Plotting methods for variogram objects.

Produces simple plots of varigram objects (semi-variance vs. time lag) and model semi-variance functions, with approximate confidence intervals around the semi-variance estimates.

# S3 method for variogram
plot(x,CTMM=NULL,level=0.95,units=TRUE,fraction=0.5,col="black",col.CTMM="red",xlim=NULL,
     ylim=NULL,ext=NULL,...)

# S4 method for variogram
zoom(x,fraction=0.5,...)

Arguments

x: A variogram object calculated using variogram.
CTMM: A ctmm movement model object in the same format as the output of ctmm.fit or variogram.fit.
level: Confidence level of confidence bands (95% default CIs). Can be an array.
units: Convert axes to natural units.
fraction: The proportion of the variogram object, variogram, that will be plotted. By convention, half is shown. The tail end is generally garbage.

col: Color for the empirical variogram. Can be an array.
col.CTMM: Color for the model. Can be an array.
xlim: Range of lags to plot (in SI units).
ylim: Range of semi-variance to plot (in SI units).
ext: Plot extent alternative to xlim and ylim (see extent).
...: Additional plot function parameters.

Value

Returns a plot of semi-variance vs. time lag, with the empirical variogram in black and the ctmm semi-variance function in red if specified. zoom includes a log-scale zoom slider to manipulate fraction.

References

C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, W. F. Fagan. From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. The American Naturalist, 183:5, E154-E167 (2014) doi:10.1086/675504 .

Author

J. M. Calabrese and C. H. Fleming

Note

The errors of the empirical variogram are correlated. Smooth trends are not necessarily significant.

Examples

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

# Extract movement data for a single animal
Cilla <- buffalo$Cilla

# Calculate variogram
SVF <- variogram(Cilla)

# Plot the variogram
plot(SVF)