R: Read Highland Tribes

tribes {latentnet} | R Documentation

## Read Highland Tribes

### Description

A network of political alliances and enmities among the 16 Gahuku-Gama sub-tribes of Eastern Central Highlands of New Guinea, documented by Read (1954).

### Format

An undirected `network` object with no loops, having the following attributes:

`%v% "vertex.names"`

  Character attribute with names of tribes.

`%e% "pos"`

  Logical attribute indicating an alliance relationship.

`%e% "neg"`

  Logical attribute indicating a hostile relationship ("rova").

`%e% "sign"`

  Numeric attribute coding -1 for enmity, 0 for no relationship, and 1 for alliance.

`%e% "sign.012"`

  Numeric attribute coding 0 for enmity, 1 for no relationship, and 2 for alliance.

Because of limitations of `network` objects, the object itself is a complete graph, and is thus meaningless if used directly or plotted.

### Details

This network shows 3 clusters.

### Source

http://vlado.fmf.uni-lj.si/pub/networks/data/UciNet/UciData.htm#gama, with corrections from Read (1954).

### References

Taken from UCINET IV, which cites the following: Hage P. and Harary F. (1983). Structural models in anthropology. Cambridge: Cambridge University Press. (See p 56-60). Read K. (1954). Cultures of the central highlands, New Guinea. Southwestern Journal of Anthropology, 10, 1-43.

### Examples

    data(tribes)
    # Only model positive ties:
    tribes.fit<-ergmm(tribes~euclidean(d=2,G=3),response="pos")
    # Edge color must be set manually, for green ties to represent alliance
    # and for red ties to represent enmity.
    plot(tribes.fit,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)
    # Model both positive and negative ties:
    tribes.fit3<-ergmm(tribes~euclidean(d=2,G=3),response="sign.012",
                       family="binomial.logit",fam.par=list(trials=2))
    # Edge color must be set manually, for green ties to represent alliance
    # and for red ties to represent enmity.
    plot(tribes.fit3,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)

[Package latentnet version 2.10.5 Index]