DSpat (version 0.1.6)

sample.points: Sample points within each transect and filter with specified detection function

Description

Create a dataframe of observations by simulating distance sampling of a point process with a systematic set of lines over a rectangular grid. The transects, lines and point process(points.ppp) are input arguments. Detection of observations is specified with a user-defined detection function which takes a distance vector and set of parameters det.par as its arguments.

Usage

sample.points(transects,lines,points.ppp,detfct=NULL,det.par=NULL, det.formula=~1,covariates=NULL)
hndetfct(x,scale)

Arguments

transects

list of transect polygons

lines

dataframe of lines

points.ppp

simulated point process

detfct

detection function name

det.par

parameters for the detection function

det.formula

formula of covariates to use for scale of distance if det.formula=~-1, uses a strip transect

covariates

a matrix with columns x,y and any number of covariates x and y are the mid points of the grid cells; the order of the rows must match the formulation for function im

x

perpendicular distance for detection function

scale

scale for detection function

Value

of the observation location and its perpendicular distance from the line

Details

Definition for half-normal detection function (hndetfct) is exp(-(x^2/(2*exp(scale)^2)))

See Also

simCovariates,simPts,create.lines

Examples

study.area=owin(xrange=c(0,100),yrange=c(0,100))
hab.range=30
probs=c(1/3,2/3)
covariates = simCovariates(hab.range, probs)
xlines=create.lines(study.area,nlines=10,width=5,angle=45)
ls=lines_to_strips(xlines,study.area)
plot(ls$lines,lty=2)
plot(owin(poly=ls$transects),add=TRUE)
xpp=simPts(covariates=covariates,int.formula=~factor(habitat),int.par=c(0,1,2),EN=1000)
obs=sample.points(transects=ls$transects,lines=xlines,points.ppp=xpp,
                    hndetfct,c(1),covariates=covariates)
plot(ppp(x=obs$x,y=obs$y,window=study.area),add=TRUE,pch=20)