The case study dataset is about growth performance and abattoir findings in pigs commercial production in a selected set of 15 Canadian farms collected in March 1987.

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

adg function

An adapted data frame of the original dataset which consists of 341 observations of 8 variables and a grouping variable (farm).<dl class="dl-horizontal">
 <dt>farm</dt><dd>farm ID.</dd>
 <dt>female</dt><dd>sex of the pig (1=female, 0=castrated).</dd>
 <dt>age</dt><dd>days elapsed from birth to slaughter (days).</dd>
 <dt>adg</dt><dd>average daily weight gain (grams).</dd>
 <dt>pneumS</dt><dd>presence of moderate to severe pneumonia.</dd>
 <dt>eggs</dt><dd>presence of fecal/gastrointestinal nematode eggs at time of slaughter.</dd>
 <dt>wormCount</dt><dd>count of nematodes in small intestine at time of slaughter.</dd>
 <dt>livdam</dt><dd>presence of liver damage (parasite-induced white spots).</dd>
 <dt>AR</dt><dd>presence of atrophic rhinitis.</dd>
 </dl>

Format

Dataset related to average daily growth performance and abattoir findings in pigs commercial production. — adg

An adapted data frame of the original dataset which consists of 341 observations of 8 variables and a grouping variable (farm).<dl class='dl-horizontal'>
 <dt>farm</dt><dd>farm ID.</dd>
 <dt>female</dt><dd>sex of the pig (1=female, 0=castrated).</dd>
 <dt>age</dt><dd>days elapsed from birth to slaughter (days).</dd>
 <dt>adg</dt><dd>average daily weight gain (grams).</dd>
 <dt>pneumS</dt><dd>presence of moderate to severe pneumonia.</dd>
 <dt>eggs</dt><dd>presence of fecal/gastrointestinal nematode eggs at time of slaughter.</dd>
 <dt>wormCount</dt><dd>count of nematodes in small intestine at time of slaughter.</dd>
 <dt>livdam</dt><dd>presence of liver damage (parasite-induced white spots).</dd>
 <dt>AR</dt><dd>presence of atrophic rhinitis.</dd>
 </dl>

adg: Dataset related to average daily growth performance and abattoir findings in pigs commercial production.

Description

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Format

References