clu {blockmodeling} | R Documentation |
Function for extraction of clu (partition), all best clus (partitions), IM (image or blockmodel) and err (total error or inconsistency) for objects, returned by functions critFunC
or optRandomParC
.
clu(res, which = 1, ...) IM(res, which = 1, drop=TRUE, ...) EM(res, which = 1, drop=TRUE, ...) err(res, ...) partitions(res)
res |
Result of function |
which |
From |
drop |
If |
... |
Not used. |
The desired element.
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
critFunC
, plot.mat
, optRandomParC
n <- 8 # If larger, the number of partitions increases dramatically, # as does if we increase the number of clusters net <- matrix(NA, ncol = n, nrow = n) clu <- rep(1:2, times = c(3, 5)) 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) # We select a random partition and then optimize it all.par <- nkpartitions(n = n, k = length(tclu)) # Forming the partitions all.par <- lapply(apply(all.par, 1, list),function(x) x[[1]]) # to make a list out of the matrix res <- optParC(M = net, clu = all.par[[sample(1:length(all.par), size = 1)]], approaches = "hom", homFun = "ss", blocks = "com") plot(res) # Hopefully we get the original partition clu(res) # Hopefully we get the original partition err(res) # Error IM(res) # Image matrix/array. EM(res) # Error matrix/array.