artificial.chds

A dataset with dummy data, based on Child Health and Development Studies (CHDS).

datasets

Create and learn Chain Event Graph (CEG) models using a Bayesian
framework. It provides us with a Hierarchical Agglomerative algorithm to
search the CEG model space.
The package also includes several facilities for visualisations of the
objects associated with a CEG. The CEG class can represent a range of
relational data types, and supports arbitrary vertex, edge and graph
attributes. A Chain Event Graph is a tree-based graphical model that
provides a powerful graphical interface through which domain experts can
easily translate a process into sequences of observed events using plain
language. CEGs have been a useful class of graphical model especially to
capture context-specific conditional independences. References: Collazo R,
Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
(forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE.
The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
<doi:10.1214/15-EJS1068>.

Pier Taranti

Chain Event Graph

Rodrigo Collazo

artificial.chds function

a data.frame with 1500 rows and 4 categorical variables. The variables
 names and values are compliant with CHDS, but the values are randomly filled.<dl class="dl-horizontal">
 <dt>Social</dt><dd>High, Low</dd>
 <dt>Economic</dt><dd>High, Low</dd>
 <dt>Event</dt><dd>High, Average, Low</dd>
 <dt>Admission</dt><dd>No, Yes</dd>
 </dl>

Format

test dataset - artificial.chds. — artificial.chds

a data.frame with 1500 rows and 4 categorical variables. The variables
 names and values are compliant with CHDS, but the values are randomly filled.<dl class='dl-horizontal'>
 <dt>Social</dt><dd>High, Low</dd>
 <dt>Economic</dt><dd>High, Low</dd>
 <dt>Event</dt><dd>High, Average, Low</dd>
 <dt>Admission</dt><dd>No, Yes</dd>
 </dl>

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artificial.chds: test dataset - artificial.chds.

Description

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Format

Examples