Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) tools:::Rd_expr_doi("10.18637/jss.v024.i05") and Krivitsky, Handcock, Raftery, and Hoff (2009) tools:::Rd_expr_doi("10.1016/j.socnet.2009.04.001").
Maintainer: Pavel N. Krivitsky pavel@statnet.org (ORCID)
Authors:
Mark S. Handcock handcock@stat.ucla.edu
Other contributors:
Susan M. Shortreed [contributor]
Jeremy Tantrum [contributor]
Peter D. Hoff [contributor]
Li Wang lxwang@gmail.com [contributor]
Kirk Li kirkli@uw.edu [contributor]
Jake Fisher jcf26@duke.edu [contributor]
Jordan T. Bates jtbates@gmail.com [contributor]
The package latentnet is used to fit latent cluster random effect
models, where the probability of a network \(g\), on a set of nodes is a
product of dyad probabilities, each of which is a GLM with linear component
\(\eta_{i,j}=\sum_{k=1}^p \beta_k
X_{i,j,k}+d(Z_i,Z_j)+\delta_i+\gamma_j\), where \(X\) is an array of dyad
covariates, \(\beta\) is a vector of covariate coefficients, \(Z_i\) is
the latent space position of node \(i\), \(d(\cdot,\cdot)\) is a
function of the two positions: either negative Euclidean
(\(-||Z_i-Z_j||\)) or bilinear (\(Z_i\cdot Z_j\)), and \(\delta\) and
\(\gamma\) are vectors of sender and receiver effects. (Note that these
are different from the eigenmodel of Hoff (2007) ``Modeling homophily and
stochastic equivalence in symmetric relational data'', fit by package
eigenmodel.)
The ergmm specifies models via: g ~ <model terms> where
g is a network object For the list of possible <model
terms>, see ergmTerm. For the list of the possible dyad
distribution families, see families.ergmm.
The arguments in the ergmm function specific to latent
variable models are ergmm.control. See the help page for
ergmm for the details.
The result of a latent variable model fit is an ergmm object.
Hence the summary, print, and plot functions
apply to the fits. The plot.ergmm function has many options
specific to latent variable models.
Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum (2007). Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.
Peter D. Hoff (2005). Bilinear Mixed Effects Models for Dyadic Data. Journal of the American Statistical Association, 100(469), 286-295.
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). tools:::Rd_expr_doi("10.18637/jss.v024.i05")
Susan M. Shortreed, Mark S. Handcock, and Peter D. Hoff (2006). Positional Estimation within the Latent Space Model for Networks. Methodology, 2(1), 24-33.
Useful links:
Report bugs at https://github.com/statnet/latentnet/issues