MGHFA: Mixture of generalized hyperbolic factor analyzers (MGHFA).

In MixGHD: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions

Description

Carries out model-based clustering and classification using the mixture of generalized hyperbolic factor analyzers.

Usage

MGHFA(data=NULL, gpar0=NULL, G=2, max.iter=100, 
label =NULL  ,q=2,eps=1e-2 , method="kmeans", scale=TRUE ,nr=10)

Arguments

data	A matrix or data frame such that rows correspond to observations and columns correspond to variables.
gpar0	(optional) A list containing the initial parameters of the mixture model. See the 'Details' section.
G	The range of values for the number of clusters.
max.iter	(optional) A numerical parameter giving the maximum number of iterations each EM algorithm is allowed to use.
label	( optional) A n dimensional vector, if label[i]=k then observation belongs to group k, If label[i]=0 then observation has no known group, if NULL then the data has no known groups.
q	The range of values for the number of factors.
eps	(optional) A number specifying the epsilon value for the convergence criteria used in the EM algorithms. For each algorithm, the criterion is based on the difference between the log-likelihood at an iteration and an asymptotic estimate of the log-likelihood at that iteration. This asymptotic estimate is based on the Aitken acceleration.
method	( optional) A string indicating the initialization criterion, if not specified kmeans clustering is used. Alternative methods are: hierarchical "hierarchical" and model based "modelBased" clustering
scale	( optional) A logical value indicating whether or not the data should be scaled, true by default.
nr	( optional) A number indicating the number of starting value when random is used, 10 by default.

Details

The arguments gpar0, if specified, is a list structure containing at least one p dimensional vector mu, alpha and phi, a pxp matrix gamma, a 2 dimensional vector cpl containing omega and lambda.

Value

A list with components

BIC	Bayesian information criterion value for each combination of G and q.
model	A list containing the following elements for the selected model
BIC	Bayesian information criterion value.
gpar	A list of the model parameters.
loglik	The log-likelihood values.
map	A vector of integers indicating the maximum a posteriori classifications for the best model.
z	A matrix giving the raw values upon which map is based.

Author(s)

Cristina Tortora, Ryan P. Browne, and Paul D. McNicholas. Maintainer: Cristina Tortora <[e-mail]>

References

C. Tortora, P.D. McNicholas, and R.P. Browne (2015). A Mixture of Generalized Hyperbolic Factor Analyzers. Advanced in Data Analysis and Classification.

Examples

## Classification
#70% belong to the training set
data(sonar)
 label=sonar[,61]
 set.seed(135)
 a=round(runif(62)*207+1)
 label[a]=0
 
 
##model estimation
model=MGHFA(data=sonar[,1:60],  G=2, max.iter=25  ,q=2,label=label)

#result
table(model$model$map,sonar[,61])

MixGHD documentation built on May 2, 2019, 5:49 p.m.