point.est.crM0: Mark Recapture Method M0 Abundance Estimation: Point Estimate

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function estimates abundance and related parameters from a mark recapture method sample object (of class ‘sample.cr’), using the mark recapture model M0.

Usage

        point.est.crM0(samp, init.N = -1, Chapmod = FALSE, numerical = TRUE)

Arguments

sample	object of class 'sample.cr´.
init.N	starting value of N used in maximum likelihood optimisation routine
Chapmod	If TRUE, Chapman's modified estimator is used. Only works with 2 sample mark-recapture survey
numerical	if TRUE, estimation is by numerical maximisation of the log likelihood function

Value

An object of class point.sim.ce with the following elements:

sample	details of the object of class 'sample.cr', used to create the sample
Nhat.grp	MLE of group abundance
Nhat.ind	MLE of individual abundance (= Nhat.grp * Es)
phat	Estimate(s) of capture probability for the relevant model (try it and see)
Es	Estimate of mean group size (simple mean of observed group sizes)
log.likelihood	the value of the log-likelihood function at the maximum
res.Deviance	Residual deviance at MLE
AIC	Akaike´s Information Criterion
parents	Details of WiSP objects passed to function
created	Creation date and time

See Also

setpars.survey.cr, generate.sample.cr int.est.crM0

Examples

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)

# M0
cr.est.M0<-point.est.crM0(cr.samp)
summary(cr.est.M0)

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