`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.

- 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
```