int.est.no: Point-to-Nearest-Object Method Abundance Estimation: Interval...

In dill/wisp: Wildlife Simulation Package (wisp)

Description

Calculates parametric or non-parametric bootstrap confidence intervals of abundance for the point-to-nearest-object method.

Usage

    int.est.no(samp, vlevels = c(0.025, 0.975), ci.type = "boot.nonpar", 
    nboot = 999, plot = T, ...)

Arguments

samp	object of class 'sample.no´, generated with function generate.sample.no
vlevels	vector of percentage levels for confidence intervals.
ci.type	method for constructing the confidence interval. Possible methods are * 'boot.par´ for using parametric bootstrap, * 'boot.nonpar´ for using nonparametric bootstrap.
nboot	number of bootstrap replications.
plot	if TRUE the bootstrap distribution of the estimator of N is plotted using kernel smoothing. The confidence interval will also be plotted.
...	additional plot parameters

Details

The parametric bootstrap generates point-to-nearest-object parametrically from the distribution of these distances that applies under the assumption that animal groups are uniformly distributed in the survey region. The nonparametric bootstrap resamples with replacement from the observed distances. The confidence interval is calculated using the percentile method.

Value

An object of class 'int.est.no´ 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.

See Also

point.est.no, generate.sample.no to create sample objects, summary.sample.no, plot.sample.no

Examples

     
     # generating all necessary objects
     myreg<-generate.region(x.length = 50, y.width = 80)
          
     mydens <- generate.density()
     mypoppars<-setpars.population(myreg, density.pop = mydens, 
                          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 = 6,
                          exposure.shape = 1)
     mypop<-generate.population(mypoppars)
     mydes <- generate.design.no(myreg, n.points = 4)
     mysamp<-generate.sample.no(mypop, mydes, with.neighbours=T)
          
     # nonparametric bootstrap
     my.int.est<-int.est.no(mysamp, ci.type="boot.nonpar")
     
     # parametric bootstrap
     my.int.est<-int.est.no(mysamp, ci.type="boot.par")
     

dill/wisp documentation built on May 15, 2019, 8:31 a.m.