This function conducts k-means on the overall mean, the row margins or column margins of a set of N matrices. These matrices are two-way slices of a three-dimensional array.
orc.lsbclust(data, margin = 3L, delta, nclust, sep.nclust = TRUE,
type = NULL, verbose = 1, ...)A three-way array representing the data.
An integer giving the single subscript of data over which the clustering
will be applied.
A four-element binary vector (logical or numeric) indicating which sum-to-zero constraints must be enforced.
An integer giving the desired number of clusters. In case type specifies
more than one method, nclust can be a vector containing the number of
clusters to be determined for each type of cluster, and in the correct order as determined by
type (after matching the arguments). If type is of length greater than one and
nclust is of length one, the behaviour is governed by sep.nclust.
Logical indicating how nclust should be used across different type's.
If sep.nclust is TRUE, nclust is recycled so that each type can
have a different number of clusters. If sep.nclust is FALSE, the same vector
nclust is used for all type's.
One of "overall", "rows" or "columns" (or a unique abbreviation of
one of these) indicating whether clustering should be done on row margins, column margins or
the overall means of the two-way slices respectively. If more than one opion are supplied, the
algorithm is run for all (unique) options supplied.
Integer controlling the amount of information printed: 0 = no information, 1 = Information on random starts and progress, and 2 = information is printed after each iteration for the interaction clustering.
Additional arguments passed to kmeans.
A list containing a subset of the classes row.kmeans, col.kmeans and
ovl.kmeans which are specific versions of class kmeans. In case type is a vector, a list
is returned containing the results for each of the (unique) elements of type, with the
same classes as before. See kmeans for an overview of the structure of these objects.