unpack_theta

Unpacks mixture weights, means, and covariance(s) from a parameter vector.

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

unpack_theta function

<dl><dt>theta</dt>
<dd>Numeric vector as returned by <code>pack_theta()</code>.</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

Unpack FMM Parameter Vector — unpack_theta

<dl>

<dt>theta</dt>
<dd>Numeric vector as returned by <code>pack_theta()</code>.</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>

unpack_theta: Unpack FMM Parameter Vector

Description

Usage

Value

Arguments