FitMVN

Given a matrix of n x d-dimensional random vectors, possibly containing
missing elements, estimates the mean and covariance of the best fitting
multivariate normal distribution.

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

FitMVN function

<dl><dt>data</dt>
<dd>Numeric data matrix.</dd>
<dt>init_mean</dt>
<dd>Optional initial mean vector.</dd>
<dt>fix_mean</dt>
<dd>Fix the mean to its starting value? Initial values must be
provided if <code>TRUE</code>.</dd>
<dt>init_cov</dt>
<dd>Optional initial covariance matrix.</dd>
<dt>lambda</dt>
<dd>Optional ridge term added to covariance matrix to ensure 
positive definiteness.</dd>
<dt>maxit</dt>
<dd>Maximum number of EM iterations.</dd>
<dt>eps</dt>
<dd>Minimum acceptable increment in the EM objective.</dd>
<dt>report</dt>
<dd>Report fitting progress?</dd></dl>

Arguments

Fit Multivariate Normal Distribution — FitMVN

<dl>

<dt>data</dt>
<dd>Numeric data matrix.</dd>


<dt>init_mean</dt>
<dd>Optional initial mean vector.</dd>


<dt>fix_mean</dt>
<dd>Fix the mean to its starting value? Initial values must be
provided if <code>TRUE</code>.</dd>


<dt>init_cov</dt>
<dd>Optional initial covariance matrix.</dd>


<dt>lambda</dt>
<dd>Optional ridge term added to covariance matrix to ensure 
positive definiteness.</dd>


<dt>maxit</dt>
<dd>Maximum number of EM iterations.</dd>


<dt>eps</dt>
<dd>Minimum acceptable increment in the EM objective.</dd>


<dt>report</dt>
<dd>Report fitting progress?</dd>

</dl>

FitMVN: Fit Multivariate Normal Distribution

Description

Usage

Value

Arguments