int.est.lt: Line Transect Method Abundance Estimation: Interval Estimate

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function estimates a confidence interval for group abundance for the line transect method.

Usage

        int.est.lt(sampl, ci.type = "boot.nonpar", nboot = 999, vlevels = c(0.025, 0.975), conditional = TRUE, model = "half.normal", plot = TRUE,  
show.all = FALSE, seed = NULL, ...)

Arguments

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

Details

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.

Value

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 point.est.lt) each containing the full set of nboot estimates of that parameter. (This is useful if you want to create a plot of the bootstrap distribution of the estimates and control bin width etc.)
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

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 point.est.lt for point estimation, set.seed

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)

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)

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