plotdiffs

This function plots edges from two graphs in one and indicates similarities and differences between these graphs.
It is also possible to use this function for plotting mistakes in estimated graph when the ground truth graph is known.

Implementation of a collection of MCMC methods for Bayesian structure learning
of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
the search space, the algorithm employs a hybrid approach, combining constraint-based
learning with search and score. A reduced search space is initially defined on the basis
of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
search and score. Search and score is then performed following two approaches:
Order MCMC, or Partition MCMC.
The BGe score is implemented for continuous data and the BDe score is implemented
for binary data or categorical data. The algorithms may provide the maximum a posteriori
(MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
References:
J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>,
J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>,
M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>,
P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

Polina Suter

BiDAG

Bayesian Inference for Directed Acyclic Graphs

plotdiffs function

<dl><dt>graph1</dt>
<dd>object of class graphNEL or its adjacency matrix</dd>
<dt>graph2</dt>
<dd>object of class graphNEL or its adjacency matrix</dd>
<dt>estimated</dt>
<dd>logical, indicates if graph1 is estimated graph and graph2 is ground truth DAG, TRUE by default; this affects the legend and colouring of the edges</dd>
<dt>name1</dt>
<dd>character, custom name for 'graph1'</dd>
<dt>name2</dt>
<dd>character, custom name for 'graph2'</dd>
<dt>clusters</dt>
<dd>(optional) a list of nodes to be represented on the graph as clusters</dd>
<dt>...</dt>
<dd>optional parameters passed to <code>Rgraphviz</code> plotting functions e.g. <code>main</code>, <code>fontsize</code></dd></dl>

Arguments

Author

Plotting difference between two graphs — plotdiffs

<dl>

<dt>graph1</dt>
<dd>object of class graphNEL or its adjacency matrix</dd>


<dt>graph2</dt>
<dd>object of class graphNEL or its adjacency matrix</dd>


<dt>estimated</dt>
<dd>logical, indicates if graph1 is estimated graph and graph2 is ground truth DAG, TRUE by default; this affects the legend and colouring of the edges</dd>


<dt>name1</dt>
<dd>character, custom name for 'graph1'</dd>


<dt>name2</dt>
<dd>character, custom name for 'graph2'</dd>


<dt>clusters</dt>
<dd>(optional) a list of nodes to be represented on the graph as clusters</dd>


<dt>...</dt>
<dd>optional parameters passed to <code>Rgraphviz</code> plotting functions e.g. <code>main</code>, <code>fontsize</code></dd>

</dl>

plotdiffs: Plotting difference between two graphs

Description

Usage

Value

Arguments

Author

Examples