funByBlocks: Computation of function values by blocks

In blockmodelingRoxygen2: Generalized and Classical Blockmodeling of Valued Networks

Description

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

Usage

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

## S3 method for class 'optMorePar'
funByBlocks(x, which = 1, ...)

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

funByBlocks(x, ...)

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

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 (different kinds of units with no ties among themselves. 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 (different kinds of units with no ties among themselves. If the network is not two-mode, the matrix must be square.
clu	A partition. Each unique value represents one cluster. If the network is one-mode, then this should be a vector, else a list of vectors, one for each mode.
ignore.diag	Should the diagonal be ignored.
sortNames	Should the rows and columns of the matrix be sorted based on their names.
FUN	The function to be computed over the blocks.
...	Further arguments to funByBlocks.default.
which	Which (if several) of the "best" solutions should be used.

Value

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

Author(s)

Aleš Žiberna

References

Žiberna, A. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002

Žiberna, A. (2008). Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. Journal of Mathematical Sociology, 32(1), 57-84. doi: 10.1080/00222500701790207

See Also

optRandomParC, optParC

Examples

n <- 8 # If larger, the number of partitions increases dramatically,
# 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
funByBlocks(res)
# Computing mean by blocks, ignoring the diagonal (default)

blockmodelingRoxygen2 documentation built on June 9, 2020, 3:30 p.m.