ergmm | 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 = control.ergmm(), 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 formula object, of the form Note that, as in |
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 specifying the conditional distribution of each edge value. See families.ergmm for the currently implemented families. |
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). doi: 10.18637/jss.v024.i05
network, set.vertex.attributes, set.network.attributes, summary.ergmm, terms.ergmm, families.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.fit2 <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver) # # See if we have convergence in the MCMC mcmc.diagnostics(samp.fit2) # # Plot the fit. # plot(samp.fit2, pie=TRUE)
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