ghype {ghypernet} | R Documentation |
Fitting gHypEG models
Description
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.
Usage
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, ...)
Arguments
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 |
Value
ghype return an object of class "ghype".
Methods (by class)
-
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.
Examples
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)