choose.cluster.BIC

variable

Orthogonal basis for each of the subspaces.

pcas

number.clusters

A boolean - if set to TRUE all warnings are displayed, default value is FALSE.

show.warnings

Selects a subspace closest to a given variable. To select the subspace, the method 
considers (for every subspace) a subset of its principal components and tries 
to fit a linear model with the variable as the response. Then the method chooses 
the subspace for which the value of BIC was the highest.

internal

Performs clustering of quantitative variables,
assuming that clusters lie in low-dimensional subspaces. Segmentation of
variables, number of clusters and their dimensions are selected based on
BIC. Candidate models are identified based on many runs of K-means
algorithm with different random initializations of cluster centers.

choose.cluster.BIC: Choses a subspace for a variable

Description

Usage

Arguments

Value