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Tools are provided for estimating, testing, and simulating abundance in a two-event (Petersen) mark-recapture experiment. Functions are given to calculate the Petersen, Chapman, and Bailey estimators and associated variances. However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or Darroch estimators. Functions are also provided to calculate recommended sample sizes.
NChapman(), NPetersen(), and
NBailey() calculate the values of Chapman, Petersen, or
Bailey abundance estimates, given values of sample sizes and number of
recaptures
vChapman(), vPetersen(), and
vBailey() calculate the estimated variance of Chapman,
Petersen, or Bailey abundance estimates, given values of sample sizes
and number of recaptures, and seChapman(),
sePetersen(), and seBailey() give standard
errors
rChapman(), rPetersen(), and
rBailey() return vectors of random draws from the Chapman,
Petersen, or Bailey abundance estimates, given values of true abundance
and sample sizes
pChapman(), pPetersen(), and
pBailey() use many random draws to calculate approximate
p-values for hypothesis testing
powChapman(), powPetersen(), and
powBailey() use simulation to calculate hypothesis testing
power, given alternative abundance
ciChapman(), ciPetersen(), and
ciBailey() calculate confidence intervals for abundance
using bootstrapping and/or normal approximation
plotdiscdensity() produces an empirical pmf plot of
a vector of discrete values, such as that returned from an abundance
estimate simulation, that is more appropriate than a traditional kernel
density plot and perhaps more illustrative than a histogram
consistencytest() and strattest()
provide the typical chi-squared tests for the consistency of a
Petersen-type estimator, and provide evidence of the necessity of a
stratified or partially stratified (Darroch-type) estimator
powconsistencytest() and powstrattest()
provide power estimates for the tests reported in
consistencytest() and strattest()
Nstrat(), vstrat(),
sestrat() and cistrat() provide estimation if
a completely stratified estimator is used
NDarroch() provides estimation if a spatially or
temporally stratified estimator is used, or if strata differs between
sampling events
n2RR() provides recommended sample size using
Robson-Regier, and plotn2sim() and
plotn1n2simmatrix() provide graphical explorations of
recommended sample sizes via simulation
The ‘recapr’ package is currently available on Github, and can be installed in R with the following code:
install.packages("devtools",dependencies=T")
devtools::install_github("mbtyers/recapr")
This package has no known issues.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.