# Function for extraction of some elements for objects, returend by functions for Generalized blockmodeling Functions for extraction of partition (`clu`), all best partitions (`partitions`), image or blockmodel (`IM`)) and total error or inconsistency (`err`) for objects, returned by functions `critFunC` or `optRandomParC`. UTF-8 ```r clu(res, which = 1, ...) partitions(res) err(res, ...) IM(res, which = 1, drop = TRUE, ...) EM(res, which = 1, drop = TRUE, ...) ``` ## Arguments - `res`: Result of function `critFunC` or `optRandomParC`. - `which`: From `which` (if there are more than one) "best" solution should the element be extracted. Warning! `which` grater than the number of "best" partitions produces an error. - ``...``: Not used. - `drop`: If `TRUE` (default), dimensions that have only one level are dropped (`drop` function is applied to the final result). ## Returns The desired element. ## Examples ```r 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. ``` ## References Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002 (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 ## See Also `critFunC`, `plot.mat`, `optRandomParC` ## Author(s)

clu function