R: Computing the threshold

find.m {blockmodeling} | R Documentation

## Computing the threshold

### Description

The functions compute the maximum value of `m/cut` where a certain block is still classified as `alt.blocks` and not "null". The difference between `find.m` and `find.m2` it that `find.m` uses an optimization approach and is faster and more precise than `find.m2`. However, `find.m` only supports regular ("reg") and complete ("com") as `alt.blocks`, while `find.m2` supports all block types. Also, `find.m` does not always work, especially if `cormet` is not "none".

### Usage

    find.m(M, clu, alt.blocks = "reg", diag = !is.list(clu),
        cormet = "none", half = TRUE, FUN = "max")
    find.m2(M, clu, alt.blocks = "reg", neval = 100, half = TRUE,
        ms = NULL, ...)
    find.cut(M, clu, alt.blocks = "reg", cuts = "all", ...)

### Arguments

`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.
`alt.blocks` | Only one of allowed blocktypes, as alternative to the null block: "com" - complete block "rdo", "cdo" - row and column-dominant blocks (binary, valued, and implicit approach only) "reg" - (f-)regular block "rre", "cre" - row and column-(f-)regular blocks "rfn", "cfn" - row and column-dominant blocks (binary, valued, and implicit approach only) "den" - density block (binary approach only) "avg" - average block (valued approach only)
`diag` | (default = `TRUE`) Should the special status of diagonal be acknowledged.
`cormet` | Which method should be used to correct for different maximum error contributions "none" - no correction "censor" - censor values larger than `M` "correct" - so that the maximum possible error contribution of the cell is the same regardless of a condition (either that something must be 0 or at least `m`).
`FUN` | (default = "max") Function f used in row-f-regular, column-f-regular, and f-regular blocks.
`cuts` | The cuts, which should be evaluated. If `cuts="all"` (default), all unique values are evaluated.
`neval` | A number of different `m` values to be evaluated.
`half` | Should the returned value of m be one half of the value where the inconsistencies are the same.
`ms` | The values of m where the function should be evaluated.
`...` | Other parameters to `crit.fun`.

### Value

A matrix of maximal `m/cut` values.

### 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

Žiberna, A. (2014). Blockmodeling of multilevel networks. Social Networks, 39(1), 46-61. doi: 10.1016/j.socnet.2014.04.002

### See Also

`critFunC` and maybe also `optParC`, `plotMat`

[Package blockmodeling version 0.3.1 Index]