# decomp2sigma: Convert mixture component covariances to matrix form. In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

## Description

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

## Usage

 `1` ```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.

`sigma2decomp`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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) ## Not run: do.call("decomp2sigma", dec) ## alternative call ## End(Not run) ```