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

In blockmodelingOld: Generalized and Classical Blockmodeling of Valued Networks

Description

Function for extraction of clu (partition), all best clus (partitions), IM (image or blockmodel) and err (total error or inconsistency) for objects, returend by functions critFunCor optRandomParC

Usage

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

Arguments

res	Result of function critFunC or optRandomParC
which	From which (if there are more than one) "best" solution whould the element be extracted. Warning! which grater than the number of "best" partitions produces an error.
...	Not used

Value

The desired element.

Author(s)

Aleš Žiberna

References

ŽIBERNA, Aleš (2006): Generalized Blockmodeling of Valued Networks. Social Networks, Jan. 2007, vol. 29, no. 1, 105-126. http://dx.doi.org/10.1016/j.socnet.2006.04.002.

ŽIBERNA, Aleš. Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. J. math. sociol., 2008, vol. 32, no. 1, 57-84. http://www.informaworld.com/smpp/content?content=10.1080/00222500701790207.

DOREIAN, Patrick, BATAGELJ, Vladimir, FERLIGOJ, Anuška (2005): Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press, 2005. XV, 384 p., ISBN 0-521-84085-6.

See Also

critFunC, plot.mat, optRandomParC

Examples

n <- 8 # if larger, the number of partitions increases dramaticaly,
     # 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 parition and then optimise 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.

blockmodelingOld documentation built on May 2, 2019, 5:11 p.m.