syncSubsample( x, startSearch = min(as.character(x$study.local.timestamp)), endSearch = max(as.character(x$study.local.timestamp)), syncIntervalSecs = 3600, syncAccuracySecs = 60, minEntities = 2, maxEntities = length(unique(x$individual.local.identifier)), mustEntities = NULL, completeSyncsOnly = TRUE, fast = TRUE )study.local.timestamp: character, time of format "YYYY-MM-DD HH:MM:SS" Such a data frame can be created by importing data from www.movebank.org.
If the output is to be processed with function mci the
data frame must also contain the following columns:
utm.easting: numeric, planar x coordinate. Although the name indicates UTM coordinates other planar coordinate systems are also allowed. utm.northing: analogue to utm.easting
Running the function with fast = FALSE one can find the combination of entities with the maximum number of synchronization events. However, an input data set with more then 8 to 10 entities should be processed with fast = TRUE. Otherwise the calculations can take a long time. (For input data with 10 entities there are more than 1000 possible combinations of entities.)
The synchronization events are numbered from 1 to n. These numbers
are referred to as sync IDs. If no records are present at a given
synchronization event the ID for this event will not appear in the
output subsample. Thus the sync IDs in the subsample show if
subsequent pairs of synchronized events exist (e.g., sync ID
1 and 2, sync ID 2 and 3, ...). Such pairs can be used to calculate
the Movement Coordination Index, see function mci.
# load example data
data(gazelleRelocations)
# subsample sychronal events
syncRelocs <- syncSubsample(x = gazelleRelocations,
startSearch = "2007-09-05 00:00:00",
syncIntervalSecs = 3600*24*16,
syncAccuracySecs = 3600*24)
# show results overview
syncRelocs$overview
# show first subsample
syncRelocs$data[[1]]
# show entities of first subsample
syncRelocs$entities[[1]]
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