blockmodelingRoxygen2 {blockmodelingRoxygen2} | R Documentation |
This package is primarily meant as an implementation of Generalized blockmodeling. In addition, functions for computation of (dis)similarities in terms of structural and regular equivalence, plotting and other "utility" functions are provided.
Aleš Žiberna
Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press.
Žiberna, A. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002
Žiberna, A. (2008). Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. Journal of Mathematical Sociology, 32(1), 57-84. doi: 10.1080/00222500701790207
Žiberna, A. (2014). Blockmodeling of multilevel networks. Social Networks, 39(1), 46-61. doi: 10.1016/j.socnet.2014.04.002
optRandomParC
, critFunC
, optParC
, IM
, clu
, err
, plotMat
#Generating a simple network corresponding to the simple Sum of squares # Structural equivalence with blockmodel: # nul com # nul nul n <- 20 net <- matrix(NA, ncol = n, nrow = n) clu <- rep(1:2, times = c(5, 15)) tclu <- table(clu) net[clu == 1, clu == 1] <- rnorm(n = tclu[1] * tclu[1], mean = 0, sd = 1) net[clu == 1, clu == 2] <- rnorm(n = tclu[1] * tclu[2], mean = 4, sd = 1) net[clu == 2, clu == 1] <- rnorm(n = tclu[2] * tclu[1], mean = 0, sd = 1) net[clu == 2, clu == 2] <- rnorm(n = tclu[2] * tclu[2], mean = 0, sd = 1) # Computation of criterion function with the correct partition res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = "com") res$err # The error is relatively small plot(res) # Computation of criterion function with the correct partition and correct pre-specified blockmodel # Prespecified blockmodel used # nul com # nul nul B <- array(NA, dim = c(1, 1, 2, 2)) B[1, 1, , ] <- "nul" B[1, 1, 1, 2] <- "com" B[1, 1, , ] res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = B) err(res) # The error is relatively small IM(res) plot(res) # Computation of criterion function with the correct partition # and pre-specified blockmodel with some alternatives # Prespecified blockmodel used # nul nul|com # nul nul B <- array(NA, dim = c(2, 2, 2)) B[1, , ] <- "nul" B[2, 1, 2] <- "com" res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = B) err(res) # The error is relatively small IM(res) plot(res) # Optimizing a very bad partition cluStart <- rep(1:2, times = 10) res <- optParC(M = net, clu = cluStart, approaches = "hom", homFun = "ss", blocks = "com") clu(res) # Hopefully we get the original partition) err(res) plot(res) # Optimizing 10 random chosen partitions with optRandomParC res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "com") clu(res) # Hopefully we get the original partition) err(res) plot(res) # Adapt network for Valued blockmodeling with the same model net[net > 4] <- 4 net[net < 0] <- 0 # Computation of criterion function with the correct partition res <- critFunC(M = net, clu = clu, approaches = "val", blocks = c("nul", "com"), preSpecM = 4) err(res) # The error is relatively small IM(res) # The image corresponds to the one used for generation of # The network plot(res)