int.est.ce: Catch-Effort Method Abundance Estimation: Interval Estimate

In DistanceDevelopment/WiSP: Wildlife Simulation Package (wisp)

Description

This function estimates confidence intervals for group abundance for the catch-effort method.

Usage

        int.est.ce(samp, ci.type = "boot.nonpar", nboot=999, vlevels=c(0.025,0.975), plot = FALSE, seed = NULL, ...)

Arguments

samp	object of class 'sample.rm´.
ci.type	The type of method used to estimate the confidence interval. The only valid option is: * 'boot.nonpar' Non-parametric bootstrap
nboot	number of bootstrap replicates to be used in estimation
vlevels	confidence interval percentage levels
plot	if TRUE a histogram of the distribution of the boostrap estimates will be plotted. The plot will also show the confidence interval.
seed	the number passed to set.seed() to initialise random number generator
...	further optional parameters for the plot

Details

The parametric bootstrap method uses the estimate of N and of the detection probabilities delivered by point.est.ce. It generates bootstrap samples using a binomial distribution model using these point estimates as parameters. For each bootstrap sample the point estimate is calculated.

Value

An object of class 'int.est.ce´ containing the following items:

levels	percentage levels for confidence interval
ci	the confidence interval
boot.mean	mean of bootstrap estimates
boot.dbn	full set of nboot bootstrap estimates.
se	standard error
cv	coefficient of variation
ci.type	Equal to thee object 'ci.type' passed to the function
parents	Details of WiSP objects passed to function
created	Creation date and time
seed	Equal to the argument 'seed' passed to the function

Note

The warning ‘NA/Inf replaced by maximum positive value’ may occur; this seems usually to be because the numerical optimization routine tried searching in an infeasible region of the paramter space.

See Also

setpars.survey.rm, generate.sample.rm point.est.ce, set.seed

Examples

rm.reg<-generate.region(x.length=100, y.width=50)

rm.dens <- generate.density(rm.reg)
rm.poppars<-setpars.population(density.pop = rm.dens, number.groups = 100,
                              size.method = "poisson", size.min = 1, size.max = 5,
                              size.mean = 1, exposure.method = "beta", exposure.min = 2,
                              exposure.max = 10, exposure.mean = 3, exposure.shape = 0.5,
                              type.values=c("Male","Female"), type.prob=c(0.48,0.52))
rm.pop<-generate.population(rm.poppars)

rm.des<-generate.design.rm(rm.reg, n.occ = 5, effort=c(1,2,3,2,1))
rm.survpars<-setpars.survey.rm(pop=rm.pop, des=rm.des, pmin=0.03, pmax=0.95, improvement=0.05)
rm.samp<-generate.sample.rm(rm.survpars)

# Catch-effort method
ce.ci<-int.est.ce(rm.samp)
summary(ce.ci)
plot(ce.ci)

DistanceDevelopment/WiSP documentation built on Sept. 18, 2020, 2:55 p.m.