decomp2sigma {mclust} | R Documentation |
Converts covariances from a parameterization by eigenvalue decomposition or cholesky factorization to representation as a 3-D array.
decomp2sigma(d, G, scale, shape, orientation, ...)
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.
|
... |
Catches unused arguments from an indirect or list call via do.call .
|
A 3-D array whose [,,k]
th component is the
covariance matrix of the kth component in an MVN mixture model.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
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)