mcmcdiagnostics_cergm: MCMC Diagnostics for Citation Exponential Random Graph Models
In schmid86/cERGM: A package to fit citation Exponential Random Graph Models

Description Usage Arguments Author(s) References Examples

MCMC Diagnostics as performed by mcmc.diagnostics

1	mcmc.diagnostics.cERGM(object, ...)

object

A cERGM object

Christian Schmid <songhyo86@gmail.com>

Hunter D, Handcock M, Butts C, Goodreau S, Morris M (2008). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3), 1-29.

Schmid C, Chen T, Desmarais B (2020). Generative Dynamics of Supreme Court Citations: Analysis with a New Statistical Network Model.

# load Supreme Court Citation Network from 1936-1941
data("scc_1936_1941")

# get vertex attribute "Term". Indicates the Term of each node
terms <- get.vertex.attribute(scc_1936_1941, "Term")

# create a matrix with the sender's term in each row
terms.matrix <- matrix(terms,length(terms),length(terms),byrow=F)

# fix all dyads that can potentially be created in 1941 as 1
unfixed.dyads <- 1*(terms.matrix == 1941)


# alternatively one can also add an unfixed vector that indicates
# which nodes can create edges
unfixed.vector<-  1*(terms==1941)

# test cergm-function
model <- cergm(scc_1936_1941~ edges+ difftransties("Term")+ nodeicov("NumberJusticesPro"),
            not.fixed=unfixed.dyads, estimate="MPLE", init.method="SAN")

mcmc.diagnostics.cERGM(model)