Allowed values are "less", "greater", or "2-sided". Defaults to "less".

Value

The approximate power of the specified hypothesis test, for the specified alternative 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

Author(s)

Matt Tyers

See Also

NBailey, vBailey, seBailey, rBailey, pBailey, ciBailey

Examples

powBailey(nullN=500, trueN=400, n1=100, n2=100, nsim=1000)

Ntotry <- seq(from=250, to=450, by=25)
pows <- sapply(Ntotry, function(x)
  powBailey(nullN=500, trueN=x, n1=100, n2=100, nsim=1000))
plot(Ntotry, pows)

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