`ghype.Rd`

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 matrix ghype( graph, directed, selfloops, xi = NULL, omega = NULL, unbiased = FALSE, regular = FALSE, ... ) # S3 method for default ghype( graph, directed, selfloops, xi = NULL, omega = NULL, unbiased = FALSE, regular = FALSE, ... ) # S3 method for igraph ghype( graph, directed, selfloops, xi = NULL, omega = NULL, unbiased = FALSE, regular = FALSE, ... ) # S3 method for ghype print(x, suppressCall = FALSE, ...)

graph | either an adjacency matrix or an igraph graph. |
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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".

`matrix`

: Fitting ghype models from an adjacency matrix`default`

: Generating a ghype model from given xi and omega`igraph`

: Fitting ghype models from an igraph graph`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)#> Call: #> ghype.matrix(graph = graph, directed = directed, selfloops = selfloops, #> unbiased = TRUE, regular = FALSE) #> ghype undirected , no selfloops #> 34 vertices, 231 edges #> Loglikelihood: #> -434.5449 #> df: 34