Find the BIC and MLE from a set of observed eigenvalues for a specific pattern.
pcbic(eigenvals, n, pattern)The \(Q\)-vector of eigenvalues of the covariance matrix, in order from largest to smallest.
The degrees of freedom in the covariance matrix.
The pattern of equalities of the eigenvalues, given by the \(K\)-vector (\(Q_1\), ... , \(Q_K\)) as in (13.8).
A `list` with the following components:
A \(Q\)-vector containing the MLE's for the eigenvalues.
The deviance of the model, as in (13.13).
The dimension of the model, as in (13.12).
The value of the BIC for the model, as in (13.14).
# Build cars1
require("mclust")
mcars <- Mclust(cars)
cars1 <- cars[mcars$classification == 1, ]
xcars <- scale(cars1)
eg <- eigen(var(xcars))
pcbic(eg$values, 95, c(1, 1, 3, 3, 2, 1))
#> $lambdaHat
#> [1] 6.20990339 1.83332778 0.71561848 0.71561848 0.71561848 0.21276067
#> [7] 0.21276067 0.21276067 0.07077652 0.07077652 0.03007833
#>
#> $Deviance
#> [1] -1141.398
#>
#> $Dimension
#> [1] 54
#>
#> $BIC
#> [1] -895.4885
#>