add_var

This function adds variables to a Gaussian mixture model.

Gaussian mixture graphical models include Bayesian networks and
dynamic Bayesian networks (their temporal extension) whose local probability
distributions are described by Gaussian mixture models. They are powerful
tools for graphically and quantitatively representing nonlinear dependencies
between continuous variables. This package provides a complete framework to
create, manipulate, learn the structure and the parameters, and perform
inference in these models. Most of the algorithms are described in the PhD
thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

J<c3><a9>r<c3><a9>my Roos

gmgm

Gaussian Mixture Graphical Model Learning and Inference

Jérémy Roos

RATP Group 

add_var function

<dl><dt>gmm</dt>
<dd>An object of class <code>gmm</code>. If <code>NULL</code>, a <code>gmm</code> object
is created with the added variables and one mixture component.</dd>
<dt>var</dt>
<dd>A character vector containing the added variables, or a data frame
or numeric matrix whose columns are named after the added variables. In the
first case, for each mixture component, the marginal mean vector of the added
variables is 0 and the marginal covariance matrix the identity matrix. In the
second case, these mean vector and covariance matrix are computed from the
data (after removing the rows that contain missing values).</dd></dl>

Arguments

Add variables to a Gaussian mixture model — add_var

<dl>

<dt>gmm</dt>
<dd>An object of class <code>gmm</code>. If <code>NULL</code>, a <code>gmm</code> object
is created with the added variables and one mixture component.</dd>


<dt>var</dt>
<dd>A character vector containing the added variables, or a data frame
or numeric matrix whose columns are named after the added variables. In the
first case, for each mixture component, the marginal mean vector of the added
variables is 0 and the marginal covariance matrix the identity matrix. In the
second case, these mean vector and covariance matrix are computed from the
data (after removing the rows that contain missing values).</dd>

</dl>

add_var: Add variables to a Gaussian mixture model

Description

Usage

Value

Arguments

See Also

Examples