fun.by.blocks: Computation of function values by blocks
In blockmodelingOld: Generalized and Classical Blockmodeling of Valued Networks

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

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

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

## Default S3 method:
fun.by.blocks(x = M, M = x, clu, 
ignore.diag = "default", sortNames = TRUE, 
FUN = "mean", ...) 

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

`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`

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

Aleš Žiberna

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.

optRandomParC, optParC

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)


#optimizing 10 random partitions with optRandomParC
res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "com")
plot(res) #Hopefully we get the original partition
fun.by.blocks(res)
#computing mean by blocks, ignoring the diagonal (default)