Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.
|Maintainer||Laurent Dubroca <[email protected]>|
|Package repository||View on GitHub|
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