obscure.sample.rm: Reducing a catch-effort sample object to the observed data

In dill/wisp: Wildlife Simulation Package (wisp)

Description

The function filters out all the unobserved information from a ‘sample.rm’ object, leaving only the observed data. It is useful when creating ‘sample.rm’ objects for exercises – when you don't want those doing the exercises to be able to see the whole population.

Usage

        obscure(sample)

Arguments

sample

sample object of class ‘sample.rm’

Details

This function removes from the ‘sample.ce’ object all data relating to animals and groups that were not detected – those for which ‘(sample$detected!=T | !is.na(sample$detected))’.

Value

obscure.sample.rm returns an object of class 'sample.rm´ which has the following elements:

population	object of class 'population´.
design	object of class 'design.rm´.
removal	two dimensional detection matrix that indicates which animal groups have been captured on which occasion. A 1 in row i, column j indicates that the i-th animal/group was removed the occasion before j; a 1 means that it was not. This object is redundant; all elements are 1; it is retained purely to conform with the structure of ‘sample.rm’ objects.
detected	two dimensional detection matrix that indicates which animal groups have been captured on which occasion. A 1 in row i, column k indicated that the i-th animal/group was detected and removed on occasion k.
parents	Details of WiSP objects passed to function
created	Creation date and time

See Also

generate.sample.rm, setpars.survey.rm summary.sample.rm, plot.sample.rm

Examples

reg<-generate.region(x.length = 50, y.width = 80)

den <- generate.density(reg)
pop.pars<-setpars.population(reg, density.pop = den, 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)
pop<-generate.population(pop.pars)
rm.des <- generate.design.rm(reg, n.occ = 4, effort = c(4,5,6,7))
rm.survpars<-setpars.survey.rm(pop = pop, des = rm.des,  pmin=0.03, pmax=0.95, improvement = 0.1)
rm.samp<-generate.sample.rm(rm.survpars)
     
# now strip the unobserved data out of the sample.ce object:
obs.rm.samp<-obscure.sample.rm(rm.samp)
# (Note: `obs.samp<-obscure(rm.samp)' has the same effect.)
plot(obs.rm.samp)
# (Note: the plot will only contain observations, so 'whole.population=T' has no effect)
summary(obs.rm.samp)
# (Note:... although the summary will be the same, as it only contains observations)

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