blockmodeling (version 0.1.9)

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, returend by functions opt.par, opt.random.par, opt.these.par, and check.these.par

Usage

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

Arguments

res

Resoult of function opt.par, opt.random.par, opt.these.par, or check.these.par

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.

References

<U+017D>IBERNA, Ale<U+0161> (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.

<U+017D>IBERNA, Ale<U+0161>. 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, Anu<U+0161>ka (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

crit.fun, check.these.par, opt.random.par, opt.these.par, plot.opt.par

Examples

# NOT RUN {
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<-opt.par(M=net,
   clu=all.par[[sample(1:length(all.par),size=1)]],
   approach="ss",blocks="com")
plot(res) #Hopefully we get the original partition
clu(res) #Hopefully we get the original partition
err(res) #Error
IM(res) #NULL, because FORTRAN subrutine is used.
# }