generate.sample.lt: Line Transect Method Survey Data Generation
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.lt´ object to create a line transect sample object.

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

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

A line transect method sample object is generated by simulating a line transect survey of the population specified in generate.sample.lt. The function setpars.survey.lt must have been run to create a ‘pars.survey.lt’ object, which contains the survey specifations.

generate.sample.lt returns an object of class 'sample.lt´ which has the following elements:

`population`	object of class 'population´.
`design`	object of class 'design.lt´.
`detected`	vector indicating which animal groups have been detected. `NA` means that an animal group was outside all survey units and could not be detected. `0` indicates that an animal group was inside a survey unit however has not been detected. `1` means that an animal group was inside a survey unit and has been detected.
`distance`	vector of perpendicular distances of animal groups inside the survey units from the respective transect paths.
`transect`	vector of transect path numbers of detectable animal groups.
`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.lt, summary.sample.lt plot.sample.lt

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

#heterogeneous population
lt.poppars<-setpars.population(density.pop = lt.dens, number.groups = 1000,
                              size.method = "poisson", size.min = 1, size.max = 30,
                              size.mean = 10, exposure.method = "beta", exposure.min = 0,
                              exposure.max = 1, exposure.mean = 0.4, exposure.shape = 0.5,
                              type.values=c("Male","Female"), type.prob=c(0.48,0.52))
lt.pop<-generate.population(lt.poppars)
lt.despars<-setpars.design.lt(lt.reg, n.transects=10, n.units=10, visual.range=4, percent.on.effort=1)
lt.des<-generate.design.lt(lt.despars, seed=3)
lt.survpars<-setpars.survey.lt(lt.pop, lt.des, disthalf.min=2, disthalf.max=4)

lt.samp<-generate.sample.lt(lt.survpars)
summary(lt.samp)
plot(lt.samp)