latentnet-package {latentnet} | R Documentation |
latentnet: Latent Position and Cluster Models for Statistical Networks
Description
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) doi:10.18637/jss.v024.i05 and Krivitsky, Handcock, Raftery, and Hoff (2009) doi:10.1016/j.socnet.2009.04.001.
Details
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.
Author(s)
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]
References
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). 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.
See Also
Useful links:
Report bugs at https://github.com/statnet/latentnet/issues