gAlgo-class


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

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) and the Generalized Adjustment Criterion (GAC)
are implemented.

Markus Kalisch

pcalg

Methods for Graphical Models and Causal Inference

gAlgo-class function

<dl>
    <dt><code>call</code>:</dt><dd>a <code><a rd-options="" href="/link/call?package=pcalg&version=2.2-4" 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 rd-options="" href="/link/integer?package=pcalg&version=2.2-4" 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><a rd-options="" href="/link/skeleton?package=pcalg&version=2.2-4" data-mini-rdoc="pcalg::skeleton">skeleton</a></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 rd-options="" href="/link/list?package=pcalg&version=2.2-4" 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 rd-options="" href="/link/matrix?package=pcalg&version=2.2-4" 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> 

gAlgo-class: Class `"gAlgo"`

Description

Arguments

Slots

See Also

Examples