blockmodeling (version 0.1.9)

fun.by.blocks: 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, ...)
# S3 method for default
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 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

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.

See Also

opt.random.par, opt.these.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

#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"
# }