point.est.pt: Point Transect Method Abundance Estimation: Point Estimate
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Value References See Also Examples

This function estimates abundance and related parameters from a point transect method sample object (of class ‘sample.pt’). It also plots a density histogram of observed radial distances with the estimated probability density function superimposed.

1	point.est.lt(sampl, plot = TRUE, title = TRUE, conditional = FALSE, model = "half.normal")

`sampl`	object of class 'sample.pt´
`plot`	if TRUE, a density histogram of observed radial distances with the estimated probability density function superimposed, is plotted
`title`	Flag telling if you want the title "Radial distance distribution and fitted detection function"
`conditional`	if FALSE, the full likelihood (Equation (7.32) of Borchers et al. 2002) is maximized; if TRUE the conditional likelihood (Equation (7.33) of Borchers et al. 2002) is maximized and abundance is estimated using Equation (7.34) of Borchers et al. 2002.
`model`	="half.normal" (no other detection function models have implemented yet)

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

`Nhat.ML.grp`	MLE of group abundance
`Nhat.ML.ind`	MLE of individual abundance (= Nhat.grp * Es)
`theta`	MLE of detection function parameter (sigma^2)
`esa`	MLE effective survey area: 2π\int_0^w x p(x)\,dx, where w is the trunction distance, and integration is from 0 to w.
`nbar`	"encounter rate" - the mean number of groups detected per point
`Es`	mean group size
`log.likelihood`	the value of the log-likelihood function at the maximum
`AIC`	Akaike´s Information Criterion
`fit.summary`	output from numerical minimization routine

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

setpars.survey.pt, summary.sample.pt, summary.pars.survey.pt, int.est.pt

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

#heterogeneous population
pt.poppars<-setpars.population(density.pop = pt.dens, number.groups = 1000, size.method = "poisson",
                               size.min = 1, size.max = 30, size.mean = 10, exposure.method = "beta",
                               exposure.min = 0, exposure.max = 1, exposure.mean = 0.4,
                               exposure.shape = 0.5, type.values=c("Male","Female"),
                               type.prob=c(0.48,0.52))
pt.pop<-generate.population(pt.poppars)

pt.despars<-setpars.design.pt(pt.reg, n.transects=8, n.units=32, visual.range=5)
pt.des<-generate.design.pt(pt.despars)

pt.survpars<-setpars.survey.pt(pt.pop, pt.des, disthalf.min=2, disthalf.max=4)
pt.samp<-generate.sample.pt(pt.survpars)

pt.est<-point.est.pt(pt.samp) 
summary(pt.est)
plot(pt.est)