logLik.mvn

Returns the final EM objective; for models with missing data this is not
the exact log-likelihood of the observed data.

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

logLik.mvn function

<dl><dt>object</dt>
<dd>A <code>mvn</code> object.</dd>
<dt>...</dt>
<dd>Unused.</dd></dl>

Arguments

Log-Likelihood for Fitted MVN Model — logLik.mvn

<dl>

<dt>object</dt>
<dd>A <code>mvn</code> object.</dd>


<dt>...</dt>
<dd>Unused.</dd>

</dl>

logLik.mvn: Log-Likelihood for Fitted MVN Model

Description

Usage

Value

Arguments