# pcbic: BIC for a particular pattern In msos: Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden

## Description

Find the BIC and MLE from a set of observed eigenvalues for a specific pattern.

## Usage

 `1` ```pcbic(eigenvals, n, pattern) ```

## 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. `pattern` The pattern of equalities of the eigenvalues, given by the K-vector (Q_1, ... , Q_K) as in (13.8).

## Value

A 'list' with the following components:

lambdaHat

A Q-vector containing the MLE's for the eigenvalues.

Deviance

The deviance of the model, as in (13.13).

Dimension

The dimension of the model, as in (13.12).

BIC

The value of the BIC for the model, as in (13.14).

`pcbic.stepwise`, `pcbic.unite`, and `pcbic.subpatterns`.
 ```1 2 3 4 5 6 7``` ```# 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)) ```