| Type: | Package |
| Title: | Model-Based Clustering via Mixtures of Unrestricted Skew-t Sactor Analyzer Models |
| Version: | 2.0 |
| Date: | 2016-05-20 |
| Author: | Paula M. Murray, Ryan P. Browne, and Paul D. McNicholas |
| Maintainer: | Paula M. Murray <paula.murray@math.mcmaster.ca> |
| Description: | Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm. |
| Depends: | tmvtnorm, mvtnorm, MCMCpack, MASS, stats |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2016-05-23 21:57:53 UTC; paulamurray |
| Repository: | CRAN |
| Date/Publication: | 2016-05-24 01:13:18 |
The Swiss Banknote Data
Description
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. This data is also available in the alr package in R.
Usage
data(banknote)References
Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman and Hall.
Examples
data(banknote) # Loads the brown bread data set
head(banknote) # Displays the first six rows of the brown bread data set
Mixtures of 'Unrestricted' Skew-t Factor Analyzers via the EM algorithm
Description
Fits a mixture of 'unrestricted' skew-t factor analyzers via the EM algorithm for estimation of model parameters
Usage
uskewFA(x, G, q, init=1, max.it=100)
Arguments
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. |
Value
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. |
Note
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.
Author(s)
Paula M. Murray, Ryan P. Browne, and Paul D. McNicholas
Maintainer: Paula M. Murray <paula.murray@math.mcmaster.ca>
References
Murray, P.M., Browne, R.P., and McNicholas, P.D. (2014), "Mixtures of 'Unrestricted' Skew-t Factor Analyzers". Arxiv preprint arXiv:1310.6224
See Also
Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman and Hall.
Examples
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
Model-Based Clustering via Mixtures of 'Unrestricted' Skew-t Factor Analyzers
Description
Contains the function uskewFA for fitting mixtures of 'unrestricted' skew-t factor analyzer models
Details
| Package: | uskewFactors |
| Type: | Package |
| Version: | 2.0 |
| Date: | 2016-05-20 |
| License: | WGPL (>=2) |
Author(s)
Paula M. Murray, Ryan P. Browne, and Paul D. McNicholas
Maintainer: Paula M. Murray <paula.murray@math.mcmaster.ca>
References
Murray, P.M., Browne, R.P., and McNicholas, P.D. (2014), "Mixtures of 'Unrestricted' Skew-t Factor Analyzers". Arxiv preprint arXiv:1310.6224
See Also
Details, references, and examples are given under uskewFA.