point.est.lt: Line Transect Method Abundance Estimation: Point Estimate

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function estimates abundance and related parameters from a line transect method sample object (of class ‘sample.lt’).

Usage

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

Arguments

sampl	object of class 'sample.lt´
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 "Perpendicular distance distribution and fitted detection function"
conditional	if TRUE the conditional likelihood (Equation (7.8) of Borchers et al. 2002) is maximized and abundance is estimated using Equation (7.12) of Borchers et al. 2002; if FALSE, the full likelihood (Equation (7.10) of Borchers et al. 2002) is maximized.
model	="half.normal" or ="hazard.rate" for detection function form

Value

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

sample	details of the object of class 'sample.cr', used to create the sample
model	the model used to fit the detection function
conditional	Equal to the argument 'conditional' passed to the function
Nhat.grp	MLE of group abundance
Nhat.ind	MLE of individual abundance (= Nhat.grp * Es)
theta	MLE of detection function parameter
mu	MLE of effective strip half-width
nL	"encounter rate" - the number of groups detected per unit distance surveyed along lines
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
parents	Details of WiSP objects passed to function
created	Creation date and time

References

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

See Also

setpars.survey.lt, generate.sample.lt summary.pars.survey.lt, int.est.lt

Examples

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

#heterogeneous population
lt.poppars<-setpars.population(density.pop = lt.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))
lt.pop<-generate.population(lt.poppars)

lt.despars<-setpars.design.lt(lt.reg, n.transects=10, n.units=10, visual.range=4, percent.on.effort=1)
lt.des<-generate.design.lt(lt.despars, seed=3)
lt.survpars<-setpars.survey.lt(lt.pop, lt.des, disthalf.min=2, disthalf.max=4)
lt.samp<-generate.sample.lt(lt.survpars)

# half-normal model
lt.est<-point.est.lt(lt.samp) 
summary(lt.est)

# hazard rate model
lt.est.hr<-point.est.lt(lt.samp, model="hazard.rate") 
summary(lt.est.hr)

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