Calculates approximate confidence intervals(s) for the Chapman estimator, using bootstrapping, the Normal approximation, or both.
The bootstrap interval is created by resampling the data in the second sampling event, with replacement; that is, drawing bootstrap values of m2 from a binomial distribution with probability parameter m2/n2. This technique has been shown to better approximate the distribution of the abundance estimator. Resulting CI endpoints both have larger values than those calculated from a normal distribution, but this better captures the positive skew of the estimator. Coverage has been investigated by means of simulation under numerous scenarios and has consistently outperformed the normal interval. The user is welcomed to investigate the coverage under relevant scenarios.
ciChapman(n1, n2, m2, conf = 0.95, method = "both", bootreps = 10000)Number of individuals captured and marked in the first sample
Number of individuals captured in the second sample
Number of marked individuals recaptured in the second sample
The confidence level of the desired intervals. Defaults to 0.95.
Which method of confidence interval to return. Allowed values
are "norm", "boot", or "both". Defaults to
"both".
Number of bootstrap replicates to use. Defaults to 10000.
A list with the abundance estimate and confidence interval bounds for the normal-distribution and/or bootstrap confidence intervals.
NChapman, vChapman, seChapman, rChapman, pChapman, powChapman
# NOT RUN {
ciChapman(n1=100, n2=100, m2=20)
# }
Run the code above in your browser using DataLab