surdata

Simulated data following a seemingly unrelated regression model.

datasets

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

surdata function

A data frame with 600 observations on the following 4 variables.<dl>
 <dt><code>A</code></dt>
<dd>a numeric response vector</dd> <dt><code>B</code></dt>
<dd>a numeric response vector</dd> <dt><code>X</code></dt>
<dd>a numeric vector</dd> <dt><code>Z</code></dt>
<dd>a numeric vector with codes <code>1</code> and <code>-1</code> for a binary variables.</dd> 
</dl>

Format

A simulated data set — surdata

A data frame with 600 observations on the following 4 variables.<dl>
 <dt><code>A</code></dt>
<dd>a numeric response vector</dd>

 <dt><code>B</code></dt>
<dd>a numeric response vector</dd>

 <dt><code>X</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Z</code></dt>
<dd>a numeric vector with codes <code>1</code> and <code>-1</code> for a binary variables.</dd>

 
</dl>

surdata: A simulated data set

Description

Usage

Arguments

Format

Examples