msos (version 1.2.0)

pcbic.stepwise: Choosing a good pattern

Description

Uses the stepwise procedure described in Section 13.1.4 to find a pattern for a set of observed eigenvalues with good BIC value.

Usage

pcbic.stepwise(eigenvals, n)

Arguments

eigenvals

The \(Q\)-vector of eigenvalues of the covariance matrix, in order from largest to smallest.

n

The degrees of freedom in the covariance matrix.

Value

A list with the following components:

Patterns

A list of patterns, one for each value of length \(K\).

BICs

A vector of the BIC's for the above patterns.

BestBIC

The best (smallest) value among the BIC's in BICs.

BestPattern

The pattern with the best BIC.

lambdaHat

A \(Q\)-vector containing the MLE's for the eigenvalues for the pattern with the best BIC.

See Also

pcbic, pcbic.unite, and pcbic.subpatterns.

Examples

# NOT RUN {
# Build cars1
require("mclust")
mcars <- Mclust(cars)
cars1 <- cars[mcars$classification == 1, ]
xcars <- scale(cars1)
eg <- eigen(var(xcars))
pcbic.stepwise(eg$values, 95)
# }