| ghype | R Documentation | 
ghype is used to fit gHypEG models when the propensity matrix is known. It can be used to estimate a null model (soft configuration model), or the benchmark 'full-model', where the propensity matrix is fitted such that the expected graph from the fitted model is the one passed to the function.
ghype(
  graph,
  directed,
  selfloops,
  xi = NULL,
  omega = NULL,
  unbiased = FALSE,
  regular = FALSE,
  ...
)
## S3 method for class 'matrix'
ghype(
  graph,
  directed,
  selfloops,
  xi = NULL,
  omega = NULL,
  unbiased = FALSE,
  regular = FALSE,
  ...
)
## Default S3 method:
ghype(
  graph,
  directed,
  selfloops,
  xi = NULL,
  omega = NULL,
  unbiased = FALSE,
  regular = FALSE,
  ...
)
## S3 method for class 'igraph'
ghype(
  graph,
  directed,
  selfloops,
  xi = NULL,
  omega = NULL,
  unbiased = FALSE,
  regular = FALSE,
  ...
)
## S3 method for class 'ghype'
print(x, suppressCall = FALSE, ...)
graph | 
 either an adjacency matrix or an igraph graph.  | 
directed | 
 a boolean argument specifying whether graph is directed or not.  | 
selfloops | 
 a boolean argument specifying whether the model should incorporate selfloops.  | 
xi | 
 an optional matrix defining the combinatorial matrix of the model.  | 
omega | 
 an optional matrix defining the propensity matrix of the model.  | 
unbiased | 
 a boolean argument specifying whether to model the hypergeometric ensemble (no propensity), defaults to FALSE.  | 
regular | 
 a boolean argument specifying whether to model the 'gnp' ensemble (no xi), defaults to FALSE.  | 
... | 
 further arguments passed to or from other methods.  | 
x | 
 ghype model  | 
suppressCall | 
 boolean, suppress print of the call  | 
ghype return an object of class "ghype".
ghype(matrix): Fitting ghype models from an adjacency matrix
ghype(default): Generating a ghype model from given xi and omega
ghype(igraph): Fitting ghype models from an igraph graph
print(ghype): Print method for ghype object.
data("adj_karate")
fullmodel <- ghype(graph = adj_karate, directed = FALSE, selfloops = FALSE, unbiased = FALSE)
data('adj_karate')
model <- scm(adj_karate, FALSE, FALSE)
print(model)
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