akde: Calculate an autocorrelated kernel density estimate

Description

This function calculates autocorrelated kernel density estimates of different confidence levels from telemetry data and a continuous-time movement model.

Usage

akde(data,CTMM,error=0.001,res=200,grid=NULL,...)

Arguments

data

2D timeseries telemetry data represented as a telemetry object.

CTMM

A ctmm movement model from the output of ctmm.fit.

error

Target error for kernel truncation.

res

Number of grid points along each axis.

grid

Optional grid specification with columns labeled x and y. Not yet supported.

...

Arguments passed to all instances of akde.bandwidth.

Value

Returns a UD object: a list with the sampled grid line locations x and y, the area of each grid cell dA, the probability density and cumulative distribution functions evaluated on the sampled grid locations PDF & CDF, the optimal bandwidth matrix H, and the effective sample size of the data in DOF.H.

References

C. H. Fleming and W. F. Fagan and T. Mueller and K. A. Olson and P. Leimgruber and J. M. Calabrese (2015). Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator. http://www.esajournals.org/doi/abs/10.1890/14-2010.1{Ecology, 96(5), 1182-1188}.

Examples

Run this code

# Load package and data
library(ctmm)
data(buffalo)
cilla <- buffalo[[1]]

# Fit a continuous-velocity model with tau ~ c(10 days, 1 hour)
# see help(variogram.fit)
GUESS <- ctmm(tau=c(10*24*60^2,60^2))
FIT <- ctmm.fit(cilla,GUESS)

# Compute akde object
UD <- akde(cilla,FIT)

# Plot data with AKDE contours
plot(cilla,UD=UD)