ergm package is capable of fitting a wide range of
exponential random network models, in which
the probability of a given network, $y$, on a set of nodes is
$\exp(\theta{\cdot}g(y))/c(\theta)$, where
$g(y)$ is a vector of network statistics,
$\theta$ is a parameter vector of the same length and $c(\theta)$ is the
normalizing constant for the distribution.
The ergm function fits these models when they are expressed
via an Rformula object, of the form
y ~ ,
where y is a network object or a matrix that can be
coerced to a network object.
To create a
network object in R, use the network() function,
then add nodal attributes to it using the %v%
operator if necessary. The ergm package contains a wide range of terms.
For the details on the possible
ergm-terms.
This package can be modified by users to add user-defined terms to ergm
models.
The terms can be used throughout the ergm package
and behave identically to the supplied terms.
ergmC program that generates
the sample of graph statistics using MCMC. This sample is then returned
to R, which then approximates the MLE.ergmuserterms package is available from the statnet
website (The code contains some simple examples and templates. These include:
m2starkstar(2). This
option adds one statistic to the model, equal to the
number of mixed-2-stars in the network, defined as a
pair of edges ${(i{\rightarrow}j), (j{\rightarrow}k)}.$
} testmeedges is isomorphic to kstar(1); for
directed networks, edges is isomorphic to both ostar(1)
and istar(1). }
library(ergmuserterms)
data(sampson)
monk.fit <- ergm(samplike~m2star)
summary(monk.fit)
monk.fit <- ergm(samplike ~ m2star + testme)
summary(monk.fit)Run the code above in your browser using DataLab