ergmm {latentnet} | R Documentation |
ergmm
is used to fit latent space and latent space cluster random network models,
as described by Hoff, Raftery and Handcock (2002),
Handcock, Raftery and Tantrum (2005), and Krivitsky, Handcock,
Raftery, and Hoff (2009).
ergmm
can return either a Bayesian model fit
or the two-stage MLE.
ergmm(formula, response=NULL, family="Bernoulli",fam.par=NULL, control=ergmm.control(), user.start=list(), prior=ergmm.prior(), tofit=c("mcmc", "mkl", "mkl.mbc", "procrustes", "klswitch"), Z.ref=NULL, Z.K.ref=NULL, seed=NULL, verbose=FALSE)
formula |
An R formula object, of the form
|
response |
An optional edge attribute that serves as the response
variable. By default, presence (1) or absence (0) of an edge in
|
family |
A character vector that is one of "Bernoulli" (the default), "binomial", or "Poisson", specifying the conditional distribution of each edge value. |
fam.par |
For those families that require additional parameters, a list. |
control |
The MCMC parameters that do not affect the posterior
distribution such as the sample size, the proposal variances, and
tuning parameters, in the
form of a named list. See |
user.start |
An optional initial configuration parameters for MCMC in the form of a list. By default, posterior mode conditioned on cluster assignments is used. It is permitted to only supply some of the parameters of a configuration. If this is done, the remaining paramters are fitted conditional on those supplied. |
prior |
The prior parameters for the model being fitted in the
form of a named list. See terms.ergmm for the names to use.
If given, will override those given in the
formula terms, making it useful as a convenient way to store and reproduce a
prior distribution. The list or prior parameters can also be
extracted from an ERGMM fit object. See
|
tofit |
A character vector listing some subset of "pmode",
"mcmc", "mkl", "mkl.mbc", "mle","procrustes", and "klswitch",
defaulting to all of the above, instructing |
Z.ref |
If given, used as a reference for Procrustes analysis. |
Z.K.ref |
If given, used as a reference for label-switching. |
seed |
If supplied, random number seed. |
verbose |
If this is |
ergmm
returns an object of class ergmm
containing the information about the posterior.
Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum (2002). Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society: Series A, 170(2), 301-354.
Peter D. Hoff, Adrian E. Raftery and Mark S. Handcock (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090-1098.
Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social Networks, 31(3), 204-213.
Pavel N. Krivitsky and Mark S. Handcock (2008).
Fitting Position Latent Cluster Models for Social Networks with
latentnet
. Journal of Statistical Software, 24(5).
network, set.vertex.attributes, set.network.attributes,
summary.ergmm
, terms.ergmm
# # Use 'data(package = "latentnet")' to list the data sets in a # data(package="latentnet") # # Using Sampson's Monk data, lets fit a # simple latent position model # data(sampson) samp.fit <- ergmm(samplike ~ euclidean(d=2)) # # See if we have convergence in the MCMC mcmc.diagnostics(samp.fit) # # Plot the fit # plot(samp.fit) # # Using Sampson's Monk data, lets fit a latent clustering random effects model # samp.fit <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver) # # See if we have convergence in the MCMC mcmc.diagnostics(samp.fit) # # Plot the fit. # plot(samp.fit)