sedist {blockmodeling} | R Documentation |

## Computes distances in terms of Structural equivalence (Lorrain & White, 1971)

### Description

The functions compute the distances in terms of Structural equivalence (Lorrain and White, 1971) between the units of a one-mode network. Several options for treating the diagonal values are supported.

### Usage

```
sedist(
M,
method = "default",
fun = "default",
fun.on.rows = "default",
handle.interaction = "switch",
use = "pairwise.complete.obs",
...
)
```

### Arguments

`M` |
A matrix representing the (usually valued) network. For now, only one-relational networks are supported. The network must be one-mode. |

`method` |
The method used to compute distances - any of the methods allowed by functions dist, |

`fun` |
Which function should be used to compute distances (given as a character). |

`fun.on.rows` |
For non-standard function - does the function compute measure on rows (such as |

`handle.interaction` |
How should the interaction between the vertices analysed be handled: |

`use` |
For use with methods |

`...` |
Additional arguments to |

### Details

If both `method`

and `fun`

are `"default"`

, the Euclidean distances are computed.
The `"default"`

method for `fun = "dist"`

is "euclidean" and for `fun = "cor"`

"pearson".

### Value

A matrix (usually of class dist) is returned.

### Author(s)

Aleš Žiberna

### References

Batagelj, V., Ferligoj, A., & Doreian, P. (1992). Direct and indirect methods for structural equivalence. Social Networks, 14(1-2), 63-90. doi: 10.1016/0378-8733(92)90014-X

Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. Journal of Mathematical Sociology, 1(1), 49-80. doi: 10.1080/0022250X.1971.9989788

### See Also

`dist`

, `hclust`

, `REGE`

, `optParC`

, `optParC`

, `optRandomParC`

### Examples

```
# Generating a simple network corresponding to the simple Sum of squares
# Structural equivalence with blockmodel:
# null com
# null null
n <- 20
net <- matrix(NA, ncol = n, nrow = n)
clu <- rep(1:2, times = c(5, 15))
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)
D <- sedist(M = net)
plot.mat(net, clu = cutree(hclust(d = D, method = "ward"), k = 2))
```

*blockmodeling*version 1.1.5 Index]