clu {blockmodeling} R Documentation

## Function for extraction of some elements for objects, returend by functions for Generalized blockmodeling

### Description

Functions for extraction of partition (clu), all best partitions (partitions), image or blockmodel (IM)) and total error or inconsistency (err) for objects, returned by functions critFunC or optRandomParC.

### Usage

clu(res, which = 1, ...)

partitions(res)

err(res, ...)

IM(res, which = 1, drop = TRUE, ...)

EM(res, which = 1, drop = TRUE, ...)


### Arguments

 res Result of function critFunC or optRandomParC. which From which (if there are more than one) "best" solution should the element be extracted. Warning! which grater than the number of "best" partitions produces an error. ... Not used. drop If TRUE (default), dimensions that have only one level are dropped (drop function is applied to the final result).

### Value

The desired element.

Aleš Žiberna

### References

Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press.

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

critFunC, plot.mat, optRandomParC

### 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)

# We select a random partition and then optimize 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
res <- optParC(M = net,
clu = all.par[[sample(1:length(all.par), size = 1)]],
approaches = "hom", homFun = "ss", blocks = "com")
plot(res) # Hopefully we get the original partition
clu(res) # Hopefully we get the original partition
err(res) # Error
IM(res) # Image matrix/array.
EM(res) # Error matrix/array.



[Package blockmodeling version 1.1.4 Index]