R: Function that performs k-means like one-mode blockmodeling....

kmBlockC {kmBlock}

R Documentation

Function that performs k-means like one-mode blockmodeling. If `clu` is a list, the method for linked/multilevel networks is applied

Function 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
)

`M`	A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation.
`clu`	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.
`weights`	The weights for each cell in the matrix/array. A matrix or an array with the same dimensions as `M`.
`diagonal`	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
`limitType`	What do the limits represent, on which "side" of this limits should the values lie. Possible values: "none","inside","outside"
`limits`	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.

A list similar to optParC in package blockmodeling.