blockmodeling (version 0.3.6)

clu: Function for extraction of some elements for objects, returend by functions for Generalized blockmodeling

Description

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.

Usage

clu(res, which = 1, ...)
IM(res, which = 1, drop=TRUE, ...)
EM(res, which = 1, drop=TRUE, ...)
err(res, ...)
partitions(res)

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.

drop

If TRUE (default), dimensions that have only one level are dropped (drop function is applied to the final result).

…

Not used.

Value

The desired element.

References

Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press.

<U+017D>iberna, A. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002

<U+017D>iberna, 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

See Also

critFunC, plot.mat, optRandomParC

Examples

# NOT RUN {
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.
# }