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1. User Guides ¶
All the materials here is names of apckages for R (a statistical software, click here for more information)
All articles here are from articles in Journal of Statistical Software, 24 (2008), also available from : Statnet user guide pages
Introduction to ergm (eponential family random graphic models) and an overview of statnet
network A package for managing relational data in R
ergm A package to fit, simulate and diagnose exponential family random models for networks
Using ergm: Specification of exponential family random graph models and computational tips
latentnet a package for fitting latent cluster models for networks
sna A package for social network analysis
dynamicnetwork and rSoNIA: Prototype packages for managing and animating longitudinal network data
networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling
A tutorial on statnet
network A package for managing relational data in R
ergm A package to fit, simulate and diagnose exponential family random models for networks
Using ergm: Specification of exponential family random graph models and computational tips
latentnet a package for fitting latent cluster models for networks
sna A package for social network analysis
dynamicnetwork and rSoNIA: Prototype packages for managing and animating longitudinal network data
networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling
A tutorial on statnet
v24i09.R: R example code from the paper
install.packages("statnet") library("statnet") data("faux.magnolia.high") fmh <- faux.magnolia.high fmh plot(fmh, displayisolates = FALSE) table(component.dist(fmh)$csize) summary(fmh) plot(fmh, displayisolates = FALSE, vertex.col = "Grade") fmh.degreedist <- table(degree(fmh, cmode = "indegree")) fmh.degreedist summary(fmh ~ degree(0:8)) help(package = "sna") summary(fmh ~ degree(0:8, "Sex")) summary(fmh ~ triangle) summary(fmh ~ edges + triangle) mixingmatrix(fmh, "Grade") gr <- fmh %v% "Grade" table(gr) save.image() model1 <- ergm(fmh ~ edges) summary(model1) names(model1) model1$coef model1$mle.lik model2 <- ergm(fmh ~ edges + nodematch("Grade") + nodematch("Race") + nodematch("Sex")) summary(model2) model2$mle.lik sim2 <- simulate(model2, burnin = 1e+6, verbose = TRUE, seed = 9) mixingmatrix(sim2, "Race") mixingmatrix(fmh, "Race") plot(summary(fmh ~ degree(0:10)), type = "l", lty = 1, lwd = 2, xlab = "Degree", ylab = "Count") lines(summary(sim2 ~ degree(0:10)), lty = 2, lwd = 3) legend("topright", legend = c("Observed", "Simulated"), lwd = 3, lty = 1:2) c(fmh = summary(fmh ~ triangle), sim2 = summary(sim2 ~ triangle)) model3 <- ergm(fmh ~ edges + triangle, seed = 99) pdf("model3diagnostics.pdf") mcmc.diagnostics(model3) dev.off() model3 <- ergm(fmh ~ edges + triangle, verbose = TRUE, seed = 99) model3.take2 <- ergm(fmh ~ edges + triangle, MCMCsamplesize = 1e+5, interval = 1000, verbose = TRUE, seed = 88) model3.take3 <- ergm(fmh ~ edges + triangle, maxit = 25, seed = 888, control = control.ergm(steplength = 0.25), verbose = TRUE) model4.take1 <- ergm(fmh ~ edges + nodematch("Grade") + nodematch("Race") + nodematch("Sex") + gwesp(0, fixed = TRUE), MCMCsamplesize = 1e+5, maxit = 15, verbose = TRUE, control = control.ergm(steplength = 0.25), seed = 123) model4.take1$coef model4.take2 <- ergm(fmh ~ edges + nodematch("Grade") + nodematch("Race") + nodematch("Sex") + gwesp(0.1, fixed = TRUE), MCMCsamplesize = 1e+5, maxit = 15, verbose = TRUE, control = control.ergm(steplength = 0.25), seed = 123) model4.take3 <- ergm(fmh ~ edges + nodematch("Grade") + nodematch("Race") + nodematch("Sex") + gwesp(0.2, fixed = TRUE), MCMCsamplesize = 1e+5, maxit = 15, verbose = TRUE, control = control.ergm(steplength = 0.25), seed = 123) c(model4.take1$mle.lik, model4.take2$mle.lik, model4.take3$mle.lik) model4 <- model4.take3 model4$coef sim4 <- simulate(model4, burnin = 1e+5, interval = 1e+5, nsim = 100, verbose = TRUE, seed = 321) class(sim4) names(sim4) class(sim4$networks[[1]]) model4.tridist <- sapply(sim4$networks, function(x) summary(x ~ triangle)) hist(model4.tridist, xlab = "Triangles") fmh.tri <- summary(fmh ~ triangle) arrows(fmh.tri, 20, fmh.tri, 5, col = "red", lwd = 3) sum(fmh.tri <= model4.tridist) gof4.deg <- gof(model4 ~ degree, verbose = TRUE, burnin = 1e+5, interval = 1e+5, seed = 246) plot(gof4.deg) gof4.deg gof4.esp.dist <- gof(model4 ~ espartners + distance, verbose = TRUE, burnin = 1e+5, interval = 1e+5, seed = 642) get(getOption("device"))(width = 8, height = 4) par(mfrow = c(1, 2)) plot(gof4.esp.dist, plotlogodds = TRUE)
2. attached documents ¶
v24i01.pdf (526.87 KB)
v24i02.pdf (514.73 KB)
v24i03.pdf (710.15 KB)
v24i04.pdf (379.46 KB)
v24i05.pdf (1.6 MB)
v24i06.pdf (2.76 MB)
v24i07.pdf (1.53 MB)
v24i08.pdf (428.51 KB)
v24i09.pdf (1021.77 KB)
Categoryv24i02.pdf (514.73 KB)
v24i03.pdf (710.15 KB)
v24i04.pdf (379.46 KB)
v24i05.pdf (1.6 MB)
v24i06.pdf (2.76 MB)
v24i07.pdf (1.53 MB)
v24i08.pdf (428.51 KB)
v24i09.pdf (1021.77 KB)