A.C. Thomas, November 22
Here we test the fitting and plotting options for SR, the asymmetric sender-receiver component.
Component alone.
library(CIDnetworks) n.nodes <- 100 test.sr <- SR(n.nodes=100, generate=TRUE) test.sr.pieces <- test.sr$pieces() test.sr.value <- test.sr$value() test.sr$random.start() test.sr$draw() test.sr.gibbs <- test.sr$gibbs.full(draws=500, report=100, burnin=200, thin=2, make.random.start=TRUE) test.sr$gibbs.summary(test.sr.gibbs) test.sr$gibbs.plot(test.sr.gibbs) test.sr.gibbs.unlist <- list.output.to.matrices (test.sr.gibbs) test.sr.gibbs.value <- test.sr$gibbs.value(test.sr.gibbs) plot(test.sr.gibbs.unlist$log.lik, main="Log-likelihood") plot(test.sr.gibbs.unlist$coef.sr[1,], main=paste("Coefficient 1", test.sr.pieces$coef.sr[1])); abline(h=test.sr.pieces$coef.sr[1], lwd=3, col=2) test.sr$plot.network() test.sr$gibbs.plot(test.sr.gibbs) test.sr$netplot(test.sr$edge.list, , colvalues=20, main="Oracle values") test.sr$plot.network(test.sr.value) test.sr$plot.network(apply(test.sr.gibbs.value, 1, mean))
Binary within CID:
test.sr.b <- CID.generate (n.nodes=100, components=list(COV(covariates=matrix(rbinom(covs*n.nodes*(n.nodes-1)/2, 1, 0.5), ncol=3), coef.sr=1:covs)), intercept=-3, residual.variance=2) test.sr.b.pieces <- test.sr.b$pieces() test.sr.b.value <- test.sr.b$value() test.sr.b.gibbs <- test.sr.b$gibbs.full(draws=1000, report=100, burnin=1000, make.random.start=TRUE) test.sr.b.gibbs.value <- test.sr.b$gibbs.value(test.sr.b.gibbs) test.sr.b$gibbs.plot (test.sr.b.gibbs) color.range <- range(c(test.sr.b.value, apply(test.sr.b.gibbs.value, 1, mean))) test.sr.b$plot.network(test.sr.b.value) test.sr.b$plot.network(apply(test.sr.b.gibbs.value, 1, mean)) plot(jitter(test.sr.b.value), jitter(apply(test.sr.b.gibbs.value, 1, mean)))
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