sample.points: Sample points within each transect and filter with specified...
In DSpat: Spatial Modelling for Distance Sampling Data

Description Usage Arguments Details Value Author(s) See Also Examples

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.

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

`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

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

observation dataframe with fields label,x,y,distance for line label, x,y coordinates of the observation location and its perpendicular distance from the line

Jeff Laake

simCovariates,simPts,create.lines

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)

Loading required package: spatstat
Loading required package: spatstat.data
Loading required package: nlme
Loading required package: rpart

spatstat 1.64-1       (nickname: ‘Help you I can, yes!’) 
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Note: spatstat version 1.64-1 is out of date by more than 7 months; we recommend upgrading to the latest version.
Loading required package: RandomFields
Loading required package: sp
Loading required package: RandomFieldsUtils

Attaching package: ‘RandomFields’

The following object is masked from ‘package:RandomFieldsUtils’:

    RFoptions

The following object is masked from ‘package:nlme’:

    Variogram

This is DSpat 0.1.6

New output format of RFsimulate: S4 object of class 'RFsp';
for a bare, but faster array format use 'RFoptions(spConform=FALSE)'.
Warning messages:
1: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
2: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
3: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
4: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
5: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
6: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
7: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
8: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
9: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated
10: In `[<-`(`*tmp*`, i, value = gpc) :
  implicit list embedding of S4 objects is deprecated