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 grouping 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

internal

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

pigs.vienna function

A data frame with a mixture of 10 discrete variables, each of which is set as a factor, and a grouping variable.<dl>
<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 grouping variable.<dl>
<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

Usage

Arguments

Format

Details

References