CalHar

Calculates the Calinski-Harabasz index (ratio of between-cluster to
within-cluster dispersion; higher values indicate better separation).

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

CalHar function

<dl><dt>data</dt>
<dd>Numeric matrix of observations.</dd>
<dt>assign</dt>
<dd>Vector of cluster assignments.</dd>
<dt>means</dt>
<dd>List of cluster mean vectors.</dd></dl>

Arguments

Calinski-Harabasz Index — CalHar

<dl>

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


<dt>assign</dt>
<dd>Vector of cluster assignments.</dd>


<dt>means</dt>
<dd>List of cluster mean vectors.</dd>

</dl>

CalHar: Calinski-Harabasz Index

Description

Usage

Value

Arguments