int.est.crMt: Mark Recapture Model Mt Abundance Estimation: Interval...
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value References See Also Examples

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

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

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

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

An object of class 'int.est.crMt´ 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`	starting value for N used in the maximum likelihood optimisation routine
`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

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

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

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 = 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))
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)

# Mt
cr.ci.Mt<-int.est.crMt(cr.samp)
summary(cr.ci.Mt)
plot(cr.ci.Mt)