makeRR.ss: this function is a support function for SCRf.fn and creates a...
In jaroyle/SCRbayes: Bayesian analysis of spatial capture-recapture models

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

function creates a square statespace around a survey area containing x,y locations of potential activity centers for use in the function SCRf.fn.

1	make.ss(traps, buffer, grid.density)

`traps`	traps=matrix containing the X,Y locations of traps laid out in a grid
`buffer`	the distance of the buffer to put around the traps. Remember if your trap location is the centerpoint of a grid square, add half the size of the grid square to your desired buffer. (i.e. for a buffer of size 10km and grid of 5X5km squares, your buffer size should be 12.5km
`ss.density`	the density of your desired statespace on the same scale as the trapping grid. An ss.density of 2 in statespace for a grid of 5x5km will return a statespace with a point every 2km

function is designed for rectangular trapping arrays

returns a matrix with three column, column 1 and 2 are the x and y coordinates of the state space, this coordinates are the locations of the potential activity centers, column three is by default =1 and indicates whether the coordinate is in a location considered habitat or not.

Robin Russell

Royle, J. A., K. U. Karanth, A. M. Gopalaswamy, and N. S. Kumar. 2009. Bayesian inference in camera trapping studies for a class of spatial capture<e2><80><93>recapture models. Ecology 90:3233<e2><80><93>3244.

Thompson, C. M., J. A. Royle, J. D. Gardner. in press. A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs. Journal of Wildlife Management.

SCRf.fn, image.scale, e2dist

###create a trapping grid
traps<-data.frame(x=rep(seq(4000,5000, by=100),6), y=sort(rep(seq(4000,5000, by=200),11)))
##make a rectangular statespace with a center of activity every 100m, a buffer of 100m, and a habitat value of 1 for each center point
ss<-make.ss(traps, 100, 100)





make.ss<-function(traps, buffer, grid.density)
{
x<-seq(min(traps$x)-buffer, max(traps$x)+buffer, by=grid.density)
y<-seq(min(traps$y)-buffer, max(traps$y)+buffer, by=grid.density)
ss.y<-rep(y, length(x))
ss.x<-rep(x[1], length(y))

for (i in 2:length(x))
{
ss.x<-c(ss.x,rep(x[i],length(y)))
}

ss.animal<-data.frame(X_coord=ss.x, Y_coord=ss.y, V3=1)
list(ss.animal=ss.animal)
}