makebugsGroup

Make BUGS model from fitted DAG with grouping

internal

utilities

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

makebugsGroup function

<dl><dt>dag</dt>
<dd>named adjacency matrix representing the DAG. Names correspond to node names.</dd>
<dt>data.dists</dt>
<dd>list of node distributions.</dd>
<dt>stderrors</dt>
<dd>a list named by the node names containing for each element a matrix with the nodes' standard errors</dd>
<dt>group.var</dt>
<dd>only applicable for mixed models and gives the column name in <code>data.df</code> of the grouping variable (which must be a factor denoting group membership).</dd>
<dt>mu</dt>
<dd>Standard deviation of fixed effects.</dd>
<dt>betas</dt>
<dd>Coefficients/slopes of fixed effects .</dd>
<dt>sigma</dt>
<dd>variance of random effects.</dd>
<dt>sigma_alpha</dt>
<dd>variance-covariance matrix corresponding to covariances output from <code>mblogit</code>.</dd></dl>

Arguments

Make BUGS model from fitted DAG with grouping — makebugsGroup

<dl>

<dt>dag</dt>
<dd>named adjacency matrix representing the DAG. Names correspond to node names.</dd>


<dt>data.dists</dt>
<dd>list of node distributions.</dd>


<dt>stderrors</dt>
<dd>a list named by the node names containing for each element a matrix with the nodes' standard errors</dd>


<dt>group.var</dt>
<dd>only applicable for mixed models and gives the column name in <code>data.df</code> of the grouping variable (which must be a factor denoting group membership).</dd>


<dt>mu</dt>
<dd>Standard deviation of fixed effects.</dd>


<dt>betas</dt>
<dd>Coefficients/slopes of fixed effects .</dd>


<dt>sigma</dt>
<dd>variance of random effects.</dd>


<dt>sigma_alpha</dt>
<dd>variance-covariance matrix corresponding to covariances output from <code>mblogit</code>.</dd>

</dl>

makebugsGroup: Make BUGS model from fitted DAG with grouping

Description

Usage

Value

Arguments

See Also