clu is a list, the method for linked/multilevel networks is appliedFunction that performs k-means like one-mode blockmodeling. If clu is a list, the method for linked/multilevel networks is applied
kmBlockC(
M,
clu,
weights = NULL,
diagonal = c("ignore", "seperate", "same"),
limitType = c("none", "inside", "outside"),
limits = NULL
)A list similar to optParC in package blockmodeling.
A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
A partition. Each unique value represents one cluster. If the network is one-mode, than this should be a vector, else a list of vectors, one for each mode. Similarly, if units are comprised of several sets, clu should be the list containing one vector for each set.
The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as M.
How should the diagonal values be treated. Possible values are:
ignore - diagonal values are ignored
seperate - diagonal values are treated separately
same - diagonal values are treated the same as all other values
What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
If diagonal is "ignore" or "same", an array with dimensions equal to:
number of clusters (of all types)
number of clusters (of all types)
number of relations
2 - the first is lower limit and the second is upper limit
If diagonal is "seperate", a list of two array. The first should be as described above, representing limits for off diagonal values. The second should be similar with only 3 dimensions, as one of the first two must be omitted.
kmBlockORPC