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.
|Date of publication||2014-09-19 00:49:05|
|Maintainer||Glenna Nightingale <firstname.lastname@example.org>|
FormatData: Formats the data for NBDA
idarray: Individual id's for RJMCMC Example 1.
Ids: This dataset contains the unique id for each individual in...
mcmc: Performs spatial NBDA in a Bayesian context
mcmcre: Performs NBDA with individual level random effects
nullmcmc: The spatial NBDA null model is considered for this analysis....
rjmcmc: Model discriminiation in a Bayesian context for spatial NBDA.
smcmc: Performs spatial NBDA in a Bayesian context with an...
socialx: x coordinates for RJMCMC Example 1.
socialy: y coordinates for RJMCMC Example 1
spatialnbda-package: Performs spatial NBDA in a Bayesian context.
timearray: Diffusions times for RJMCMC Example 1
Times: This dataset contains the diffusion times using in the This...
Xx: x coordinates for data example for NBDA with random effects...
Yy: y coordinates for data example for NBDA with random effects...