In CIDnetworks: Generative Models for Complex Networks with Conditionally Independent Dyadic Structure

CIDnetworks test: Generative examples with reciprocity alone

library(CIDnetworks)

cid.one <- CID (n.nodes=100, int.correlation=0.2, generate=TRUE)  #
cid.one$plot.network()

cid.one$draw()
cid.one$draw.int.correlation()
cid.one$pieces()
cid.one.gibbs <- cid.one$gibbs.full(draws=50, thin=1, burnin=0)
cid.one$gibbs.plot(cid.one.gibbs)

g1 <- cid.one$test.int.correlation(-199:199/200);
plot(exp(g1 - max(g1)))

p1 <- cid.one$pieces()
p1$int.correlation <- -0.9
cid.one$value.ext(p1)

Tests:

table (cid.one$outcome[1:nrow(cid.one$edge.list)],
      cid.one$outcome[cid.one$reciprocal.match[1:nrow(cid.one$edge.list)]])
plot (cid.one$int.outcome[1:nrow(cid.one$edge.list)],
      cid.one$int.outcome[cid.one$reciprocal.match[1:nrow(cid.one$edge.list)]])
cor (cid.one$int.outcome[1:nrow(cid.one$edge.list)],
      cid.one$int.outcome[cid.one$reciprocal.match[1:nrow(cid.one$edge.list)]])

cid.one$int.correlation.prior(-19:19/20)

library(mnormt)

CIDnetworks documentation built on May 30, 2017, 4:10 a.m.