point.sim.crM0: Mark-Recapture Method M0 : Simulation
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value See Also Examples

This function simulates mark-recapture samples and estimates abundance and related parameters for each simulated samples using the mark-recpature model M0.

1 2	point.sim.crM0(pop.spec, survey.spec, design.spec, B = 99, init.N = -1, seed=NULL, Chapmod = FALSE, numerical=TRUE, plot=FALSE)

`pop.spec`	population specification; either an object of class 'population' or 'pars.population'
`survey.spec`	survey specification; an object of class 'pars.survey.cr'
`design.spec`	design specification; an object of class 'design.cr'
`B`	number of simulations required
`init.N`	starting value of N used by the maximum likelihood opitimisation routine (see point.est.crM0)
`seed`	Number passed to set.seed() to initialise random number generator
`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
`plot`	if TRUE, a histogram of the group abundance point estimates obtained from each sample is produced with true group abundance and the mean of the abundance estimates from the simulations displayed on the plot.

This function simulates surveys from the observation model (using survey.spec) and if pop.spec is of class 'pars.population' from the state model (using pop.spec to generate new populations on each simulation).

An object of class point.sim.crM0, with the following elements:

`est`	A results matrix, each row of which contains the following values:
`Nhat.grp`	MLE of group abundance
`Nhat.ind`	MLE of individual abundance (= Nhat.grp * Es)
`Es`	Estimate of mean group size (simple mean of observed group sizes)
`phat`	Estimate(s) of capture probability for the relevant model (try it and see)
`log.Likelihood`	Value of log-likelihood at MLE
`res.Deviance`	Residual deviance at MLE
`AIC`	Akaike's information criterion
`true`	The true (simulated) values of group abundance, animal abundance and mean group size
`numerical`	Equal to the argument 'numerical' passed to the function
`init.N`	Equal to the argument 'init.N' passed to the function
`Chapmod`	Equal to the argument 'Chapmod' passed to the function
`random.pop`	TRUE if population is randomised
`random.design`	TRUE if design is randomised
`parents`	Details of WiSP objects passed to function
`created`	Creation date and time
`seed`	Equal to the argument 'seed' passed to the function

setpars.population, setpars.survey.cr point.est.crM0, set.seed

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)

#Randomise population and survey
cr.sim.M0<- point.sim.crM0(pop.spec=cr.poppars, survey.spec=cr.survpars, design.spec=cr.des, B = 99) 
summary(cr.sim.M0)
plot(cr.sim.M0)
plot(cr.sim.M0, type="hist")
plot(cr.sim.M0, type="box")

#Randomise survey only
cr.sim.M0<- point.sim.crM0(pop.spec=cr.pop, survey.spec=cr.survpars, design.spec=cr.des, B = 99) 
summary(cr.sim.M0)
plot(cr.sim.M0)