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

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The 'abn' R package facilitates Bayesian network analysis, a
probabilistic graphical model that derives from empirical data a
directed acyclic graph (DAG). This DAG describes the dependency
structure between random variables. The R package 'abn' provides
routines to help determine optimal Bayesian network models for a given
data set. These models are used to identify statistical dependencies
in messy, complex data. Their additive formulation is equivalent to
multivariate generalised linear modelling, including mixed models with
independent and identically distributed (iid) random effects. The core
functionality of the 'abn' package revolves around model selection,
also known as structure discovery. It supports both exact and
heuristic structure learning algorithms and does not restrict the data
distribution of parent-child combinations, providing flexibility in
model creation and analysis. The 'abn' package uses Laplace
approximations for metric estimation and includes wrappers to the
'INLA' package. It also employs 'JAGS' for data simulation purposes.
For more resources and information, visit the 'abn' website.

Matteo Delucchi

Modelling Multivariate Data with Additive Bayesian Networks

Reinhard Furrer

Gilles Kratzer

Fraser Iain Lewis

Jonas I. Liechti

Marta Pittavino

Kalina Cherneva

adg function

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

<dt>pneumS</dt>
<dd>presence of moderate to severe pneumonia.</dd>

<dt>female</dt>
<dd>sex of the pig (1=female, 0=castrated).</dd>

<dt>livdam</dt>
<dd>presence of liver damage (parasite-induced white spots).</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>age</dt>
<dd>days elapsed from birth to slaughter (days).</dd>

<dt>adg</dt>
<dd>average daily weight gain (grams).</dd>

<dt>farm</dt>
<dd>farm ID.</dd>


</dl>

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

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References