abun_oi: Semiparametric empirical likelihood inference for abundance...

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

Description

abun_oi is used to calculate the maximum empirical likelihood estimator and the empirical likelihood ratio confidence interval of abundance by fitting one-inflated count regression model.

Usage

abun_oi(
  formula,
  data,
  model = c("zt", "ztoi", "oizt"),
  dist = c("poisson", "binomial"),
  K = NULL,
  ci = FALSE,
  level = 0.95,
  control = abun_oi.control()
)

Arguments

formula	symbolic description of the model, see 'Details'.
data	data frame or list containing the variables in the model. If not found in data, the variables are taken from environment(formula).
model	character specification of "zt" (zero-truncated model without one-inflation), "ztoi" (zero-truncated one-inflated model), or "oizt" (one-inflated zero-truncated model).
dist	character specification of count regression model family, "poisson" or "binomial".
K	number specifying the number of capture occasions when dist is "binomial".
ci	logic. If TRUE, the empirical likelihood ratio conficence interval of abundance is calculated.
level	number specifying the nominal level of confidence interval of abundance.
control	list of control arguments in EM algorithm specified via abun_oi.control.

Details

If model = "zt", the formula has the form y ~ x where y is the (numeric) vector representing the number of captures and x is a series of terms which specifies a linear predictor in count regression model.

If model = "ztoi" or model = "oizt", the formula has the form y ~ x|z where z is a series of terms which specifies a linear predictor in one-inflated logistic regression model.

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.

Examples

### Estimation results for prinia data
### under zero-truncated binomial regression model without one-inflation
(pri_zt <- abun_oi(y ~ x, data = prinia, model = "zt",
                   dist = "binomial", K = 17))

### P-values of score tests for one-inflation
pri_st_ztoi <- scoretest_oi(pri_zt, model = "ztoi")
pri_st_oizt <- scoretest_oi(pri_zt, model = "oizt")
pnorm(pri_st_ztoi)
pnorm(pri_st_oizt)

### Under zero-truncated one-inflated binomial regression model
pri_ztoi <- abun_oi(y ~ x|1, data = prinia, model = "ztoi",
                    dist = "binomial", K = 17, ci = TRUE)
(pri_ztois <- summary(pri_ztoi, boot = 200))
### Maximum EL estimate of w
(w <- plogis(pri_ztois@eta))

### Under one-inflated zero-truncated binomial regression model
pri_oizt <- abun_oi(y ~ x|1, data = prinia, model = "oizt",
                    dist = "binomial", K = 17, ci = TRUE)
round(pri_oizt@N)
round(pri_oizt@ci)

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