DSpat (version 0.1.6)

simDSpat: Simulate a distance sample from a specified spatial point process

Description

This is a wrapper function that calls all of the functions needed to simulate and sample a point process over a defined study.area with a specified covariates on a grid. In sequence it calls create.lines, lines_to_strips, simPts, and sample.points.

Usage

simDSpat(study.area=owin(xrange=c(0,100),yrange=c(0,100)),covariates, angle=90,nlines=10,spacing=10,width=1,int.formula=~1, int.par=1,model="exp",cor.par=NULL,EN=1000,detfct=hndetfct, det.formula=~1,det.par=log(width/3),showplot=FALSE,showlines=FALSE, showpts=FALSE,pts=NULL,...)

Arguments

study.area

owin class defining area

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

angle

angle of rotation in degrees anticlockwise from x-axis

nlines

number of lines

spacing

spacing distance between centerlines

width

full transect width

int.formula

formula for deriving expected intensity from covariates

int.par

parameters for intensity formula

model

either "exp" or "gauss" for exponential or Gaussian correlation

cor.par

parameters controlling clustering of points cor.par[1] $sigma^2$ cor.par[2]=$alpha$ where cov(y1,y2)=$sigma^2*exp(-d^p/alpha)$ and d is the distance between y1 and y2 and p=1 for exp and p=2 for gauss; if it is not specified then no additional clustering is included.

EN

expected number of points

detfct

detection function name

det.formula

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

det.par

parameters for the detection function

showplot

if TRUE show plot of the simulated points

showlines

if TRUE show lines and transects on the plot

showpts

if TRUE show points on the plot

pts

if not NULL use these points rather than generating new ones; this allows generation of a single set of points and evaluation of different sampling designs or intensity

...

parameters, if any, passed to plot

Value

lines

lines dataframe with label,x0,y0,x1,y1,width where x0,y0 is beginning and x1,y1 is end of the line

observations

a dataframe of the coordinates of the observed points

See Also

simCovariates,simPts

Examples

# Code stored in a function to speed up package checking run by typing do.simDSpat
# Some portions of this code will not pass the packge check on Linux and Mac and will issue
# an error that the polygons intersect even though when run as an example, the
# error is not encountered; so polygon checking is turned off
do.simDSpat=function()
{
# Now that it is in a function shouldn't need following line
spatstat.options(checkpolygons=FALSE)
study.area=owin(poly=list(x=c(0,40,40,100,100,0),y=c(0,0,40,40,100,100)))
covariates = simCovariates(hab.range=30, probs=c(1/3,2/3))
simdata=simDSpat(study.area,covariates,int.formula=~factor(habitat),
                   int.par=c(0,1,2),angle=45,nlines=10,width=3,det.par=.1)
sim.dspat=dspat(int.formula=~factor(habitat),study.area=study.area,
               obs=simdata$observations,lines=simdata$lines,
               covariates=covariates,epsvu=c(1,.05))
summary(sim.dspat)
AIC(sim.dspat)
coef(sim.dspat)
mu.B <- integrate.intensity(sim.dspat,dimyx=100,se=TRUE)
cat('Abundance =       ', round(mu.B$abundance,0), "\n")
cat('Standard Error =  ', round(mu.B$precision$se,0), "\n",
    '95 Percent Conf. Int. =   (', round(mu.B$precision$lcl.95,0), ',',
           round(mu.B$precision$ucl.95,0), ')', '\n')

