Convert mixture component covariances to matrix form

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)
#> [1] "modelName"  "prior"      "n"          "d"          "G"         
#> [6] "z"          "parameters" "control"    "loglik"    
meEst$parameters$variance
#> $modelName
#> [1] "VEV"
#> 
#> $d
#> [1] 4
#> 
#> $G
#> [1] 3
#> 
#> $sigma
#> , , 1
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   0.13321144  0.10939010   0.01919074  0.01158562
#> Sepal.Width    0.10939010  0.15495729   0.01209929  0.01001100
#> Petal.Length   0.01919074  0.01209929   0.02827503  0.00581811
#> Petal.Width    0.01158562  0.01001100   0.00581811  0.01069593
#> 
#> , , 2
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   0.22571809  0.07613379   0.14689790  0.04335743
#> Sepal.Width    0.07613379  0.08024536   0.07371576  0.03435377
#> Petal.Length   0.14689790  0.07371576   0.16611738  0.04951832
#> Petal.Width    0.04335743  0.03435377   0.04951832  0.03337836
#> 
#> , , 3
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   0.42945746  0.10782916   0.33457182  0.06541484
#> Sepal.Width    0.10782916  0.11595505   0.08903504  0.06133241
#> Petal.Length   0.33457182  0.08903504   0.36427088  0.08711121
#> Petal.Width    0.06541484  0.06133241   0.08711121  0.08666116
#> 
#> 
#> $scale
#> [1] 0.03748690 0.05792051 0.11417100
#> 
#> $shape
#> [1] 6.8586562 1.0115190 0.6265407 0.2300581
#> 
#> $orientation
#> , , 1
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length  -0.66907840   0.5978840   -0.4399628 -0.03607712
#> Sepal.Width   -0.73414783  -0.6206734    0.2746075 -0.01955027
#> Petal.Length  -0.09654390   0.4900556    0.8324495 -0.23990129
#> Petal.Width   -0.06356359   0.1309379    0.1950675  0.96992969
#> 
#> , , 2
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    0.7058553  -0.6603954   0.24389224 -0.07850316
#> Sepal.Width     0.3292982   0.5666772   0.68428027  0.31968755
#> Petal.Length    0.5956635   0.3576279  -0.68509739  0.21892660
#> Petal.Width     0.1962550   0.3389057   0.05402459 -0.91853594
#> 
#> , , 3
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   -0.7104631 -0.30095824   -0.5382163  -0.3391010
#> Sepal.Width    -0.2167218  0.71570645   -0.4318489   0.5042841
#> Petal.Length   -0.6483892 -0.07068137    0.6545951   0.3822313
#> Petal.Width    -0.1669287  0.62625279    0.3087637  -0.6961374
#> 
#> 

dec <- meEst$parameters$variance
decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale,
             orientation = dec$orientation)
#> , , 1
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.13321144 0.10939010 0.01919074 0.01158562
#> [2,] 0.10939010 0.15495729 0.01209929 0.01001100
#> [3,] 0.01919074 0.01209929 0.02827503 0.00581811
#> [4,] 0.01158562 0.01001100 0.00581811 0.01069593
#> 
#> , , 2
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.22571809 0.07613379 0.14689790 0.04335743
#> [2,] 0.07613379 0.08024536 0.07371576 0.03435377
#> [3,] 0.14689790 0.07371576 0.16611738 0.04951832
#> [4,] 0.04335743 0.03435377 0.04951832 0.03337836
#> 
#> , , 3
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.42945746 0.10782916 0.33457182 0.06541484
#> [2,] 0.10782916 0.11595505 0.08903504 0.06133241
#> [3,] 0.33457182 0.08903504 0.36427088 0.08711121
#> [4,] 0.06541484 0.06133241 0.08711121 0.08666116
#> 
#> attr(,"modelName")
#> [1] "VEV"
# \donttest{
do.call("decomp2sigma", dec)  ## alternative call
#> , , 1
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.13321144 0.10939010 0.01919074 0.01158562
#> [2,] 0.10939010 0.15495729 0.01209929 0.01001100
#> [3,] 0.01919074 0.01209929 0.02827503 0.00581811
#> [4,] 0.01158562 0.01001100 0.00581811 0.01069593
#> 
#> , , 2
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.22571809 0.07613379 0.14689790 0.04335743
#> [2,] 0.07613379 0.08024536 0.07371576 0.03435377
#> [3,] 0.14689790 0.07371576 0.16611738 0.04951832
#> [4,] 0.04335743 0.03435377 0.04951832 0.03337836
#> 
#> , , 3
#> 
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.42945746 0.10782916 0.33457182 0.06541484
#> [2,] 0.10782916 0.11595505 0.08903504 0.06133241
#> [3,] 0.33457182 0.08903504 0.36427088 0.08711121
#> [4,] 0.06541484 0.06133241 0.08711121 0.08666116
#> 
#> attr(,"modelName")
#> [1] "VEV"
# }

Convert mixture component covariances to matrix form

Usage

Arguments

Value

See also

Examples