int.est.crMh: Mark Recapture Model Mh Abundance Estimation: Interval...

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function estimates a confidence interval for group abundance from mark recapture method Mh.

Usage

        int.est.crMh(samp, num.mix = 2, init.N = -1, ci.type = "boot.nonpar", nboot = 999,
                     vlevels = c(0.025, 0.975), plot = FALSE, seed = NULL)

Arguments

samp	object of class 'sample.crMh´.
num.mix	the number of mixtures of animal groups
init.N	starting value for N used in the maximum likelihood optimisation routine
ci.type	method for constructing the confidence interval. Possible methods are * 'boot.nonpar´ gives a nonparametric bootstrap CI,
nboot	number of bootstrap replications.
vlevels	vector of percentage levels for confidence intervals.
plot	if TRUE the distribution of group abundance estimates from the bootstrap resamples is plotted.
seed	the number passed to set.seed() to initialise random number generator

Details

Details of the bootstrap methods are given in Borchers et al. (2002), pp112-113.

Value

An object of class 'int.est.crMh´ 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.
init.N	Equal to the object init.N passed to the function
se	standard error
cv	coefficient of variation
ci.type	Equal to the 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

References

Borchers, D.L., Buckland, S.T. and Zucchini, W. 2002. Estimating animal abundance: closed populations. Springer. London. 314pp.

See Also

setpars.survey.cr, generate.sample.cr point.est.crMh

Examples

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

cr.dens <- generate.density(cr.reg)
cr.poppars<-setpars.population(density.pop = cr.dens, number.groups = 1000, 
                               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))
cr.pop<-generate.population(cr.poppars)

cr.des<-generate.design.cr(cr.reg, n.occ = 4)
cr.survpars<-setpars.survey.cr(cr.pop, cr.des, pmin.unmarked=0.00001, pmax.unmarked=0.5, improvement=0.01)
cr.samp<-generate.sample.cr(cr.survpars)

# Mh
cr.ci.Mh<-int.est.crMh(cr.samp, nboot=3)
summary(cr.ci.Mh)
plot(cr.ci.Mh)

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