abun_zt: Semiparametric empirical likelihood inference for abundance...

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

Description

abun_zt is used to implement the maximum empirical likelihood method by fitting zero-truncated count regression models.

Usage

abun_zt(
  y,
  K = NULL,
  x,
  dist,
  maxN = NULL,
  start_beta = NULL,
  eps = 1e-05,
  maxit = 5000,
  N0 = NULL
)

Arguments

y	number of times that individuals were captured
K	number specifying the number of capture occasions when dist is "binomial".
x	vector or matrix containing the individual covariates which have influence on the capture probability.
dist	character specification of count regression model family, "poisson" or "binomial".
maxN	number specifying the largest searching value for N in EM algorithm.
start_beta	vector specifying the starting value of the coefficient β in count regression model.
eps	positive convergence tolerance ε. The iterations converge in EM algorithm when the increase of the log-EL is less than ε.
maxit	integer specifying the maximal number of iterations in EM algorithm.
N0	number specifying the value of abundance. If it is NULL, the maximum EL estimator of abundance is calculated.

Value

An abun_oi object.

References

Liu, Y., Li, P., Liu, Y., and Zhang, R. (2021). Semiparametric empirical likelihood inference for abundance from one-inflated capture-recapture data. Biometrical Journal.

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