R: Function for extraction of some elements for objects,...

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.

### Author(s)

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

### See Also

`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 1.0.5 Index]