obscure.sample.lt: Reducing a line transect 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.lt’ object, leaving only the observed data. It is useful when creating sample.lt 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

Line transect sample object (of class ‘sample.lt’).

Details

This function removes from the ‘sample.lt’ 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.lt returns an object of class 'sample.lt´ 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.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. This object is redundant; all elements are 1; it is retained purely to conform with the structure of ‘sample.lt’ objects.
distance	vector of perpendicular distances of detected animal groups inside the survey units from the respective transect paths.
transect	vector of transect path numbers of detected animal groups.
parents	Details of WiSP objects passed to function
created	Creation date and time

See Also

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

Examples

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

dens <- generate.density(reg)
pop.pars<-setpars.population(reg, density.pop = 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)
pop<-generate.population(pop.pars)

lt.des.pars<-setpars.design.lt(reg, n.transects = 4, n.units = 20, visual.range = 2, percent.on.effort = 0.7)
lt.des<-generate.design.lt(lt.des.pars)
lt.surv.pars<-setpars.survey.lt(pop, lt.des, disthalf.min = 1, disthalf.max = 2)
lt.samp<-generate.sample.lt(lt.surv.pars)

# now strip the unobserved data out of the sample.lt object:
lt.obs.samp<-obscure(lt.samp)
# (Note: `lt.obs.samp<-obscure.sample.lt(lt.samp)' has the same effect.)
plot(lt.obs.samp,whole.population=TRUE)
# note that `whole.population=T' has no effect - because all unobserved data is gone
summary(lt.obs.samp)
# ... but the summary is the same - because summary involves only the observed data

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