singleton_filter: Filters out singleton detections
In bsmity13/ADePTR: Acoustic Detection Processing and Visualization Tool in R

Description Usage Arguments Details References See Also Examples

Filters out detections if only 1 detection occurred for an ID in a user-defined time horizon.

1	singleton_filter(proc_det, time_horizon = "1 day")

`proc_det`	A `data.frame` of class `dets` as returned by the function `proc_dets()`.
`time_horizon`	The time during which a single detection will be filtered. Can be any value that can be accepted by the `by` argument of `seq.POSIXt()`.

False-positive detections in acoustic data can occur for reasons related to signal collisions. Type A false detections are detections of IDs that are not present in the study system, and here we assume the user has already removed these detections prior to using this package. Type B false detections occur when a signal is erroneously decoded as the signal of a different tag that is in the study system – these detections are much harder to distinguish from true detections and are the focus of this function.

This function is based on the assumption that it is possible for signal collisions to result in a single false detection over a sufficiently long time horizon, but that the probability of two false detections of the same ID over that same time horizon is very low. Thus, the choice of time horizon is very critical here. The period of time should be long enough that your animal would plausibly use a receiver many times, but not so long that the probability of two false detections of the same ID becomes unacceptably high.

Simpfendorfer, C. A., C. Huveneers, A. Steckenreuter, K. Tattersall, X. Hoenner, R. Harcourt, and M.R. Heupel. (2015). Ghosts in the data: false detections in VEMCO pulse position modulation acoustic telemetry monitoring equipment. Animal Biotelemetry 3:55.

See Simpfendorfer et al. (2015) for details about false detections in acoustic telemetry.

#Load the example data set
data(acoustic)

#Process detections
proc.det <- proc_dets(det = acoustic$detections, sta = acoustic$stations)

#Apply the singleton filter with default time_horizon
det.filt <- singleton_filter(proc.det)

#Apply the singleton filter with 1 month time horizon
##Note: The user should see ?lubridate::add_with_rollback
 #to understand the behavior when adding months.
det.filt2 <- singleton_filter(proc.det, time_horizon = "1 month")