outlie  R Documentation 
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,plot=TRUE,by='d',...) ## S3 method for class 'outlie' plot(x,level=0.95,units=TRUE,axes=c('d','v'),xlim=NULL,ylim=NULL,...)
data 

plot 
Output a plot highlighting high speeds (blue) and distant locations (red). 
by 
Color and size sideeffect plot points by 
... 
Arguments passed to 
x 

level 
Confidence level for error bars. 
units 
Convert axes to natural units. 
axes 
xy axes to plot. Can be any of 
xlim 
xaxis plotting range in SI units. 
ylim 
yaxis plotting range in SI units. 
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 straightline 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 an outlie
object, which is a data.frame of distance and speed information. Can also produce a plot as a side effect.
The speed estimates here are tailored for outlier detection and have poor statistical efficiency. The predict
and speed
methods are appropriate for estimating speed (after outliers have been removed and a movement model has been selected).
In 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.
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.
as.telemetry
.
# 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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