Description Usage Arguments Details Value References See Also Examples
This function estimates abundance and related parameters from a plot sampling method sample object (of class ‘sample.pl’).
1 | point.est.pl(samp, HT=FALSE)
|
samp |
object of class 'sample.pl´. |
HT |
if FALSE, the abundance estimate produced is the MLE, if TRUE it is the Horvitz-Thompson estimate. |
The MLE (given immediately after Equation (4.3) in Borchers et al. 2002) is
Nhat = n / pc, where
n = number of detected animals
pc = percentage of area covered
The Horvitz-Thompson estimator is given in Equation (4.1) of Borchers et al. 2002:
Nhat = sum (n_j / p_j), where
n_j = number of animals detected in plot j p_j = pc_j * J, with pc_j = percentage of area covered by plot j and J = number of plots
NOTE: In the current implementation the percentage of the area covered by any plot j is the same for all j (all plots have equal size) so that the Horvitz-Thompson estimator is equal to the ML estimator.
An object of class 'point.est.pl´ containing the following items:
sample |
The sample used in estimation (an object of class 'sample.pl') |
Nhat.grp |
Either MLE or HT estimator of group abundance |
Nhat.ind |
Either MLE or HT estimator of individual abundance (= Nhat.grp * Es) |
Es |
mean group size |
HT |
Equal to the argument 'HT' passed to the function |
parents |
Details of WiSP objects passed to 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.
generate.sample.pl
, int.est.pl
,
summary.sample.pl
, plot.sample.pl
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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)
pl.est<-point.est.pl(pl.samp)
summary(pl.est)
#With Horvitz-Thompson estimator
HT.pl.est<-point.est.pl(pl.samp, HT=TRUE)
summary(HT.pl.est)
|
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