| latent {latentnetHRT} | R Documentation |
latent is a term to the
function ergmm
which is used to fit a latent position model to a
given network, g.
ergmm returns a Bayesian model fit based on a
Monte Carlo scheme.
The default prior specifications are diffuse. An approximate MLE fit is
also returned.
The ergmm specifies models via: y ~ latent(<options>)
where y is a network object.
For the list of possible <options>, see below.
For the list of other model terms, see
the manual pages for terms.ergmm.
latent(k=2, z.delta=0.1, z.prior.mu=0,
z.prior.sd=10, b.delta=0.5, b.prior.mu=0,
b.prior.sd=10)
k |
Dimension of the latent space. |
z.delta |
Standard deviation of deviance in the proposal for the latent positions. If a constant is passed it is used for each dimension. |
z.prior.mu |
Prior mean for each dimension of the latent positions. If a constant is passed it is used for each dimension. |
z.prior.sd |
Prior standard deviation for each dimension of the latent positions. If a constant is passed it is used for each dimension. |
b.delta |
Standard deviation of the deviance for covariate parameters. If a constant is passed it is used for each dimension. |
b.prior.mu |
Prior mean for the covariate parameters. If a constant is passed it is used for each dimension. |
b.prior.sd |
Prior standard deviation for the covariate parameters. If a constant is passed it is used for each dimension. |
ergmm returns an object of class ergmm that
is a list.
Peter D. Hoff, Adrian E. Raftery and Mark S. Handcock. Latent space approaches to social network analysis. Journal of the American Statistical Association, Dec 2002, Vol.97, Iss. 460; pg. 1090-1098.
latentcluster, plot.ergmm, sna, network, terms.ergmm
#
# Using Sampson's Monk data, lets fit a
# simple latent position model
#
data(sampson)
#
# Get the group labels
samp.labs <- substr(get.vertex.attribute(samplike,"group"),1,1)
#
samp.fit <- ergmm(samplike ~ latent(k=2), burnin=10000,
MCMCsamplesize=2000, interval=30)
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
#
# Plot the fit
#
plot(samp.fit,label=samp.labs, vertex.col="group")