pcor

Computes the partial correlation between two variables given
 a set of other variables.

multivariate

models

Provides functions for defining
mixed graphs containing three types of edges, directed,
undirected and bi-directed, with possibly multiple edges.
These graphs are useful because they capture fundamental
independence structures in multivariate distributions
and in the induced distributions after marginalization
and conditioning.
The package is especially concerned with Gaussian graphical
models for
(i) ML estimation for directed acyclic graphs, undirected and
bi-directed graphs and ancestral graph models
(ii) testing several conditional independencies
(iii) checking global identification of DAG Gaussian models
with one latent variable
(iv) testing Markov equivalences and generating Markov
equivalent graphs of specific types.

Giovanni M Marchetti

Graphical Markov Models with Mixed Graphs

Giovanni M. Marchetti

Mathias Drton

Kayvan Sadeghi

pcor function

<dl> <dt>u</dt>
<dd>a vector of integers of length &gt; 1. The first two integers are
 the indices of variables the correlation of which must be
 computed. The rest of the vector is the conditioning set.</dd> <dt>S</dt>
<dd>a symmetric positive definite matrix, a sample covariance
 matrix.</dd></dl>

Arguments

Author

Partial correlation — pcor

<dl>

 <dt>u</dt>
<dd>a vector of integers of length &gt; 1. The first two integers are
 the indices of variables the correlation of which must be
 computed. The rest of the vector is the conditioning set.</dd>

 <dt>S</dt>
<dd>a symmetric positive definite matrix, a sample covariance
 matrix.</dd>

</dl>

pcor: Partial correlation

Description

Usage

Value

Arguments

Author

See Also

Examples