mclust (version 5.3)

mclustVariance: Template for variance specification for parameterized Gaussian mixture models

Description

Specification of variance parameters for the various types of Gaussian mixture models.

Usage

mclustVariance(modelName, d = NULL, G = 2)

Arguments

modelName

A character string specifying the model.

d

A integer specifying the dimension of the data.

G

An integer specifying the number of components in the mixture model.

Details

The variance component in the parameters list from the output to e.g. me or mstep or input to e.g. estep may contain one or more of the following arguments, depending on the model:

modelName

A character string indicating the model.

d

The dimension of the data.

G

The number of components in the mixture model.

sigmasq

for the one-dimensional models ("E", "V") and spherical models ("EII", "VII"). This is either a vector whose kth component is the variance for the kth component in the mixture model ("V" and "VII"), or a scalar giving the common variance for all components in the mixture model ("E" and "EII").

Sigma

For the equal variance models "EII", "EEI", and "EEE". A d by d matrix giving the common covariance for all components of the mixture model.

cholSigma

For the equal variance model "EEE". A d by d upper triangular matrix giving the Cholesky factor of the common covariance for all components of the mixture model.

sigma

For all multidimensional mixture models. A d by d by G matrix array whose [,,k]th entry is the covariance matrix for the kth component of the mixture model.

cholsigma

For the unconstrained covariance mixture model "VVV". A d by d by G matrix array whose [,,k]th entry is the upper triangular Cholesky factor of the covariance matrix for the kth component of the mixture model.

scale

For diagonal models "EEI", "EVI", "VEI", "VVI" and constant-shape models "EEV" and "VEV". Either a G-vector giving the scale of the covariance (the dth root of its determinant) for each component in the mixture model, or a single numeric value if the scale is the same for each component.

shape

For diagonal models "EEI", "EVI", "VEI", "VVI" and constant-shape models "EEV" and "VEV". Either a G by d matrix in which the kth column is the shape of the covariance matrix (normalized to have determinant 1) for the kth component, or a d-vector giving a common shape for all components.

orientation

For the constant-shape models "EEV" and "VEV". Either a d by d by G array whose [,,k]th entry is the orthonomal matrix whose columns are the eigenvectors of the covariance matrix of the kth component, or a d by d orthonormal matrix if the mixture components have a common orientation. The orientation component is not needed in spherical and diagonal models, since the principal components are parallel to the coordinate axes so that the orientation matrix is the identity.

In all cases, the value -1 is used as a placeholder for unknown nonzero entries.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.