obscure.sample.pt: Reducing a point transect sample object to the observed data
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value See Also Examples

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

1 2	obscure(sample) obscure.sample.pt(sample)

sample

Point transect sample object (of class ‘sample.pt’).

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

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

`population`	object of class 'population´, but containing only data for observed members of the population.
`design`	object of class 'design.pt´.
`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. This object is redundant; all elements are `1`; it is retained purely to conform with the structure of ‘sample.pt’ objects.
`distance`	vector of perpendicular distances of detected animal groups inside the survey units from the respective points.
`transect`	vector of point numbers from which animal groups were detected.

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

     # First generate a population and sample it:
     myreg<-generate.region(x.length = 50, y.width = 80)
     mydens <- generate.density()
     mypoppars<-setpars.population(myreg, density.pop = mydens, 
                        number.groups = 500, 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)
     mypop<-generate.population(mypoppars)
     mydespars<-setpars.design.pt(myreg, n.transects = 4, n.units = 20, visual.range = 2.5)
     set.seed(1066)
     mydes<-generate.design.pt(mydespars)
     mysurvpars<-setpars.survey.pt(mypop, mydes, disthalf.min = 1.5, disthalf.max = 2)
     mysamp<-generate.sample.pt(mysurvpars)
     plot(mysamp,whole.population=T)
     summary(mysamp)
     # now strip the unobserved data out of the sample.pt object:
     obs.samp<-obscure(mysamp)
     plot(obs.samp,whole.population=T)
     # note that `whole.population=T' has no effect - because all unobserved data is gone
     summary(obs.samp)
     # ... but the summary is the same - because summary involves only the observed data