Find thresholds for contour intervals
Find the cell or voxel probabilities that correspond to user-specified probability contours
computeContourValues(mkde.obj, prob)
mkde.obj |
An MKDE object with density initialized |
prob |
Probabilities (i.e. proportions) for desired contours of the MKDE |
This function computes threshold cell or voxel probability values
corresponding to contours for specified proportions of the utilization
distribution. Note that the arugment prob specifies the
cumulative probability of the cells or voxels within the contour
corresponding to the cell or voxel threshold probability. The cell or
voxel threshold probabilities may be orders of magnitude smaller than
the cumulative probabilities provided in the prob argument.
A data frame with the probabilities given in the prob argument and corresponding thresholds in the MKDE
Jeff A. Tracey, PhD
USGS Western Ecological Research Center, San Diego Field Station
jatracey@usgs.gov
James Sheppard, PhD
San Diego Zoo Institute for Conservation Research
jsheppard@sandiegozoo.org
library(raster) data(condor) condor <- condor[1:10,] # simply to make example run more quickly mv.dat <- initializeMovementData(condor$time, condor$x, condor$y, z.obs=condor$z, sig2obs=25.0, sig2obs.z=81.0, t.max=65.0) data(condordem120) cell.sz <- mean(res(condordem120)) ext <- extent(condordem120) nx <- ncol(condordem120) ny <- nrow(condordem120) mkde.obj <- initializeMKDE3D(ext@xmin, cell.sz, nx, ext@ymin, cell.sz, ny, min(values(condordem120), na.rm=TRUE), cell.sz, 25) # note: we use a raster coarse integration time step so the # example runs faster dens.res <- initializeDensity(mkde.obj, mv.dat, integration.step=10.0) mkde.obj <- dens.res$mkde.obj mv.dat <- dens.res$move.dat my.quantiles <- c(0.95, 0.75, 0.50) res <- computeContourValues(mkde.obj, my.quantiles) print(res)
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