mu.B <- integrate.intensity(sim.dspat,dimyx=100,se=TRUE,od=TRUE,reps=50)
cat('Abundance =       ', round(mu.B$abundance,0), "\n")
cat('Standard Error (corrected) =  ', round(mu.B$precision.od$se,0), "\n",
    '95 Percent Conf. Int.(corrected)  =   (', round(mu.B$precision.od$lcl.95,0), ',',
           round(mu.B$precision.od$ucl.95,0), ')', '\n')
plot(mu.B$lambda, main='Estimated Intensity')
plot(sim.dspat$lines.psp,lty=2,add=TRUE)
plot(owin(poly=sim.dspat$transect),add=TRUE)
plot(sim.dspat$model$Q$data,add=TRUE)
# Now sample with same point process realization with a different sampling angle
dev.new()
simdata=simDSpat(study.area,covariates,int.formula=~factor(habitat),
                   int.par=c(0,1,2),angle=90,nlines=10,width=3,pts=simdata$pts)
sim.dspat=dspat(int.formula=~factor(habitat),study.area=study.area,
               obs=simdata$observations,lines=simdata$lines,
               covariates=covariates,epsvu=c(1,.05))
mu.B <- integrate.intensity(sim.dspat,dimyx=100,se=TRUE)
cat('Abundance =       ', round(mu.B$abundance,0), "\n")
cat('Standard Error =  ', round(mu.B$precision$se,0), "\n",
    '95 Percent Conf. Int. =   (', round(mu.B$precision$lcl.95,0), ',',
           round(mu.B$precision$ucl.95,0), ')', '\n')
mu.B <- integrate.intensity(sim.dspat,dimyx=100,se=TRUE,od=TRUE,reps=50)
cat('Abundance  =       ', round(mu.B$abundance,0), "\n")
cat('Standard Error (corrected)=  ', round(mu.B$precision.od$se,0), "\n",
    '95 Percent Conf. Int. (corrected)=   (', round(mu.B$precision.od$lcl.95,0), ',',
           round(mu.B$precision.od$ucl.95,0), ')', '\n')
plot(mu.B$lambda, main='Estimated Intensity')
plot(sim.dspat$lines.psp,lty=2,add=TRUE)
plot(owin(poly=sim.dspat$transect),add=TRUE)
spatstat.options(checkpolygons=TRUE)
plot(sim.dspat$model$Q$data,add=TRUE)
# Sample with detection as a function of habitat
dev.new()
study.area=owin(poly=list(x=c(0,40,40,100,100,0),y=c(0,0,40,40,100,100)))
simdata=simDSpat(study.area,covariates,int.formula=~factor(habitat),
                   int.par=c(0,1,2),angle=45,nlines=10,width=3,
                   det.par=c(.1,.5,-.2),det.formula=~factor(habitat))
sim.dspat=dspat(int.formula=~factor(habitat),det.formula=~factor(habitat),
                study.area=study.area, obs=simdata$observations,lines=simdata$lines,
                covariates=covariates,epsvu=c(1,.05))
summary(sim.dspat)
AIC(sim.dspat)
coef(sim.dspat)
mu.B <- integrate.intensity(sim.dspat,dimyx=100,se=TRUE)
cat('Abundance =       ', round(mu.B$abundance,0), "\n")
cat('Standard Error =  ', round(mu.B$precision$se,0), "\n",
    '95 Percent Conf. Int. =   (', round(mu.B$precision$lcl.95,0), ',',
           round(mu.B$precision$ucl.95,0), ')', '\n')
plot(mu.B$lambda, main='Estimated Intensity')
plot(sim.dspat$lines.psp,lty=2,add=TRUE)
plot(owin(poly=sim.dspat$transect),add=TRUE)
plot(sim.dspat$model$Q$data,add=TRUE)
############################################################
# Generate example like Figure used in paper for simulations
# Note: it required a patch to plot.im from spatstat to
# fix the ribbon bar on the side.
#
#
#             if(is.null(list(...)$zlim))
#            {
#               ribbonvalues <- seq(vrange[1], vrange[2], length = ribn)
#               ribbonrange <- vrange
#               ribbonticks <- clamp(pretty(ribbonvalues), vrange)
#            }
#            else
#            {
#               zlim=list(...)$zlim
#               ribbonvalues <- seq(zlim[1], zlim[2], length = ribn)
#               ribbonrange <- zlim
#               ribbonticks <- clamp(pretty(ribbonvalues), zlim)
#            }
#
#############################################################
study.area=owin(poly=list(x=c(0,100,100,0),y=c(0,0,100,100)))
covariates = simCovariates(hab.range=30, probs=c(1/3,2/3))
postscript("Figure1.eps",horizontal=FALSE)
par(mfrow=c(2,1),mar=c(3, 1, 3, 1) + 0.1)
k=10
width=0.04*100/k
En=75
p=0.25
EN=En/(.04*p)
simdata=simDSpat(study.area,covariates,int.formula=~factor(habitat)+river,EN=EN,
                   int.par=c(0,1,2,-1),angle=90,nlines=k,width=width,
                   det.par=log(width/5),showplot=TRUE,col=gray(1-c(1:100)/120),
                   breaks=(0:100)*2.5/100,
                   zlim=c(0,2.5))
                   
lines(c(50,50),c(0,100),lty=2)
sim.dspat=dspat(int.formula=~factor(habitat)+river,study.area=study.area,
               obs=simdata$observations,lines=simdata$lines,
               covariates=covariates,epsvu=c(1,width/100))
summary(sim.dspat)
AIC(sim.dspat)
coef(sim.dspat)
mu.B <- integrate.intensity(sim.dspat,dimyx=100)
plot(mu.B$lambda, col=gray(1-c(1:100)/120), main='Estimated Intensity',
  breaks=(0:100)*2.5/100,zlim=c(0,2.5))
plot(sim.dspat$lines.psp,lty=2,add=TRUE)
plot(owin(poly=sim.dspat$transect),add=TRUE)
plot(sim.dspat$model$Q$data,add=TRUE)
dev.off()
spatstat.options(checkpolygons=TRUE)
}