Description Usage Arguments Details Value References See Also Examples
This function estimates a confidence interval for group abundance from mark recapture method Mt.
1 2 |
samp |
object of class 'sample.crMt´. |
init.N |
starting value of N used by maximum likelihood optimisation routine |
vlevels |
vector of percentage levels for confidence intervals. |
ci.type |
method for constructing the confidence interval. Possible methods are * 'boot.par´ gives a parametric bootstrap CI, * 'boot.nonpar´ gives a nonparametric bootstrap CI, |
nboot |
number of bootstrap replications. |
plot |
if TRUE the distribution of the group abundance estimates is plotted. |
seed |
the number passed to set.seed() to initialise random number generator |
Details of the bootstrap methods are given in Borchers et al. (2002), pp112-113.
An object of class 'int.est.crMt´ 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. |
init.N |
starting value for N used in the maximum likelihood optimisation routine |
se |
standard error |
cv |
coefficient of variation |
ci.type |
Equal to thee object 'ci.type' 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.
setpars.survey.cr
, generate.sample.cr
point.est.crMt
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | cr.reg<-generate.region(x.length=100, y.width=50)
cr.dens <- generate.density(cr.reg)
cr.poppars<-setpars.population(density.pop = cr.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 = 3, exposure.shape = 0.5,
type.values = c("Male","Female"), type.prob = c(0.48,0.52))
cr.pop<-generate.population(cr.poppars)
cr.des<-generate.design.cr(cr.reg, n.occ = 4)
cr.survpars<-setpars.survey.cr(cr.pop, cr.des, pmin.unmarked=0.00001, pmax.unmarked=0.5, improvement=0.01)
cr.samp<-generate.sample.cr(cr.survpars)
# Mt
cr.ci.Mt<-int.est.crMt(cr.samp)
summary(cr.ci.Mt)
plot(cr.ci.Mt)
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