diabetes

The data set contains three measurements made on 145 non-obese adult patients classified into three groups.

datasets

Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

Luca Scrucca

mclust

Gaussian Mixture Modelling for Model-Based Clustering,
Classification, and Density Estimation

Chris Fraley

Adrian E. Raftery

Thomas Brendan Murphy

Michael Fop

diabetes function

A data frame with the following variables:<dl class="dl-horizontal">
<dt>class</dt><dd>The type of diabete: <code>Normal</code>, <code>Overt</code>, and <code>Chemical</code>.</dd>
<dt>glucose</dt><dd>Area under plasma glucose curve after a three hour oral glucose tolerance test (OGTT).</dd>
<dt>insulin</dt><dd>Area under plasma insulin curve after a three hour oral glucose tolerance test (OGTT).</dd>
<dt>sspg</dt><dd>Steady state plasma glucose.</dd>
</dl>

Format

Diabetes data — diabetes

A data frame with the following variables:<dl class='dl-horizontal'>
<dt>class</dt><dd>The type of diabete: <code>Normal</code>, <code>Overt</code>, and <code>Chemical</code>.</dd>
<dt>glucose</dt><dd>Area under plasma glucose curve after a three hour oral glucose tolerance test (OGTT).</dd>
<dt>insulin</dt><dd>Area under plasma insulin curve after a three hour oral glucose tolerance test (OGTT).</dd>
<dt>sspg</dt><dd>Steady state plasma glucose.</dd>
</dl>

diabetes: Diabetes data

Description

Usage

Arguments

Format