data.frame of speed and distance estimates to analyze, as well as a plot highlighting potential speed and distance outliers in
outlie(data,plot=TRUE,by='d',...) # S3 method for outlie plot(x,level=0.95,units=TRUE,axes=c('d','v'),...)
Output a plot highlighting high speeds (blue) and distant locations (red).
Color and size side-effect plot points by
Arguments passed to
Confidence level for error bars.
Convert axes to natural units.
\(x\)-\(y\) axes to plot. Can be any of
outlie(), intervals of high speed are highlighted with blue segments, while distant locations are highlighted with red points.
When plotting the
outlie object itself, `core deviation' denotes distances from the median longitude & latitude, while `minimum speed' denotes the minimum speed required to explain the location estimate's displacement as straight-line motion. Both estimates account for telemetry error and condition on as few data points as possible. The speed estimates furthermore account for timestamp truncation and assign each timestep's speed to the most likely offending time, based on its other adjacent speed estimate.
outlie object contains the above noted speed and distance estimates in a
data.frame, with rows corresponding to those of the input
data.frame of distances and speeds. Can also produce a plot as a side effect.
C. H. Fleming et al, ``A comprehensive framework for handling location error in animal tracking data'', bioRxiv 2020.06.12.130195 (2020) doi: 10.1101/2020.06.12.130195 .
C. H. Fleming.
The speed estimates here are tailored for outlier detection and have poor statistical efficiency. The
speed methods are appropriate for estimating speed (after outliers have been removed and a movement model has been selected).
ctmm v0.6.1 the
UERE argument was deprecated. For uncalibrated data, the initial esitmates used by
outlie are now generated on import and stated by
summary(uere(data)). These values not be reasonable for arbitrary datasets.