
The ids are the unique id for each individual that was observed to first solve a task
or demonstrate a new behaviour.


Network based diffusion analysis (NBDA) allows inference on
the asocial and social transmission of information.  This may involve
the social transmission of a particular behaviour such as tool use, for example.
For the NBDA, the key parameters estimated are the social effect and baseline rate
parameters.  The baseline rate parameter gives the rate at which the behaviour
is first performed (or acquired) asocially amongst the individuals in a given population.
The social effect parameter quantifies the effect of the social associations amongst
the individuals on the rate at which each individual first performs or displays
the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.
This is done by incorporating social networks derived from
spatial point patterns (of the home bases of the individuals under study).  In addition,
a spatial covariate such as vegetation cover, or slope may be included in the modelling
process.

Glenna Nightingale

spatialnbda

Performs spatial NBDA in a Bayesian context

Ids function


This dataset contains the unique id for each individual in each group.
This is part of the data example to demonstrate modelling NBDA with random effects.


Ids: This dataset contains the unique id for each individual in each group. This is part of the data example to demonstrate modelling NBDA with random effects.

Description

Usage

Arguments