ctmm (version 0.3.1)

plot.telemetry: Plotting methods for telemetry objects.

Description

Produces simple plots of telemetry objects, possibly overlayed with a Gaussian ctmm movement model or an akde autocorrelated kernel density estimate.

Usage

## S3 method for class 'telemetry':
plot(x, CTMM=NULL, UD=NULL, level.UD=0.95, level=0.95, DF="CDF", col="red",
col.level="black", col.DF="blue", col.grid="grey", fraction=1, add=FALSE, xlim=NULL,
ylim=NULL, ...)

## S3 method for class 'telemetry':
zoom(x,fraction=1,...)

Arguments

x

The 2D timeseries data represented as a telemetry object or list of such objects. All other arguments are optional.

CTMM

Optional Gaussian ctmm movement model from the output of ctmm.fit or list of such objects.

UD

Optional UD object such as from the output of akde or list of such objects.

level.UD

Confidence level of Gaussian ctmm model or UD estimate contours to be displayed. I.e., level.UD=0.50 can yield the 50% core home range within the rendered contours.

level

Confidence levels placed on the contour estimates themselves. I.e., the above 50% core home-range area can be estimated with 95% confidence via level=0.95.

DF

Plot the maximum likelihood probability density function "PDF" or cumulative distribution function "CDF".

col

Color option for telemetry data. Can be an array.

col.level

Color option for home-range contours. Can be an array.

col.DF

Color option for the density function. Can be an array.

col.grid

Color option for the maximum likelihood akde bandwidth grid.

fraction

Fraction of the data, Gaussian ctmm, or UD range to plot, whichever is larger.

add

Setting to TRUE will disable the unit conversions and base layer plot, so that plot.telemetry can be overlayed atop other outputs more easily.

xlim

The x limits c(x1, x2) of the plot.

ylim

The y limits c(y1, y2) of the plot.

...

Additional options passed to plot.

Value

• Returns a plot of $x$ vs. $y$, and, if specified, Gaussian ctmm distribution or UD. akde UD plots also come with a standard resolution grid. zoom includes a zoom slider to manipulate fraction.

Details

Confidence intervals placed on the ctmm Gaussian home-range contour estimates only represent uncertainty in the area's magnitude and not uncertainty in the mean location, eccentricity, or orientation angle. For akde UD estimates, the provided contours also only represent uncertainty in the magnitude of the area. With akde estimates, it is also important to note the scale of the bandwidth and, by default, grid cells are plotted with akde contours such that their length and width matches that of a bandwidth kernels' standard deviation in each direction. Therefore, this grid provides a visual approximation of the kernel-density estimate's ``resolution''.

References

C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, and W. F. Fagan. (2014). From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. http://www.jstor.org/discover/10.1086/675504{The American Naturalist, 183(5), E154-E167.} C. H. Fleming and J. M. Calabrese and T. Mueller and K. A. Olson and P. Leimgruber and W. F. Fagan (2014). Non-Markovian maximum likelihood estimation of autocorrelated movement processes http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12176/abstract{Methods in Ecology and Evolution, 5(5) 462-472}. 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, doi:10.1890/14-2010.1}.

See Also

akde, ctmm.fit, plot, SpatialPoints.telemetry.

Examples

# Load package and data
library(ctmm)
data(buffalo)

# Plot the data
plot(buffalo,col=rainbow(length(buffalo)))