Methods to facilitate outlier detection.
Produces a data.frame of speed and distance estimates to analyze, as well as a plot highlighting potential speed and distance outliers in telemetry data.
outlie(data,UERE=10,plot=TRUE,by='d',...)
## S3 method for class 'outlie'
plot(x,level=0.95,units=TRUE,axes=c('d','v'),...)data |
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UERE |
Device-dependent telemetry error in meters (RMS UERE). Only necessary for uncalibrated data. |
plot |
Output a plot highlighting high speeds (blue) and distant locations (red). |
by |
Color and size side-effect plot points by |
... |
Arguments passed to |
x |
|
level |
Confidence level for error bars. |
units |
Convert axes to natural units. |
axes |
x-y axes to plot. Can be any of |
If plot=TRUE in 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.
The output outlie object contains the above noted speed and distance estimates in a data.frame, with rows corresponding to those of the input telemetry object.
Returns a data.frame of distances and speeds. Can also produce a plot as a side effect.
C. H. Fleming.
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.
# Load package and data library(ctmm) data(turtle) # look for outliers in a turtle OUT <- outlie(turtle[[3]]) # look at the distribution of estimates plot(OUT)
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