point.est.pl: Plot Sampling Method Abundance Estimation: Point Estimate
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value References See Also Examples

This function estimates abundance and related parameters from a plot sampling method sample object (of class ‘sample.pl’).

1	point.est.pl(samp, HT=FALSE)

`samp`	object of class 'sample.pl´.
`HT`	if FALSE, the abundance estimate produced is the MLE, if TRUE it is the Horvitz-Thompson estimate.

The MLE (given immediately after Equation (4.3) in Borchers et al. 2002) is

Nhat = n / pc, where

n = number of detected animals
pc = percentage of area covered

The Horvitz-Thompson estimator is given in Equation (4.1) of Borchers et al. 2002:

Nhat = sum (n_j / p_j), where

n_j = number of animals detected in plot j p_j = pc_j * J, with pc_j = percentage of area covered by plot j and J = number of plots

NOTE: In the current implementation the percentage of the area covered by any plot j is the same for all j (all plots have equal size) so that the Horvitz-Thompson estimator is equal to the ML estimator.

An object of class 'point.est.pl´ containing the following items:

`sample`	The sample used in estimation (an object of class 'sample.pl')
`Nhat.grp`	Either MLE or HT estimator of group abundance
`Nhat.ind`	Either MLE or HT estimator of individual abundance (= Nhat.grp * Es)
`Es`	mean group size
`HT`	Equal to the argument 'HT' 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.

generate.sample.pl, int.est.pl, summary.sample.pl, plot.sample.pl

pl.reg <- generate.region(x.length = 100, y.width = 50)
pl.dens <- generate.density(pl.reg)
pl.poppars<-setpars.population(density.pop=pl.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 = 6, exposure.shape = 1)
pl.pop <- generate.population(pl.poppars, seed=456)
pl.despars<-setpars.design.pl(pl.reg, n.interval.x = 10, n.interval.y = 20,method = "random", area.covered = 0.2)

pl.des <- generate.design.pl(pl.despars, seed=789)
pl.samp<-generate.sample.pl(pl.pop, pl.des, seed=101112)

pl.est<-point.est.pl(pl.samp)
summary(pl.est)

#With Horvitz-Thompson estimator
HT.pl.est<-point.est.pl(pl.samp, HT=TRUE)
summary(HT.pl.est)