ex3.dag.data

<p>1000 observations across with 13 binary variables and one grouping variable. Real (anonymised) data of unknown structure.</p>

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

ex3.dag.data function

<p>A data frame with 14 columns, where
<code>b1,b2,...,b13</code> are binary variables encoded as factors and
<code>group</code> is a factor with 100 factors  defining the sampling
groups (10 observations each).</p>

Format

Validation data set for use with abn library examples — ex3.dag.data

Validation data set for use with abn library examples

ex3.dag.data: Validation data set for use with abn library examples

Description

Usage

Arguments

Format