critFunKmeans function

Function that computes criterion function used in k-means like one-mode blockmodeling. If clu is a list, the method for linked/multilevel networks is applied

critFunKmeans(
  M,
  clu,
  weights = NULL,
  diagonal = c("ignore", "seperate", "same"),
  limitType = c("none", "inside", "outside"),
  limits = NULL
)

Arguments

• 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.

Returns

A list similar to optParC in package blockmodeling.