R: Convert mixture component covariances to matrix form

decomp2sigma {mclust} | R Documentation

## 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.

### See Also

`sigma2decomp`

### 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

[Package mclust version 5.4.8 Index]