plot_missingness

This function plots the smoothed values of `-log(entropy)` against the missingness mechanism
and a boxplot of entropy for labeled vs. unlabeled observations.

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.

Ziyang Lyu

gmmsslm

Semi-Supervised Gaussian Mixture Model with a Missing-Data
Mechanism

Ziyang Lyu 

Daniel Ahfock 

Ryan Thompson 

Geoffrey J. McLachlan 

plot_missingness function

<dl><dt>dat</dt>
<dd>An \(n\times p\) matrix where each row represents an individual observation</dd>
<dt>g</dt>
<dd>Number of multivariate normal classes.</dd>
<dt>parlist</dt>
<dd>A list containing the required parameters \((\pi, \mu, \Sigma)\).</dd>
<dt>zm</dt>
<dd>An n-dimensional vector containing the class labels including the missing-label denoted as NA.</dd>
<dt>bandwidth</dt>
<dd>Bandwidth for kernel smoothing. Default is 5.</dd>
<dt>range.x</dt>
<dd>Range for x values. Default is c(0, 5).</dd>
<dt>ylim</dt>
<dd>The y-axis limits in the form of c(ylim[1], ylim[2]). Default is NULL.</dd>
<dt>kernel</dt>
<dd>Kernel type for smoothing. Default is 'normal'.</dd></dl>

Arguments

Plot Missingness Mechanism and Boxplot — plot_missingness

<dl>

<dt>dat</dt>
<dd>An \(n\times p\) matrix where each row represents an individual observation</dd>


<dt>g</dt>
<dd>Number of multivariate normal classes.</dd>


<dt>parlist</dt>
<dd>A list containing the required parameters \((\pi, \mu, \Sigma)\).</dd>


<dt>zm</dt>
<dd>An n-dimensional vector containing the class labels including the missing-label denoted as NA.</dd>


<dt>bandwidth</dt>
<dd>Bandwidth for kernel smoothing. Default is 5.</dd>


<dt>range.x</dt>
<dd>Range for x values. Default is c(0, 5).</dd>


<dt>ylim</dt>
<dd>The y-axis limits in the form of c(ylim[1], ylim[2]). Default is NULL.</dd>


<dt>kernel</dt>
<dd>Kernel type for smoothing. Default is 'normal'.</dd>

</dl>

plot_missingness: Plot Missingness Mechanism and Boxplot

Description

Usage

Value

Arguments