mvn-class

Defines a class to hold multivariate normal models.

Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.

Zachary McCaw

MGMM

Missingness Aware Gaussian Mixture Models

mvn-class function

<dl>
<dt><code>Completed</code></dt>
<dd>Completed data, with missing values imputed to their
posterior expectations.</dd></dl>
<dt><code>Covariance</code></dt>
<dd>Fitted covariance matrix.</dd>
<dt><code>Data</code></dt>
<dd>Original data, with missing values present.</dd>
<dt><code>Mean</code></dt>
<dd>Fitted mean vector.</dd>
<dt><code>Objective</code></dt>
<dd>Final value of the EM objective.</dd>

Slots

Multivariate Normal Model Class — mvn-class

<dl>
<dt><code>Completed</code></dt>
<dd>Completed data, with missing values imputed to their
posterior expectations.</dd>


<dt><code>Covariance</code></dt>
<dd>Fitted covariance matrix.</dd>


<dt><code>Data</code></dt>
<dd>Original data, with missing values present.</dd>


<dt><code>Mean</code></dt>
<dd>Fitted mean vector.</dd>


<dt><code>Objective</code></dt>
<dd>Final value of the EM objective.</dd>


</dl>

mvn-class: Multivariate Normal Model Class

Description

Arguments

Slots