| Title: | Subsample Temporal Data to Synchronal Events and Compute the MCI |
|---|---|
| Description: | The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'. |
| Authors: | Martin Rimmler [aut, cre], Thomas Mueller [aut] |
| Maintainer: | Martin Rimmler <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1-0 |
| Built: | 2024-12-09 06:41:56 UTC |
| Source: | CRAN |
The function syncSubsample subsamples temporal data of different
entities so that the result only contains synchronal events. The
function mci calculates the Movement Coordination Index (MCI, see
reference on help page for function mci) e.g. of a data set
created with the function syncSubsample.
| Package: | SyncMove |
| Type: | Package |
| Version: | 0.1-0 |
| Date: | 2015-10-08 |
| License: | GLP-2 |
Martin Rimmler (maintainer, martin.rimmler[AT]gmail.com), Thomas Mueller
Mueller, T., et al. (2011) How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data, Global Ecology and Biogeography, 20, pages 683–694.
Data set of GPS tracked relocations of gazelles in the Mongolian steppe. For more information contact Thomas Mueller (e-mail address see below).
gazelleRelocationsgazelleRelocations
Data frame with columns id, sex, time, x and y.
Mueller, T. (2015), muellert[AT]gmail.com
Computes the Movement Coordination Index as described in Mueller, T., et al. (2011).
mci(x)mci(x)
x |
data frame of synchronized relocations of a set of individuals. The data frame must containing the following columns: syncID: numeric (integer), ID marking the synchronization event. For each synchronization event every individual must have a record. Therefore each ID must appear as often as every other ID (e.g. not c(1,1,1,2,2,3,3,3) but c(1,1,1,2,2,2,3,3,3)). 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 (Further columns are allowed, but will be ignored. Usually a column naming the individual is present in the data frame.) Such a data frame is returned by the function |
Returns a numeric vector of MCI values, one for each pair of subsequent synchronization events.
Martin Rimmler (maintainer, martin.rimmler[AT]gmail.com), Thomas Mueller
Mueller, T., et al. (2011) How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data, Global Ecology and Biogeography, 20, pages 683–694.
# load example data data(gazelleRelocations) # create input data frame syncRelocs <- syncSubsample(x = gazelleRelocations, startSearch = "2007-09-05 00:00:00", syncIntervalSecs = 3600*24*16, syncAccuracySecs = 3600*24) # calculate MCI mci(syncRelocs$data[[1]])# load example data data(gazelleRelocations) # create input data frame syncRelocs <- syncSubsample(x = gazelleRelocations, startSearch = "2007-09-05 00:00:00", syncIntervalSecs = 3600*24*16, syncAccuracySecs = 3600*24) # calculate MCI mci(syncRelocs$data[[1]])
Subsamples temporal records of different entities to a data set which only includes records that occur at predefined and equally spaced synchronization events. A subsample is created for each possible combination of entities (unless restricted by argument minEntities, maxEntities or mustEntities).
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$individual.local.identifier)), mustEntities = NULL, completeSyncsOnly = TRUE, fast = TRUE )syncSubsample( x, startSearch = min(as.character(x$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 )
x |
a data frame with the following columns (further columns are allowed, but will be ignored): individual.local.identifier: character, entity ID 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 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 |
startSearch |
character, time of format "YYYY-MM-DD HH:MM:SS" as the start time for the creation of synchronization events. Default is the minimum timestamp in the data. |
endSearch |
analogue to startSearch. |
syncIntervalSecs |
numeric, interval between synchronization events in seconds. (e.g., 60*60*24*3 defines a three day interval) |
syncAccuracySecs |
numeric, accuracy for synchronization events in seconds. (e.g., 60*60*2 defines a two hour accuracy) |
minEntities |
numeric, minimum number of entities to be included in the synchronization events. |
maxEntities |
analogue to minEntities |
mustEntities |
character, vector of IDs of entities which have to be included in the synchronization events. |
completeSyncsOnly |
boolean, if TRUE (default) only events will appear in the output where each entity of a given combination of entities has a record. If FALSE also events with no records for some entities will appear in the output. |
fast |
boolean, if TRUE (default) synchronized subsamples are created only for those combinations of entities that seem to be the most prominent in the input data. If FALSE synchronized subsamples are created for all combinations of entities in the input data. See details. |
The synchronization events are created with a start time as the first synchronization event (argument startSearch) and an interval between following synchronization events. Each synchronization event has an accuracy. All records of a given combination of entities which fall into synchronization events + - accuracy are selected for the subsample. If there is more than one record for an entity in a synchronization event + - accuracy the record that is closest to the synchronization event is selected. The arguments startSearch, syncIntervalSecs and syncAccuracySecs must be chosen with respect to the input data in order to get good synchronization results.
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.
List, returns a list with 3 elements named overview, data, and entities.
overview |
An overview of the synchronized subsamples for all possible combinations of entities. Each row refers to the respective element in the data and entities lists (see below). E.g., for the data described in row 1 see <output>$data[[1]] and <output>$entities[[1]]. The overview table contains the following columns: numberOfEntities: the number of entities used for creating the subsample numberOfSyncs: number of synchronized events in the subsample numberOfSubsequentSyncs: number subsequent synchronized events (see details) firstEvent: time of first synchronization event lastEvent: analogue to lastEvent syncIntervalSecs: interval used for creating the synchronization events syncAccuracySecs: accuracy of the synchronization events |
data |
A list containing the subsampled data described in the overview. The subsamples have the same columns as the input data set plus the additional columns syncTime and syncID for the time and ID of the synchronization events respectively. |
entities |
A list containing the entity combinations used for creating the synchronized subsamples. |
Martin Rimmler (maintainer, martin.rimmler[AT]gmail.com), Thomas Mueller
# 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$entities[[1]]# load example data data(gazelleRelocations) # subsample sychronal events syncRelocs <- syncSubsample(x = gazelleRelocations, startSearch = "2007-09-05 00:00:00", syncIntervalSecs = 3600*24*16, syncAccuracySecs = 3600*24) # show results overview syncRelocs$overview # show first subsample syncRelocs$data[[1]] # show entities of first subsample syncRelocs$entities[[1]]