plotAG

This function is DEPRECATED! Use the <code>plot</code> method of the
<code>fciAlgo</code> class instead.

multivariate

models

graphs

Functions for causal structure
learning and causal inference using graphical models. The main algorithms
for causal structure learning are PC (for observational data without hidden
variables), FCI and RFCI (for observational data with hidden variables),
and GIES (for a mix of data from observational studies
(i.e. observational data) and data from experiments
involving interventions (i.e. interventional data) without hidden
variables). For causal inference the IDA algorithm, the Generalized
Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
and some related functions are implemented. Functions for incorporating
background knowledge are provided.

Markus Kalisch

pcalg

Methods for Graphical Models and Causal Inference

Alain Hauser

Martin Maechler

Diego Colombo

Doris Entner

Patrik Hoyer

Antti Hyttinen

Jonas Peters

Nicoletta Andri

Emilija Perkovic

Preetam Nandy

Philipp Ruetimann

Daniel Stekhoven

Manuel Schuerch

Marco Eigenmann

Leonard Henckel

Joris Mooij

plotAG function

<dl> <dt>amat</dt>
<dd>Adjacency matrix (coding 0,1,2,3 for no edge, circle,
 arrowhead, tail; e.g., <code>amat[a,b] = 2</code> and <code>amat[b,a] = 3</code>
 implies a -&gt; b)</dd>
 
</dl>

Arguments

Markus Kalisch (<a href="/link/kalisch%40stat.math.ethz.ch?package=pcalg&version=2.7-11" data-mini-rdoc="pcalg::kalisch@stat.math.ethz.ch">kalisch@stat.math.ethz.ch</a>)

Author

Plot partial ancestral graphs (PAG) — plotAG

<dl>

 <dt>amat</dt>
<dd>Adjacency matrix (coding 0,1,2,3 for no edge, circle,
 arrowhead, tail; e.g., <code>amat[a,b] = 2</code> and <code>amat[b,a] = 3</code>
 implies a -&gt; b)</dd>
 
</dl>

Markus Kalisch (<a href='mailto:kalisch@stat.math.ethz.ch'>kalisch@stat.math.ethz.ch</a>)

Plot partial ancestral graphs (PAG)

plotAG: Plot partial ancestral graphs (PAG)

Description

Usage

Arguments

Author

See Also