Description Usage Arguments Details Value See Also Examples
This function simulates samples and estimates abundance and related parameters for each simulated sample object.
1 |
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 |
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).
An object of class point.sim.ce 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) |
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 |
setpars.population
, setpars.survey.rm
point.est.ce
, set.seed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | 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)
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