Functions for estimating and assigning the root-mean-square User Equivalent Range Error (UERE) of a GPS device from calibration data.

uere(data)

uere(data) <- value

uere.fit(data,precision=1/2)

# S3 method for UERE
summary(object,level=0.95,...)

Arguments

data

telemetry object or list of telemetry objects, preferably with DOP columns.

value

RMS UERE value(s) to assign to telemetry data (see details).

precision

Fraction of maximum possible digits of precision to target in categorical error fitting. precision=1/2 results in about 7 decimal digits of precision.

object

UERE object to summarize or list of UERE objects to compare.

level

Confidence level for UERE estimate confidence intervals.

...

Further arguments are ignored.

Details

Often times GPS animal tracking devices return HDOP values but do not specifiy the device's RMS UERE necessary to transform the HDOP values into absolute errors. uere.fit() allows users to estimate the RMS UERE from calibration data, where the device was left fixed over a period of time. The calibration RMS UERE can then be applied to tracking data with the uere()<- assignment method. Otherwise, when error=TRUE in ctmm, ctmm.fit will estimate the RMS UERE simultaneously with the movement model, which is less reliable than using calibration data.

summary() applied to single UERE object will return RMS UERE parameter estimates and confidence intervals in meters, while summary() applied to a list of UERE objects will return a model-selection table, with AICc and reduced Z squared (goodness of fit) values.

Value

The RMS UERE estimate.

References

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 .

Author

C. H. Fleming

Note

The GPS device should be fixed during calibraiton.

See also

Examples

# Load package and data library(ctmm) data(turtle) # the first two datasets are calibration data names(turtle)
#> [1] "60s" "90s" "F231" "F403"
# estimate RMS UERE from calibration data UERE <- uere.fit(turtle[1:2]) # inspect UERE estimate summary(UERE)
#> , , horizontal #> #> low est high #> 2D 22.786528 29.598802 36.397467 #> 3D 6.477391 6.946024 7.414237 #>
# assign RMS UERE to entire dataset uere(turtle) <- UERE # calculate residuals of calibration data RES <- lapply(turtle[1:2],residuals) # scatter plot of residuals with 50%, 95%, and 99.9% coverage areas plot(RES,col.DF=NA,level.UD=c(0.50,0.95,0.999))
# check calibration data for autocorrelation using fast=FALSE because samples are small ACFS <- lapply(RES,function(R){correlogram(R,fast=FALSE,dt=10 %#% 'min')})
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# pooling ACFs ACF <- mean(ACFS) plot(ACF)