pack_theta

Packs mixture weights, means, and covariance(s) into a single numeric vector.
Uses the last component as the baseline for mixture weights (g-1 logits stored).

Implements a semi-supervised learning framework for finite mixture
models under a mixed-missingness mechanism. The approach models both
missing completely at random (MCAR) and entropy-based missing at random
(MAR) processes using a logistic–entropy formulation. Estimation is carried
out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
initialisation routines for stable convergence. The methodology relates to
the statistical perspective and informative missingness behaviour discussed
in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
provides functions for data simulation, model estimation, prediction, and
theoretical Bayes error evaluation for analysing partially labelled data
under a mixed-missingness mechanism.

Jinran Wu

SSLfmm

Semi-Supervised Learning under a Mixed-Missingness Mechanism in
Finite Mixture Models

Geoffrey J. McLachlan

pack_theta function

<dl><dt>pi_k</dt>
<dd>Numeric vector of length g with mixture weights (positive, sum to 1).</dd>
<dt>mu_k</dt>
<dd>List of length g; each element a numeric vector of length p (component means).</dd>
<dt>Sigma</dt>
<dd>Covariance: if <code>ncov = 1</code>, a single p x p matrix;
if <code>ncov = 2</code>, a list of g p x p matrices.</dd>
<dt>g</dt>
<dd>Integer: number of components.</dd>
<dt>p</dt>
<dd>Integer: dimension.</dd>
<dt>ncov</dt>
<dd>Integer: covariance structure; 1 for shared covariance, 2 for class-specific.</dd></dl>

Arguments

Pack FMM Parameters into a Vector — pack_theta

<dl>

<dt>pi_k</dt>
<dd>Numeric vector of length g with mixture weights (positive, sum to 1).</dd>


<dt>mu_k</dt>
<dd>List of length g; each element a numeric vector of length p (component means).</dd>


<dt>Sigma</dt>
<dd>Covariance: if <code>ncov = 1</code>, a single p x p matrix;
if <code>ncov = 2</code>, a list of g p x p matrices.</dd>


<dt>g</dt>
<dd>Integer: number of components.</dd>


<dt>p</dt>
<dd>Integer: dimension.</dd>


<dt>ncov</dt>
<dd>Integer: covariance structure; 1 for shared covariance, 2 for class-specific.</dd>

</dl>

pack_theta: Pack FMM Parameters into a Vector

Description

Usage

Value

Arguments