computeContourValues: Find thresholds for contour intervals

Description Usage Arguments Details Value Author(s) Examples

View source: R/mkdeFunctions.R

Description

Find the cell or voxel probabilities that correspond to user-specified probability contours

Usage

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computeContourValues(mkde.obj, prob)	

Arguments

mkde.obj

An MKDE object with density initialized

prob

Probabilities (i.e. proportions) for desired contours of the MKDE

Details

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.

Value

A data frame with the probabilities given in the prob argument and corresponding thresholds in the MKDE

Author(s)

Jeff A. Tracey, PhD
USGS Western Ecological Research Center, San Diego Field Station
[email protected]v
James Sheppard, PhD
San Diego Zoo Institute for Conservation Research
[email protected]

Examples

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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)

mkde documentation built on May 2, 2019, 6:46 a.m.