int.est.rm: Removal Method Abundance Estimation: Interval Estimate

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function estimates the animal population size for the current survey sample of the simple removal method.

Usage

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

Arguments

function (samp, ci.type="boot.nonpar", nboot=999, vlevels=c(0.025,0.975), numerical=TRUE, plot=FALSE, seed=NULL, ...)

samp	object of class 'sample.rm´.
ci.type	method for constructing the confidence interval. Possible methods are * 'boot.par´ for parametric bootstrap CI, * 'boot.nonpar´ for nonparametric bootstrap CI, or * 'profile´ to get a profile likelihood CI.
nboot	number of bootstrap replications.
vlevels	vector of percentage levels for confidence intervals.
numerical	if TRUE, estimation is by numerical maximisation of the log likelihood function. Otherwise, estimation is by analytical methods
plot	if true the distribution of the estimator of N is plotted (bootstrap methods only).
seed	the number passed to set.seed() to initialise random number generator
...	other plot parameters

Value

An object of class 'int.est.rm´ 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 the object 'ci.type' passed to the function
proflik	(not currently used)
numerical	Equal to the object 'numerical' 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.rm, 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)

rm.ci<-int.est.rm(rm.samp)
summary(rm.ci)
plot(rm.ci, est="Nhat.grp")
plot(rm.ci, est="Es", type="hist")
plot(rm.ci, est="Nhat.ind", type="box")

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