Description Usage Arguments Details Value References See Also Examples
This function estimates a confidence interval for group abundance for the line transect method.
1 2 |
sampl |
object of class 'sample.lt´. |
ci.type |
="boot.nonpar" (no other methods implemented yet) |
nboot |
number bootstrap replicates |
vlevels |
percentage levels for confidence iterval |
conditional |
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; if FALSE, the full likelihood (Equation (7.32) of Borchers et al. 2002) is maximized. |
model |
="half.normal" or "hazard.rate" |
plot |
=T if you want a plot of boostrap distribution of Nhat.grp |
plot.all.fits |
=F if you want to see the fit for every bootstrap sample as it is fitted. |
seed |
the number passed to set.seed() to initialise random number generator |
... |
other plot arguments |
The nonparametric bootstrap method resamples transects with replacement (each resample has the same number of transects as were in the original sampele). Details of the method are given in Borchers et al. (2002), p146.
An object of class 'int.est.lt´ containing the following items:
levels |
percentage levels for confidence interval |
ci |
the confidence interval |
boot.mean |
mean of bootstrap estimates |
boot.dbn |
a list with components "Nhat.grp", "Nhat.ind","theta", "mu", "nL" and "Es" (see |
se |
standard error |
cv |
coefficient of variation |
ci.type |
Equal to the object 'ci.type' passed to the function |
conditional |
Equal to the object 'conditional' passed to the function |
parents |
Details of WiSP objects passed to the 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.
setpars.survey.lt
, generate.sample.lt
point.est.lt
for point estimation, set.seed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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)
lt.ci<-int.est.lt(lt.samp, vlevels=c(0.025, 0.975), ci.type="boot.nonpar", nboot=99, plot=T, seed=NULL, model="hazard.rate")
summary(lt.ci)
plot(lt.ci)
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