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EMMIXmfa (version 2.0.11)

Mixture Models with Component-Wise Factor Analyzers

Description

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) McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D, Bean RW (2003) McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) Baek J, McLachlan GJ, Flack LK (2010) Baek J, McLachlan GJ (2011) McLachlan GJ, Baek J, Rathnayake SI (2011) .

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install.packages('EMMIXmfa')

Monthly Downloads

186

Version

2.0.11

License

GPL (>= 2)

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Maintainer

Suren Rathnayake

Last Published

December 16th, 2019

Functions in EMMIXmfa (2.0.11)

factor_scores

Computes Factor Scores
EMMIXmfa-package

Mixture Models with Component-Wise Factor Analyzers
minmis

Minimum Number of Misallocations
gmf

General Matrix Factorization
mcfa

Mixture of Common Factor Analyzers
print.emmix

Print Method for Class 'emmix'
plot_factors

Plot Function for Factor Scores
predict.emmix

Extend Clustering to New Observations
ari

Computes adjusted Rand Index
mfa

Mixtures of Factor Analyzers
rmix

Random Deviates from EMMIX Models