ex5.dag.data

434 observations across with 18 variables, 6 binary and 12 continuous, 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). 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.

ex5.dag.data: Valdiation data set for use with abn library examples

Description

Usage

Arguments