latentcluster {latentnetHRT} | R Documentation |
latentcluster()
is a term to the
function ergmm
to fit a latent position cluster 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 ~ latentcluster(<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
.
latentcluster(k=2, ngroups, z.prior.mu=0, z.prior.sd=10, b.delta=0.5, b.prior.mu=0, b.prior.sd=10, Sigprior = qchisq(0.05,3), muSigprior = 2, dirprior=3, alphaprior=3, chisqprop = 6, thetaprop=0.1, ...)
k |
Dimension of the latent space. |
ngroups |
Number of clusters in the latent space. |
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. |
Sigprior |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
muSigprior |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
dirprior |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
alphaprior |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
chisqprop |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
thetaprop |
Prior standard deviations for the node positions relative to the cluster mean. If a constant is passed it is used for each dimension. |
... |
Specific to the model term. |
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.
Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum. Model-Based Clustering for Social Networks. Working Paper Number 46, Center for Statistics and the Social Sciences, University of Washington, April 2005.
latent, plot.ergmm, sna, network, terms.ergmm
## Not run: # # 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) # # Using Sampson's Monk data, lets fit a latent clustering model # samp.fit <- ergmm(samplike ~ latentcluster(k=2, ngroups=3), burnin=10000, MCMCsamplesize=2000, interval=30) # # See if we have convergence in the MCMC mcmc.diagnostics(samp.fit) # # Lets look at the goodness of fit: # plot(samp.fit,label=samp.labs, vertex.col="group") plot(samp.fit,pie=TRUE,label=samp.labs) plot(samp.fit,density=c(2,2)) plot(samp.fit,contours=5,contour.color="red") plot(samp.fit,density=TRUE,drawarrows=TRUE) ergmm.add.contours(samp.fit,nlevels=8,lwd=2) points(samp.fit$Z.mkl,pch=19,col=samp.fit$class) ## End(Not run)