Description Usage Arguments Details Value See Also Examples
This function simulates surveys and estimates abundance and related parameters for each simulated survey.
1 |
pop.spec |
population specification; either an object of class 'population' or 'pars.population' |
design.spec |
design specification; either an object of class 'design.pl' or 'pars.design.no' |
B |
number of simulations required |
HT |
argument in point.est.pl - HT: if FALSE, the abundance estimate produced is the MLE, if TRUE it is the Horvitz-Thompson estimate. |
seed |
Equal to the argument 'seed' passed to the function |
show |
if TRUE displays the position of plots and the observations 'seen', for each simulated survey as it is run |
plot |
if TRUE a histogram of the group abundance point estimates obtained from each sample object is produced with true group abundance and the mean of the simulated abundance estimates displayed on the plot |
This function simulates surveys by simulating from the state model if pop.spec is of class 'pars.population' (using pop.spec to generate new populations on each simulation ) and if design.spec is of class 'pars.design.dp' from the design (using design.spec to generate new design realisations 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 |
HT |
Equal to the argument 'model.sel' passed to the function |
random.pop |
TRUE if population is randomised |
random.design |
TRUE is 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.design.pl
point.est.pl
, 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 26 27 28 29 30 | pl.reg <- generate.region(x.length = 100, y.width = 50)
pl.dens <- generate.density(pl.reg)
pl.poppars<-setpars.population(density.pop=pl.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 = 6,
exposure.shape = 1)
pl.pop <- generate.population(pl.poppars, seed=456)
pl.despars<-setpars.design.pl(pl.reg, n.interval.x = 10, n.interval.y = 20,
method = "random", area.covered = 0.2)
pl.des <- generate.design.pl(pl.despars, seed=789)
pl.samp<-generate.sample.pl(pl.pop, pl.des, seed=101112)
plot(pl.samp, whole.population=TRUE)
# To simulate with fixed population and random design realizations:
pl.sim<-point.sim.pl(pop.spec=pl.pop, design.spec=pl.despars, B=99, show=TRUE)
summary(pl.sim)
plot(pl.sim)
plot(pl.sim, type="hist")
plot(pl.sim, type="box")
# To simulate with fixed design realization and random animal locations:
pl.sim<-point.sim.pl(pop.spec=pl.poppars, design.spec=pl.des, B=99,)
summary(pl.sim)
plot(pl.sim)
# To simulate with random population and random design realizations:
pl.sim<-point.sim.pl(pop.spec=pl.poppars, design.spec=pl.despars, B=99)
summary(pl.sim)
plot(pl.sim)
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