mclustVariance {mclust} | R Documentation |
Specification of variance parameters for the various types of Gaussian mixture models.
The variance
component in the parameters list from the
output to e.g. me
ormstep
or input to e.g.
estep
may contain one or more of the following
arguments, depending on the model:
A character string indicating the model.
The dimension of the data.
The number of components in the mixture model.
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").
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.
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.
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.
For the unconstrained covaraince 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.
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.
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.
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.
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 and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.