telemetry
objects, possibly overlayed with a Gaussian ctmm
movement model or a UD
utilization distribution.plot(x,y,...)# S3 method for 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, cex=1, lwd=1, ...)
# S4 method for list
zoom(x,...)
# S4 method for telemetry
zoom(x,fraction=1,...)
# S4 method for UD
zoom(x,fraction=1,...)
telemetry
or UD
object.ctmm
movement model from the output of ctmm.fit
or list of such objects.UD
object such as from the output of akde
or list of such objects.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=0.95
. "PDF"
or cumulative distribution function "CDF"
.akde
bandwidth grid.ctmm
, or UD
range to plot, whichever is larger.TRUE
will disable the unit conversions and base layer plot, so that plot.telemetry
can be overlayed atop other outputs more easily.x
limits c(x1, x2)
of the plot.y
limits c(y1, y2)
of the plot.UD
contours.plot
.ctmm
distribution or UD
.
akde
UD
plots also come with a standard resolution grid.
zoom
includes a zoom slider to manipulate fraction
.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''.akde
, ctmm.fit
, plot
, SpatialPoints.telemetry
.# Load package and data
library(ctmm)
data(buffalo)
# Plot the data
plot(buffalo,col=rainbow(length(buffalo)))
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