gAlgo-class

<code>"gAlgo"</code> is a <code>"VIRTUAL"</code> class, the common basis of classes
 <code>"pcAlgo"</code> and <code>"fciAlgo"</code>.
We describe the common slots here; for more see the help pages of the
 specific classes.

classes

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

gAlgo-class function

<dl>
 <dt><code>call</code>:</dt>
<dd>a <code><a href="/link/call?package=pcalg&version=2.7-12" data-mini-rdoc="pcalg::call">call</a></code> object: the original function call.</dd> <dt><code>n</code>:</dt>
<dd>an <code>"integer"</code>, the sample size used to estimate the graph.</dd> <dt><code>max.ord</code>:</dt>
<dd>an <code><a href="/link/integer?package=pcalg&version=2.7-12" data-mini-rdoc="pcalg::integer">integer</a></code>, the maximum size of
 the conditioning set used in the conditional independence tests of
 the (first part of the algorithm), in function <code>skeleton</code>.</dd> <dt><code>n.edgetests</code>:</dt>
<dd>the number of conditional independence
 tests performed by the (first part of the) algorithm.</dd> <dt><code>sepset</code>:</dt>
<dd>a <code><a href="/link/list?package=pcalg&version=2.7-12" data-mini-rdoc="pcalg::list">list</a></code>, the conditioning sets
 that led to edge deletions. The set that led to the removal of
 the edge <code>i -- j</code> is saved in either <code>sepset[[i]][[j]]</code> or
 in <code>sepset[[j]][[i]]</code>.</dd> <dt><code>pMax</code>:</dt>
<dd>a numeric square <code><a href="/link/matrix?package=pcalg&version=2.7-12" data-mini-rdoc="pcalg::matrix">matrix</a></code>, where the
 \((i,j)\)th entry contains the maximal p-value of all conditional
 independence tests for edge \(i -- j\).</dd> 
</dl>

Slots

Author

Class "gAlgo" — gAlgo-class

<dl>
 <dt><code>call</code>:</dt>
<dd>a <code><a href='https://rdrr.io/r/base/call.html'>call</a></code> object: the original function call.</dd>

 <dt><code>n</code>:</dt>
<dd>an <code>"integer"</code>, the sample size used to estimate the graph.</dd>

 <dt><code>max.ord</code>:</dt>
<dd>an <code><a href='https://rdrr.io/r/base/integer.html'>integer</a></code>, the maximum size of
 the conditioning set used in the conditional independence tests of
 the (first part of the algorithm), in function <code>skeleton</code>.</dd>

 <dt><code>n.edgetests</code>:</dt>
<dd>the number of conditional independence
 tests performed by the (first part of the) algorithm.</dd>

 <dt><code>sepset</code>:</dt>
<dd>a <code><a href='https://rdrr.io/r/base/list.html'>list</a></code>, the conditioning sets
 that led to edge deletions. The set that led to the removal of
 the edge <code>i -- j</code> is saved in either <code>sepset[[i]][[j]]</code> or
 in <code>sepset[[j]][[i]]</code>.</dd>

 <dt><code>pMax</code>:</dt>
<dd>a numeric square <code><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></code>, where the
 \((i,j)\)th entry contains the maximal p-value of all conditional
 independence tests for edge \(i -- j\).</dd>

 
</dl>

Class <code>"gAlgo"</code>

gAlgo-class: Class `"gAlgo"`

Description

Arguments

Slots

Author

See Also

Examples