pcor.test

Test for conditional independence between two variables, given the
 other ones, assuming a multivariate normal distribution.

multivariate

htest

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.test function

<dl> <dt>r</dt>
<dd>a partial correlation coefficient, computed by <code>pcor</code>.</dd> <dt>q</dt>
<dd>the number of variables in the conditioning set.</dd> <dt>n</dt>
<dd>integer &gt; 0, the sample size.</dd></dl>

Arguments

Author

Test for zero partial association — pcor.test

<dl>

 <dt>r</dt>
<dd>a partial correlation coefficient, computed by <code>pcor</code>.</dd>

 <dt>q</dt>
<dd>the number of variables in the conditioning set.</dd>

 <dt>n</dt>
<dd>integer &gt; 0, the sample size.</dd>

</dl>

pcor.test: Test for zero partial association

Description

Usage

Value

Arguments

Author

See Also

Examples