ctmm (version 0.2.9)

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,alpha=0.05,fast=NULL,dt=NULL,error=0.001,res=200,grid=NULL)

Arguments

data

2D timeseries telemetry data represented as a telemetry object or data.frame or list with components t, x, y all numeric values.

CTMM

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

alpha

Significance level for when propagating uncertainties in the ctmm parameter estimates.

fast, dt

Arguments passed to all instances of akde.bandwidth.

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.

Value

• Returns an akde object: a list with component kernel-density objects low, ML, high, and the corresponding significance level alpha for the optimal bandwidth area. Each kernel-density object contains the sampled grid line locations x and y, the probability density function evaluated on the sampled grid locations PDF, the optimal bandwidth matrix H, and the area of each grid cell dA.

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}.

See Also

akde.bandwidth, raster.akde

Examples

# 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)
m2 <- ctmm(tau=c(10*24*60^2,60^2))
M2 <- ctmm.fit(cilla,m2)

# Compute akde object
KD2 <- akde(cilla,M2)

# Plot data with AKDE contours
plot(cilla,AKDE=KD2)