point.sim.ce: Catch-Effort Method: Simulation

In dill/wisp: Wildlife Simulation Package (wisp)

Description

This function simulates samples and estimates abundance and related parameters for each simulated sample object.

Usage

        point.sim.ce(pop.spec, survey.spec, design.spec, B = 999, plot = T, show = FALSE, seed = NULL, ...)

Arguments

pop.spec	population specification; either an object of class 'population' or 'pars.population'
survey.spec	survey specification; an object of class 'pars.survey.ce'
design.spec	design specification; an object of class 'design.ce'
B	number of simulations required
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 simulated samples displayed on the plot.
show	if TRUE displays the plots of cumulative removals for each simulated survey as it is run
seed	Number passed to set.seed() to initialise random number generator
...	extra plot arguments

Details

This function simulates sample objects of class 'sample.rm' by simulating 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).

Value

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

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)
true	The true (simulated) values of group abundance, animal abundance and mean group size
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

See Also

setpars.population, setpars.survey.rm point.est.ce, set.seed

Examples

rm.reg<-generate.region(x.length=100, y.width=50)

rm.dens <- generate.density(rm.reg)
rm.poppars<-setpars.population(density.pop = rm.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))
rm.pop<-generate.population(rm.poppars)

rm.des<-generate.design.rm(rm.reg, n.occ = 5, effort=c(1,2,3,2,1))
rm.survpars<-setpars.survey.rm(pop=rm.pop, des=rm.des, pmin=0.03, pmax=0.95, improvement=0.05)
rm.samp<-generate.sample.rm(rm.survpars)

#Randomise population and survey
ce.sim<-point.sim.ce(pop.spec=rm.poppars, survey.spec=rm.survpars, design.spec=rm.des, B=9,
                     seed=NULL, plot=FALSE, show=TRUE)
summary(ce.sim)
plot(ce.sim)
plot(ce.sim, type = "hist")
plot(ce.sim, type = "box")

#Randomise survey only
ce.sim<-point.sim.ce(pop.spec=rm.pop, survey.spec=rm.survpars, design.spec=rm.des, B=999,
                     seed=NULL, plot=FALSE, show=FALSE)

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