point.est.crMt: Mark Recapture Method Mt Abundance Estimation: Point Estimate
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Value See Also Examples

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

1	point.est.crMt(samp.cr, init.N = -1)

`sample`	object of class 'sample.cr.´
`init.N`	starting value of N used in maximum likelihood optimisation routine

An object of class 'point.est.crMt´ containing the following items:

`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`	Value of log-likelihood at MLE
`res.Deviance`	Residual deviance at MLE
`AIC`	Akaike's information criterion
`init.N`	starting value of N used in maximum likelihood optimisation routine
`parents`	Details of WiSP objects passed to function
`created`	Creation date and time

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

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.est.Mt<-point.est.crMt(cr.samp)
summary(cr.est.Mt)