fun.by.blocks {blockmodeling}R Documentation

Computation of function values by blocks

Description

Computes a value of a functions over blocks of a matrix, defined by a partition.

Usage

fun.by.blocks(x, ...)

## Default S3 method:
fun.by.blocks(x = M, M = x, clu,
  ignore.diag = identical(ss(diag(M)), 0) && !is.list(clu),
  FUN = "mean", sortNames = TRUE, ...)

## S3 method for class 'opt.more.par':
fun.by.blocks(x, which = 1, ...)

Arguments

x An object of suitable class or a matrix representing the (usually valued) network. For now, only one-relational networks are supported. The network can have one or more modes (diferent kinds of units with no ties among themselvs. If the network is not two-mode, the matrix must be square.
M A matrix representing the (usually valued) network. For now, only one-relational networks are supported. The network can have one or more modes (diferent kinds of units with no ties among themselvs. If the network is not two-mode, the matrix must be square.
clu A partition. Each unique value represents one cluster. If the nework is one-mode, than this should be a vector, else a list of vectors, one for each mode
ignore.diag Should the diagonal be ingored.
sortNames Should the rows and columns of the matrix be sorted based on their names?
FUN Function to be computed over the blocks
which Which (if several) of the "best" solutions should be used
... Further arguments to fun.by.blocks.default

Value

A numerical matrix of FUN values by blocks, induced by a partition clu

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.

See Also

opt.random.par, opt.these.par

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

#optimizing 10 random partitions with opt.these.par
res<-opt.these.par(M=net,
   partitions=all.par[sample(1:length(all.par),size=10)],
   approach="ss", blocks="com")
plot(res) #Hopefully we get the original partition
fun.by.blocks(res)
#computing mean by blocks, ignoring the diagonal (default)
res$best[[1]]$BM
#the same result computed by opt.these.par when
#approach="ss" and blocks="com"

[Package blockmodeling version 0.1.7 Index]