pigs.vienna

The data we consider here comprise of a randomly chosen
batch of 50 pigs from each of 500 randomly chosen
pig producers in the UK. The dataset consists of 25000 observations 10 binary variables and a group variable. These are `finishing pigs', animals
about to enter the human food chain at an abattoir. Further description of the data set is present on the vignette.

datasets

Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' 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 package which can be obtained from <http://www.r-inla.org>. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website <http://r-bayesian-networks.org>.

Gilles Kratzer

Modelling Multivariate Data with Additive Bayesian Networks

Fraser Ian Lewis

Reinhard Furrer

Marta Pittavino

pigs.vienna function

A data frame with a mixture of 10 discrete variables, each of which is set as a factor, and a group variable.<dl class="dl-horizontal">
 <dt>PC</dt><dd>Binary.</dd>
 <dt>PT</dt><dd>Binary.</dd>
 <dt>MS</dt><dd>Binary.</dd>
 <dt>HS</dt><dd>Binary.</dd>
 <dt>TAIL</dt><dd>Binary.</dd>
 <dt>Abscess</dt><dd>Binary.</dd>
 <dt>Pyaemia</dt><dd>Binary.</dd>
 <dt>EPcat</dt><dd>Binary.</dd>
 <dt>PDcat</dt><dd>Binary.</dd>
 <dt>plbinary</dt><dd>Binary.</dd>
 <dt>batch</dt><dd>Group variable, corresponding to the 500 different pig producers</dd>
 </dl>

Format

Dataset related to diseases present in `finishing pigs', animals
about to enter the human food chain at an abattoir. — pigs.vienna

A data frame with a mixture of 10 discrete variables, each of which is set as a factor, and a group variable.<dl class='dl-horizontal'>
 <dt>PC</dt><dd>Binary.</dd>
 <dt>PT</dt><dd>Binary.</dd>
 <dt>MS</dt><dd>Binary.</dd>
 <dt>HS</dt><dd>Binary.</dd>
 <dt>TAIL</dt><dd>Binary.</dd>
 <dt>Abscess</dt><dd>Binary.</dd>
 <dt>Pyaemia</dt><dd>Binary.</dd>
 <dt>EPcat</dt><dd>Binary.</dd>
 <dt>PDcat</dt><dd>Binary.</dd>
 <dt>plbinary</dt><dd>Binary.</dd>
 <dt>batch</dt><dd>Group variable, corresponding to the 500 different pig producers</dd>
 </dl>

pigs.vienna: Dataset related to diseases present in `finishing pigs', animals about to enter the human food chain at an abattoir.

Description

Arguments

Format

References