Nothing
simDSpat=function(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,...)
#################################################################################
# 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 covariate
# structure. In sequence it calls create.lines, lines_to_strips, simPts,
# and sample.points.
#
# 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 for plot
#
#
# Value: a list with elements
#
# lines - lines dataframe with label,x0,y0,x1,y1,width where x0,y0 is beginning
# and x1,y1 is end of the line
# observations - an observation dataframe
#
# Jeff Laake
# 23 April 2008
#################################################################################
{
# Create sample of lines
xlines=create.lines(study.area=study.area,nlines=nlines,width=width,angle=angle)
# Create strips and line psp
ls=lines_to_strips(xlines,study.area)
# Simulate point process across study area unless pts are supplied
if(is.null(pts))
obs=simPts(covariates=covariates,int.formula=int.formula,
int.par=int.par,EN=EN,showplot=showplot, showpts=showpts, ...)
else
obs=pts
# Sample points via lines and detection
observations=sample.points(ls$transects,xlines,obs,detfct=hndetfct,
det.par=det.par,det.formula=det.formula,covariates=covariates)
# If showplot=TRUE, show points and lines
if(showplot & showlines)
{
plot(ls$lines,lty=2,add=TRUE)
plot(owin(poly=ls$transects),add=TRUE)
}
return(list(lines=xlines,observations=observations,pts=obs))
}
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