weightsMlLoglik function

Computes weights for parts of the multilevel network based on random errors using the SS approach with complete blocks only (compatible with k-means)

weightsMlLoglik(
  mlNet,
  cluParts,
  k,
  mWeights = 1000,
  sumFun = sd,
  nCores = 0,
  weightClusterSize = 0,
  paramGenPar = list(genPajekPar = FALSE),
  ...
)

Arguments

• mlNet: A multilevel/linked network - The code assumes only one relation --> a matrix.
• cluParts: A partition spliting the units into different sets
• k: A vecotor of number of clusters for each set of units in the network.
• mWeights: The number of repetitions for computing random errors. Defaults to 1000
• sumFun: The function to compute the summary of errors, which is then used to compute the weights by computing 1/summary. Defaults to sd.
• nCores: The number of to use for parallel computing. 0 means all available - 1, 1 means only once core - no parallel computing.
• weightClusterSize: The weight given to cluster sizes. Defalults to 0, as only this is weighted my the tie-based weights.
• paramGenPar: The parameter addParam from genRandomPar (see documentation there). Default here is paramGenPar=list(genPajekPar = FALSE), which is different from the default in genRandomPar. The same value is used for generating partitions for all partitions.
• ...: Paramters passed to llStochBlock

Returns

Weights and "intermediate results": - errArr: A 3d array of errors (mWeights for each part of the network)

• errMatSum: errArr summed over all repetitions.

• weightsMat: A matrix of weights, one for each part. An inverse of errMatSum with NaNs replaced by zeros.

References

Škulj, D., & Žiberna, A. (2022). Stochastic blockmodeling of linked networks. Social Networks, 70, 240-252.

See Also

llStochBlock; ICLStochBlock

Author(s)