clu: Function for extraction of some elements for objects,...

Description Usage Arguments Value Author(s) References See Also Examples

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

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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.

Author(s)

Aleš Žiberna

References

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

See Also

critFunC, plot.mat, optRandomParC

Examples

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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.

blockmodeling documentation built on Nov. 22, 2018, 3 a.m.