The dataset is about the Feline calicivirus (FCV) infection among cats in Switzerland.
FCV is a virus that occurs worldwide in domestic cats but also in exotic felids.
FCV is a highly contagious virus that is the major cause of upper respiratory
disease or cat flue that affects felids. This is a complex disease caused by
different viral and bacterial pathogens, i.e., FCV, FHV-1, Mycoplasma felis,
Chlamydia felis and Bordetella bronchiseptica.
It can be aggravated by retrovirus infections such as FeLV and FIV.
This composite dynamic makes it very interesting for a BN modeling approach.
The data were collected between September 2012 and April 2013.

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 <https://www.r-inla.org>.
The computing library JAGS <https://mcmc-jags.sourceforge.io> is used to simulate 'abn'-like data.
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>.

Matteo Delucchi

Modelling Multivariate Data with Additive Bayesian Networks

Reinhard Furrer

Gilles Kratzer

Fraser Iain Lewis

Marta Pittavino

Kalina Cherneva

FCV function

An adapted data frame of the original dataset, which consists of 300 observations of 15 variables.<ul>
<li>FCVFeline Calici Virus status (0/1).</li>
<li>FHV_1Feline Herpes Virus 1 status (0/1).</li>
<li>C_felisC-felis and Chlamydia felis status (0/1).</li>
<li>M_felisMycoplasma felis status (0/1).</li>
<li>B_bronchisepticaB-bronchiseptica &amp; Bordetella bronchispetica status (0/1).</li>
<li>FeLVfeline leukosis virus status (0/1).</li>
<li>FIVfeline immunodeficiency virus status (0/1).</li>
<li>Gingivostomatitisgingivostomatitis complex status (0/1).</li>
<li>URTDURTD complex (upper respiratory complex) (0/1).</li>
<li>Vaccinatedvaccination status (0/1).</li>
<li>Pedigreepedigree (0/1).</li>
<li>Outdooroutdoor access (0/1).</li>
<li>Sexsex and castrated status (M, MN, F, FS).</li>
<li>GroupSizenumber of cats in the group (counts).</li>
<li>Ageage in year (continuous)\.</li>
</ul>

Format

Dataset related to Feline calicivirus infection among cats in Switzerland. — FCV

Dataset related to Feline calicivirus infection among cats in Switzerland.

FCV: Dataset related to Feline calicivirus infection among cats in Switzerland.

Description

Usage

Arguments

Format

References