Calculates approximate confidence intervals(s) for the Stratified estimator, using bootstrapping, the Normal approximation, or both.
The bootstrap interval is created by resampling the data in the second sampling event, with replacement for each stratum; that is, drawing bootstrap values of m2 from a binomial distribution with probability parameter m2/n2.
cistrat(
n1,
n2,
m2,
conf = 0.95,
method = "both",
bootreps = 10000,
estimator = "Chapman",
useChapvar = FALSE
)
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.
The type of estimator to use. Allowed values are
"Chapman"
, "Petersen"
, and "Bailey"
. Default to
"Chapman"
.
Whether to use the Chapman estimator variance instead of
the Petersen estimator variance for the normal-distribution interval, if
"method"
is set to "Petersen"
. Defaults to FALSE
.
A list with the abundance estimate and confidence interval bounds for the normal-distribution and/or bootstrap confidence intervals.
\linkstrattest, Nstrat, rstrat, vstrat, sestrat, NChapman, NPetersen, NBailey
# NOT RUN {
cistrat(n1=c(100,200), n2=c(100,500), m2=c(10,10))
# }
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