funByBlocks.default {blockmodeling} | R Documentation |

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

```
## Default S3 method:
funByBlocks(
x = M,
clu,
M = x,
ignore.diag = "default",
sortNames = TRUE,
FUN = "mean",
...
)
## S3 method for class 'optMorePar'
funByBlocks(x, which = 1, orderClu = FALSE, sortNames = NULL, ...)
## S3 method for class 'opt.more.par'
funByBlocks(x, which = 1, orderClu = FALSE, sortNames = NULL, ...)
funByBlocks(x, ...)
fun.by.blocks(x, ...)
```

`x` |
An object of suitable class or a matrix/array representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation. 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. |

`M` |
A matrix representing the (usually valued) network. For multi-relational networks, this should be an array with the third dimension representing the relation. 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. |

`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 |

`which` |
Which (if several) of the "best" solutions should be used. |

`orderClu` |
Should the partition be ordered before computing. |

A numerical matrix of `FUN`

values by blocks, induced by a partition `clu`

.

Aleš Žiberna

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

```
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)
```

[Package *blockmodeling* version 1.1.5 Index]