int.est.pl: Plot Sampling Method Abundance Estimation: Interval Estimate

Description Usage Arguments Value See Also Examples

Description

This function calculates confidence intervals for group abundance for the plot sampling method.

Usage

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        int.est.pl(samp, HT=FALSE, vlevels = c(0.025, 0.975), ci.type = "boot.nonpar", 
				nboot = 999, plot = TRUE, seed = NULL, ...)

Arguments

samp

object of class 'sample.pl´.

HT

if FALSE, the abundance estimate produced is the MLE, if TRUE it is the Horvitz-Thompson estimate.

vlevels

vector of percentage levels for confidence interval.

ci.type

method for constructing the confidence interval. Possible methods are * 'normal´ for a CI based on assumed normality of the estimator, * 'boot.par´ for a parametric bootstrap CI, * 'boot.nonpar´ for a nonparametric bootstrap CI.

nboot

number of bootstrap replications.

plot

if true the distribution of the estimator of N is to be plotted.

seed

the number passed to set.seed() to initialise random number generator

...

other plot parameters

Value

An object of class 'int.est.pl´ containing the following items:

levels

percentage levels for confidence interval

ci

the confidence interval

boot.mean

mean of bootstrap estimates

boot.dbn

full set of nboot bootstrap estimates.

See Also

generate.sample.pl, point.est.pl summary.sample.pl, plot.sample.pl set.seed

Examples

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pl.reg <- generate.region(x.length = 100, y.width = 50)
pl.dens <- generate.density(pl.reg)
pl.poppars<-setpars.population(density.pop=pl.dens, number.groups = 100, size.method = "poisson", 
						size.min = 1, size.max = 5, size.mean = 1, exposure.method = "beta", exposure.min = 2, 
						exposure.max = 10, exposure.mean = 6, exposure.shape = 1)
pl.pop <- generate.population(pl.poppars, seed=456)

pl.despars<-setpars.design.pl(pl.reg, n.interval.x = 10, n.interval.y = 20,method = "random", area.covered = 0.2)
pl.des <- generate.design.pl(pl.despars, seed=789)
pl.samp<-generate.sample.pl(pl.pop, pl.des, seed=101112)

# normal-based CI
pl.int.est.norm<-int.est.pl(pl.samp, vlevels = c(0.025, 0.975), ci.type = "normal", nboot = 99, plot = T, seed=1) 
summary(pl.int.est.norm)
plot(pl.int.est.norm)

# parametric bootstrap
pl.int.est.pbs<-int.est.pl(pl.samp, vlevels = c(0.025, 0.975), ci.type = "boot.par", nboot = 99, plot = F, seed=NULL) 
summary(pl.int.est.pbs)
plot(pl.int.est.pbs, nclass=20)

# nonparametric bootstrap
pl.int.est.npbs<-int.est.pl(pl.samp, vlevels = c(0.025, 0.975), ci.type = "boot.nonpar", nboot = 99, plot = F, seed=3)
summary(pl.int.est.npbs)
plot(pl.int.est.npbs)

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