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 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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)
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