LP_CL_fit {Petersen}

Fit the Chen-Lloyd model to estimate abundance using a non-parametric smoother for a covariates

Description

This will take a data frame of capture histories, frequencies, and a covariates and will do a non-parametric smoother for the detection probabilities as a function of the covariates and use this to estimate the population size.

Usage

LP_CL_fit(
  data,
  covariate,
  centers = hist(data[, covariate, drop = TRUE], breaks = "Sturges", plot = FALSE)$mids,
  h1 = (centers[2] - centers[1]) * 0.75,
  h2 = (centers[2] - centers[1]) * 0.75,
  conf_level = 0.95
)

Arguments

data

Data frame containing the variables:

• cap_hist Capture history (see details below)

• freq Number of times this capture history was observed

plus any other covariates (e.g. discrete strata and/or continuous covariates) to be used in the model fitting.

covariate

Name of continuous covariate that influences capture probabilities at each event

centers

Centers of bins to group the covariates. We suggest no more than 30 bins in total with fewer bins with smaller sample sizes. Of course with smaller sample sizes, a simple stratified estimator may be easier to use.

h1, h2

Standard deviation of normal kernel for first sampling event. This should be between 1/2 and the 1.5x the bin width. Larger values imply more smoothing. Smaller values imply less smoothing.

conf_level

The expected coverage for confidence intervals on N.

Details

The frequency variable (freq in the data argument) is the number of animals with the corresponding capture history.

Capture histories (cap_hist in the data argument) are character values of length 2.

• 10 Animals tagged but never seen again.

• 11 Animals tagged and recaptured and tag present at event 2.

• 01 Animals captured at event 2 that appear to be untagged.

Value

An list object of class LP_CL_fit with abundance estimates and other information with the following elements

• summary A data frame with the estimates of abundance, SE, and CI

• fit Details on the Chen and Lloyd fit including the smoothed estimates of catchability, estimates abundance by category classes, estimates of total abundance, plots of the estimated abundance curve and catchability curves, etc.

• datetime Date and time the fit was done

References

SX Chen, CJ Lloyd (2000). A nonparametric approach to the analysis of two-stage mark-recapture experiments. Biometrika, 87, 633–649. doi:10.1093/biomet/87.3.633.

Examples

library(Petersen)
data(data_NorthernPike)
res <- LP_CL_fit(data_NorthernPike, "length")
res$summary