decomp2sigma {mclust} | R Documentation

## Convert mixture component covariances to matrix form.

### Description

Converts a set of covariances from a parameterization by eigenvalue decomposition 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 of 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.
`...` | Catch unused arguments from a `do.call` call.

### Value

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

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation, and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`sigma2decomp`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]

    meEst <- meVEV(irisMatrix, unmap(irisClass))
    names(meEst)
    meEst$decomp
    meEst$sigma

    dec <- meEst$decomp
    decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale,
                 orientation = dec$orientation)
    ## Not run:
    do.call("decomp2sigma", meEst$decomp)  ## alternative call
    ## End(Not run)

[Package mclust version 2.1-8 Index]