generate.sample.rm: Survey Sample of Removal Methods
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value See Also Examples

This function uses the data stored in the 'pars.survey.rm´ object to create a removal method sample object.

1	generate.sample.rm(pars.survey.rm, seed = NULL)

`pars.survey.rm`	object returned by the function `setpars.survey.rm` defining the survey parameters.
`seed`	the number passed to set.seed() to initialise random number generator

A removal sample object is generated by simulating a removal survey of the population specified in generate.sample.rm. The function setpars.survey.rm must have been run to create a ‘pars.survey.rm’ object, which contains the survey specifications.

generate.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 `0` means that it was not.
`detected`	two dimensional detection matrix that indicates which animal groups have been detected and consequently removed on which occasion. A `1` in row i, column j indicates that the i-th animal/group was detected on occasion j; a `0` means that it was not.
`parents`	Details of WiSP objects passed to function
`created`	Creation date and time
`seed`	Equal to the argument 'seed' passed to the function

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

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)
plot(rm.samp)
plot(rm.samp,whole.population=TRUE)