mclust: decomp2sigma – R documentation

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decomp2sigma

Convert mixture component covariances to matrix form

Description

Converts covariances from a parameterization by eigenvalue decomposition or cholesky factorization to representation as a 3-D array.

Usage

decomp2sigma(d, G, scale, shape, orientation, ...)

Arguments

`d`	The dimension of the data.
`G`	The number of components in the mixture model.
`scale`	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`	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`	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.
`...`	Catches unused arguments from an indirect or list call via `do.call`.

Value

A 3-D array whose [,,k]th component is the covariance matrix of the kth component in an MVN mixture model.

Examples

meEst <- meVEV(iris[,-5], unmap(iris[,5])) 
names(meEst)
meEst$parameters$variance

dec <- meEst$parameters$variance
decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale,
             orientation = dec$orientation)

do.call("decomp2sigma", dec)  ## alternative call

mclust

Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

v5.4.10

GPL (>= 2)

Authors

Chris Fraley [aut], Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>), Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)

Initial release

2022-05-20