fun.by.blocks: Computation of function values by blocks

Description

Computes a value of a function 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 = "default", sortNames = TRUE, 
FUN = "mean", ...) 
# S3 method for opt.more.par
fun.by.blocks(x, which = 1, ...)

Arguments

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.

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.

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

<U+017D>iberna, 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

Examples

Run this code

# NOT RUN {
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
fun.by.blocks(res)
# Computing mean by blocks, ignoring the diagonal (default)
# }