Description Usage Arguments Value References See Also Examples
This function estimates abundance and related parameters from a line transect method sample object (of class ‘sample.lt’).
1 |
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 |
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 |
Borchers, D.L., Buckland, S.T. and Zucchini, W. 2002. Estimating animal abundance: closed populations. Springer. London. 314pp.
setpars.survey.lt
, generate.sample.lt
summary.pars.survey.lt
, int.est.lt
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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)
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