EMMIXmfa - Mixture Models with Component-Wise Factor Analyzers
We provide functions to fit finite mixtures of
multivariate normal or t-distributions to data with various
factor analytic structures adopted for the covariance/scale
matrices. The factor analytic structures available include
mixtures of factor analyzers and mixtures of common factor
analyzers. The latter approach is so termed because the matrix
of factor loadings is common to components before the
component-specific rotation of the component factors to make
them white noise. Note that the component-factor loadings are
not common after this rotation. Maximum likelihood estimators
of model parameters are obtained via the
Expectation-Maximization algorithm. See descriptions of the
algorithms used in McLachlan GJ, Peel D (2000)
<doi:10.1002/0471721182.ch8> McLachlan GJ, Peel D (2000)
<ISBN:1-55860-707-2> McLachlan GJ, Peel D, Bean RW (2003)
<doi:10.1016/S0167-9473(02)00183-4> McLachlan GJ, Bean RW,
Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> Baek
J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149>
Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112>
McLachlan GJ, Baek J, Rathnayake SI (2011)
<doi:10.1002/9781119995678.ch9>.
