beta.special

This function is DEPRECATED! Use <code>ida</code> 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

beta.special function

<dl><dt>dat</dt>
<dd>Data matrix</dd><dt>x.pos, y.pos</dt>
<dd>integer column positions of \(x\) and \(y\) in <code>dat</code>.</dd><dt>verbose</dt>
<dd>0=no comments, 2=detail on estimates</dd><dt>a</dt>
<dd>Significance level of tests for finding CPDAG</dd><dt>myDAG</dt>
<dd>Needed if true correlation matrix shall be computed</dd><dt>myplot</dt>
<dd>Plot estimated graph</dd><dt>perfect</dt>
<dd>True cor matrix is calculated from myDAG</dd><dt>method</dt>
<dd>"local" - local (all combinations of parents in regr.);
 "global" - all DAGs</dd><dt>collTest</dt>
<dd>True - Exclude orientations of undirected edges that
 introduce a new collider</dd><dt>pcObj</dt>
<dd>Fit of PC Algorithm (CPDAG); if this is available, no
 new fit is done</dd><dt>all.dags</dt>
<dd>All DAGs in the format of function allDags; if this is
 available, no new function call allDags is done</dd><dt>u2pd</dt>
<dd>function for converting a UDAG to a PDAG;
 "rand": <code>udag2pdag</code>;
 "relaxed": <code>udag2pdagRelaxed</code>;
 "retry": <code>udag2pdagSpecial</code>.</dd></dl>

Arguments

Markus Kalisch (<a href="/link/kalisch%40stat.math.ethz.ch?package=pcalg&version=2.7-12" data-mini-rdoc="pcalg::kalisch@stat.math.ethz.ch">kalisch@stat.math.ethz.ch</a>)

Author

Compute set of intervention effects — beta.special

<dl>

<dt>dat</dt>
<dd>Data matrix</dd>

<dt>x.pos, y.pos</dt>
<dd>integer column positions of \(x\) and \(y\) in <code>dat</code>.</dd>

<dt>verbose</dt>
<dd>0=no comments, 2=detail on estimates</dd>

<dt>a</dt>
<dd>Significance level of tests for finding CPDAG</dd>

<dt>myDAG</dt>
<dd>Needed if true correlation matrix shall be computed</dd>

<dt>myplot</dt>
<dd>Plot estimated graph</dd>

<dt>perfect</dt>
<dd>True cor matrix is calculated from myDAG</dd>

<dt>method</dt>
<dd>"local" - local (all combinations of parents in regr.);
 "global" - all DAGs</dd>

<dt>collTest</dt>
<dd>True - Exclude orientations of undirected edges that
 introduce a new collider</dd>

<dt>pcObj</dt>
<dd>Fit of PC Algorithm (CPDAG); if this is available, no
 new fit is done</dd>

<dt>all.dags</dt>
<dd>All DAGs in the format of function allDags; if this is
 available, no new function call allDags is done</dd>

<dt>u2pd</dt>
<dd>function for converting a UDAG to a PDAG;
 "rand": <code>udag2pdag</code>;
 "relaxed": <code>udag2pdagRelaxed</code>;
 "retry": <code>udag2pdagSpecial</code>.</dd>

</dl>

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

Compute set of intervention effects

beta.special: Compute set of intervention effects

Description

Usage

Value

Arguments

Author

See Also