glcSplit

Data matrix (n x j) on which to perform clustering

List of functions of type &#147;glcInitialize...&#148; for initializing latent class model.
See <code>glcInitializeFanny</code> for an example of arguments and return values.

initFunctions

Weight corresponding to the indices passed (see <code>index</code>). Defaults to 1 for all indices

weight

Row indices of data matrix to include. Defaults to all (1 to n).

index

level

Weight threshold for filtering data to children. 
  Indices having weight less than this value will not be passed to children nodes.

wthresh

Level of verbosity.  Default=2 (too much).  0 for quiet.

verbose

Total weight in node required for node to be a candidate for splitting.  
Nodes with weight less than this value will never split.

nthresh

Function of type &#147;glcSplitCriterion...&#148; for determining whether split should occur.
See <code>glcSplitCriterionBIC</code> for an example of arguments and return values.

splitCriterion


Splits a data set into two via a Gaussian mixture models


cluster

tree


Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.
This is a model-based clustering algorithm that returns a hierarchy
of classes, similar to hierarchical clustering, but also similar to
finite mixture models.

glcSplit: Gaussian Latent Class Splitter

Description

Usage

Arguments

Value

Details