cluster.pca.BIC: mBIC for subspace clustering
In varclust: Variables Clustering

Description Usage Arguments Value

Computes the value of modified Bayesian Information Criterion (mBIC) for given data set partition and clusters' dimensionalities. In each cluster we assume that variables are spanned by few factors. Considering maximum likelihood we get that those factors are in fact principal components. Additionally, it uses by default an informative prior distribution on models.

1 2	cluster.pca.BIC(X, segmentation, dims, numb.clusters, max.dim, flat.prior = FALSE)

`X`	A matrix with only quantitative variables.
`segmentation`	A vector, segmentation for which likelihood is computed. Clusters numbers should be from range [1, numb.clusters].
`dims`	A vector of integers, dimensions of subspaces. Number of principal components (fixed or chosen by PESEL criterion) that span each subspace.
`numb.clusters`	An integer, number of clusters.
`max.dim`	An integer, upper bound for allowed dimension of a subspace.
`flat.prior`	A boolean, if TRUE (default is FALSE) then flat prior on models is used.