Converts a set of covariance matrices from representation as a 3-D array to a parameterization by eigenvalue decomposition.
sigma2decomp(sigma, G = NULL, tol = sqrt(.Machine$double.eps), …)Either a 3-D array whose [,,k]th component is the covariance matrix for the kth component in an MVN mixture model, or a single covariance matrix in the case that all components have the same covariance.
The number of components in the mixture. When
sigma is a 3-D array, the number of components
can be inferred from its dimensions.
Tolerance for determining whether or not the covariances have equal volume,
shape, and or orientation. The default is the square root of the relative
machine precision, sqrt(.Machine$double.eps), which is about
1.e-8.
Catches unused arguments from an indirect or list call via do.call.
The covariance matrices for the mixture components in decomposition form, including the following components:
A character string indicating the infered model. The help file for
mclustModelNames describes the available models.
The dimension of the data.
The number of components in the mixture model.
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.
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.
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 of
decomp can be omitted in spherical and diagonal models, for
which the principal components are parallel to the coordinate axes
so that the orientation matrix is the identity.
# NOT RUN {
meEst <- meEEE(iris[,-5], unmap(iris[,5]))
names(meEst$parameters$variance)
meEst$parameters$variance$Sigma
sigma2decomp(meEst$parameters$variance$Sigma, G = length(unique(iris[,5])))
# }
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