uskewFA: Mixtures of 'Unrestricted' Skew-t Factor Analyzers via the EM...
In uskewFactors: Model-Based Clustering via Mixtures of Unrestricted Skew-t Sactor Analyzer Models

Description Usage Arguments Value Note Author(s) References See Also Examples

Fits a mixture of 'unrestricted' skew-t factor analyzers via the EM algorithm for estimation of model parameters

1	uskewFA(x, G, q, init=1, max.it=100)

`x`	A numeric matrix.
`G`	The number of mixture components to fit.
`q`	The number of latent factors.
`init`	This number controls the starting values that are used: (1) k-means, or (2) random.
`max.it`	The maximum number of iterations of the EM algorithm.

`map`	A vector of the maximum a posteriori group memberships.
`bic`	The value of the Bayesian Information Criterion.
`zhat`	The matrix of estimated probabilities of group membership.
`likelihood`	A vector containing the value of the complete-data log-likelihood computed at each iteration of the EM algorithm.

This package contains measurements on 200 Swiss banknotes: 100 genuine and 100 counterfeit. The variables are length of bill, width of left edge, width of right edge , bottom margin width and top margin width. All measurements are in millimetres. The data source is noted below.

Paula M. Murray, Ryan P. Browne, and Paul D. McNicholas

Maintainer: Paula M. Murray <paula.murray@math.mcmaster.ca>

Murray, P.M., Browne, R.P., and McNicholas, P.D. (2014), "Mixtures of 'Unrestricted' Skew-t Factor Analyzers". Arxiv preprint arXiv:1310.6224

Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman and Hall.

data("banknote")
x=banknote[,c(5,6)]
# We let max.it=3 for a speedy illustration.
# More 	iterations are needed to ensure
# convergence.
results=uskewFA(x,G=2,q=1,max.it=3)
results