ex4.dag.data

2000 observations across with 10 binary variables and one grouping variable. Real (anonymised) data of unknown structure.

datasets

Additive Bayesian network models are equivalent to
        Bayesian multivariate regression using graphical modelling.
        This library provides routines to help determine optimal
        Bayesian network models for a given data set, where these
        models are used to identify statistical dependencies in messy,
        complex data. The additive formulation of these models is
        equivalent to multivariate generalised linear modelling
        (including mixed models with iid random effects). The usual
        term to describe this model selection process is structure
        discovery. The core functionality is concerned with model
        selection - determining the most robust empirical model of data
        from interdependent variables. Laplace approximations are used
        to estimate goodness of fit metrics and model parameters, and
        wrappers are also included to the INLA library. A comprehensive
        set of documented case studies, numerical accuracy/quality
        assurance exercises, and additional documentation are available
        from the abn website.

ex4.dag.data: Valdiation data set for use with abn library examples

Description

Usage

Arguments