Benhamou's IAB Index
IAB computes the IAB index following the methods described in the paper by
Benhamou et al. (2014). It facilitates global analysis, with the significance testing procedure
described in the paper, but also a local level output, to explore the IAB statistic through time.
an object of the class
time threshold for determining simultaneous fixes – see function:
critical distance where the IAB function will show maximium slope – see Benhamou et al. (2014) for more advice on selecting this parameter.
logical value indicating whether a dataframe (
IAB can be used to test for direct interaction in wildlife telemetry data and affords
a novel significance testing procedure that takes into account the serially correlated structure of
telemetry data. Specifically, it computes an index analogous to the Bhattacharyya coefficient between the
potential influence domains of two animals. Like the other indices, IAB is dependent on the selection of an
appropriate value for
dc (which is termed Delta in the article). The
dc parameter here is
not a threshold distance, but rather the distance at which the function shows maximum slope (see
Benhamou et al. 2014).
The significance testing procuedure uses a wrapped shifting method in order to maintain the serially correlated structure of the data. At each shift, a sample value of IAB (termed MAB) is computed in order to generate a distribution of values to test against (for more information see Benhamou et al. 2014).
local=FALSE (the default) IAB returns the numeric value of the IAB index and the associated p-value.
local=TRUE IAB returns a dataframe (containing the date/times of all simultaneous fixes,
along with the distance between fixes at each time, and the IAB index value for each simultaneous fix.
Benhamou, S., Valeix, M., Chamaille-Jammes, S., Macdonald, D., Loveridge, A.J. (2014) Movement-based analysis of interactions in African lions. Animal Behaviour, 90: 171-180.
GetSimultaneous, DI, Prox
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