variogram(data,dt=NULL,fast=TRUE,CI="Markov",axes=c("x","y"))telemetry data object of the 2D timeseries data. FALSE or the FFT algorithm if TRUE. The slow algorithm outputs a progress bar.lag, the semi-variance estimate at that lag, SVF, and the approximate number of degrees of freedom associated with that semi-variance, DOF, with which its confidence intervals can be estimated.dt is specified, the median sampling interval is used. This is typically a good assumption for most data, even when there are gaps. A dt coarser than the sampling interval may bias the variogram (particuarly if fast=TRUE) and so this should be reserved for poor data quality. For irregularly sampled data, it may be useful to provide an array of time-lag bin widths to progressively coarsen the variogram. I.e., if you made the very bad choice of changing your sampling interval on the fly from dt1 to dt2, where dt1 \(<\) dt2, the an appropriate choice would be dt=c(dt1,dt2). On the other hand, if your sampling is itself a noisy process, then you might want to introduce larger and larger dt components as the visual appearance of the variogram breaks down with increasing lags.
Alternatively, you might try the fast=FALSE option or aggregating multiple individuals with mean.variogram. In standard variogram regression treatments, all lags are considered as independent (CI="IID") for the purposes of confidence-interval estimation, even if they overlap in time. However, in high resolution datasets this will produce vastly underestimated confidence intervals. Therefore, the default CI="Markov" behavior is to consider only the maximum number of non-overlapping lags in calculating confidence intervals.vignette("variogram"), mean.variogram, plot.variogram, variogram.fit.#Load package and data
library(ctmm)
data(buffalo)
#Extract movement data for a single animal
cilla <- buffalo[[1]]
#Calculate variogram
SVF <- variogram(cilla)
#Plot the variogram
plot(SVF)
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