glcSplit: Gaussian Latent Class Splitter
In RPMM: Recursively Partitioned Mixture Model

Description Usage Arguments Details Value

Splits a data set into two via a Gaussian mixture models

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glcSplit(x, initFunctions, weight = NULL, index = NULL, level =
                 0, wthresh = 1e-09, verbose = TRUE, nthresh = 5,
                 splitCriterion = glcSplitCriterionBIC)

`x`	Data matrix (n x j) on which to perform clustering
`initFunctions`	List of functions of type “glcInitialize...” for initializing latent class model. See `glcInitializeFanny` for an example of arguments and return values.
`weight`	Weight corresponding to the indices passed (see `index`). Defaults to 1 for all indices
`index`	Row indices of data matrix to include. Defaults to all (1 to n).
`level`	Current level.
`wthresh`	Weight threshold for filtering data to children. Indices having weight less than this value will not be passed to children nodes.
`verbose`	Level of verbosity. Default=2 (too much). 0 for quiet.
`nthresh`	Total weight in node required for node to be a candidate for splitting. Nodes with weight less than this value will never split.
`splitCriterion`	Function of type “glcSplitCriterion...” for determining whether split should occur. See `glcSplitCriterionBIC` for an example of arguments and return values.