# M2B - Movement to behaviour

A package to build behavioural model on track data, using randomForest.

# Installation

In a R console:

`install.packages("devtools")`

`library(devtools)`

`install_github("ldbk/m2b")`

# Description

Animal behaviour, including social interactions, are fundamental to the field of
ecology. Whereas the direct observation of animal behaviour is often limited due
to logistical constraints, collection of movement data have been greatly
facilitated through the development of bio-logging. Animal movement data
obtained through tracking instrumentation may potentially constitute a relevant
proxy to infer animal behaviour. This is, however, based on the premise that a
range of movement patterns can be linked to specific behaviours.

Statistical learning constitutes a number of methods that can be used to
assess the link between given variables from a fully informed training
dataset and then predict the values on a non-informed variable. We chose the
random forest algorithm for its capacity to deal with imbalanced data
(particularly relevant for behavioural data), its high prediction accuracy
and its ease of implementation (@breiman2001b, @chen2004). 
The strength of random forest partly relies
in its ability to handle a very large number of variables. Hence, our
methodology is based on the derivation of multiple predictor variables from
the movement data over various temporal scales, in order to capture as much
information as possible on the changes and variations of movement.

In this package we developed a method to link the movement patterns of animals
with their behaviour, using the random forest algorithm. 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 dataset providing movement data together with
observation of behaviour.
