abun_oi_boot: Bootstrap-based standard errors and confidence intervals...

In ecnuliuyang/AbunOI: Semiparametric empirical likelihood inference for abundance from one-inflated capture-recapture data

Description

Function to calculate standard errors and confidence intervals based on a bootstrap procedure under discrete time capture-recapture (CR) models.

Usage

abun_oi_boot(object, boot = 100, seed = 2021)

Arguments

object	A abun_oi object.
boot	number specifying the number of bootstrap samples. Default is 200.
seed	number specifying the random seed. Default is 2021.

Value

A list with four elements:

se_N	the standard error of the abundance estimate.
se_beta	the standard error of the coefficients in count regression model.
se_eta	the standard error of the coefficients in one-inflated regression model.
se_alpha	the standard error of the probability of never being captured.
quant_N	the quantiles of the sampling distribution of the abundance estimate.

References

Liu, Y., Li, P., and Qin, J. (2017). Maximum empirical likelihood estimation for abundance in a closed population from capture-recapture data. Biometrika 104, 527-543.

ecnuliuyang/AbunOI documentation built on Feb. 13, 2022, 4:32 p.m.